2023/04/13 19:56:16 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.7.0 (default, Oct 9 2018, 10:31:47) [GCC 7.3.0] CUDA available: True numpy_random_seed: 214624949 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/cache/share/cuda-11.1 NVCC: Cuda compilation tools, release 11.1, V11.1.74 GCC: gcc (GCC) 5.4.0 PyTorch: 1.9.0+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.1 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu111 OpenCV: 4.6.0 MMEngine: 0.7.2 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None diff_rank_seed: False deterministic: False Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2023/04/13 19:56:16 - mmengine - INFO - Config: preprocess_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]) model = dict( type='Recognizer2D', backbone=dict( type='MobileNetV2TSM', shift_div=8, num_segments=8, is_shift=True, pretrained='mmcls://mobilenet_v2'), cls_head=dict( type='TSMHead', num_segments=8, num_classes=400, in_channels=1280, spatial_type='avg', consensus=dict(type='AvgConsensus', dim=1), dropout_ratio=0.5, init_std=0.001, is_shift=True, average_clips='prob'), data_preprocessor=dict( type='ActionDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]), train_cfg=None, test_cfg=None) default_scope = 'mmaction' default_hooks = dict( runtime_info=dict(type='RuntimeInfoHook'), timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=20, ignore_last=False), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict( type='CheckpointHook', interval=3, save_best='auto', max_keep_ckpts=3), sampler_seed=dict(type='DistSamplerSeedHook'), sync_buffers=dict(type='SyncBuffersHook')) env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) log_processor = dict(type='LogProcessor', window_size=20, by_epoch=True) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='ActionVisualizer', vis_backends=[dict(type='LocalVisBackend')]) log_level = 'INFO' load_from = None resume = False dataset_type = 'VideoDataset' data_root = 'data/kinetics400/videos_train' data_root_val = 'data/kinetics400/videos_val' ann_file_train = 'data/kinetics400/kinetics400_train_list_videos.txt' ann_file_val = 'data/kinetics400/kinetics400_val_list_videos.txt' file_client_args = dict( io_backend='petrel', path_mapping=dict({ 'data/kinetics400': 's254:s3://openmmlab/datasets/action/Kinetics400' })) train_pipeline = [ dict( type='DecordInit', io_backend='petrel', path_mapping=dict({ 'data/kinetics400': 's254:s3://openmmlab/datasets/action/Kinetics400' })), dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict( type='MultiScaleCrop', input_size=224, scales=(1, 0.875, 0.75, 0.66), random_crop=False, max_wh_scale_gap=1, num_fixed_crops=13), dict(type='Resize', scale=(224, 224), keep_ratio=False), dict(type='Flip', flip_ratio=0.5), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ] val_pipeline = [ dict( type='DecordInit', io_backend='petrel', path_mapping=dict({ 'data/kinetics400': 's254:s3://openmmlab/datasets/action/Kinetics400' })), dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ] test_pipeline = [ dict( type='DecordInit', io_backend='petrel', path_mapping=dict({ 'data/kinetics400': 's254:s3://openmmlab/datasets/action/Kinetics400' })), dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='ThreeCrop', crop_size=256), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ] train_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='VideoDataset', ann_file='data/kinetics400/kinetics400_train_list_videos.txt', data_prefix=dict(video='data/kinetics400/videos_train'), pipeline=[ dict( type='DecordInit', io_backend='petrel', path_mapping=dict({ 'data/kinetics400': 's254:s3://openmmlab/datasets/action/Kinetics400' })), dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict( type='MultiScaleCrop', input_size=224, scales=(1, 0.875, 0.75, 0.66), random_crop=False, max_wh_scale_gap=1, num_fixed_crops=13), dict(type='Resize', scale=(224, 224), keep_ratio=False), dict(type='Flip', flip_ratio=0.5), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ])) val_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='VideoDataset', ann_file='data/kinetics400/kinetics400_val_list_videos.txt', data_prefix=dict(video='data/kinetics400/videos_val'), pipeline=[ dict( type='DecordInit', io_backend='petrel', path_mapping=dict({ 'data/kinetics400': 's254:s3://openmmlab/datasets/action/Kinetics400' })), dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='CenterCrop', crop_size=224), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ], test_mode=True)) test_dataloader = dict( batch_size=1, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='VideoDataset', ann_file='data/kinetics400/kinetics400_val_list_videos.txt', data_prefix=dict(video='data/kinetics400/videos_val'), pipeline=[ dict( type='DecordInit', io_backend='petrel', path_mapping=dict({ 'data/kinetics400': 's254:s3://openmmlab/datasets/action/Kinetics400' })), dict( type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8, test_mode=True), dict(type='DecordDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='ThreeCrop', crop_size=256), dict(type='FormatShape', input_format='NCHW'), dict(type='PackActionInputs') ], test_mode=True)) val_evaluator = dict(type='AccMetric') test_evaluator = dict(type='AccMetric') train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=100, val_begin=1, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict( type='MultiStepLR', begin=0, end=100, by_epoch=True, milestones=[40, 80], gamma=0.1) ] optim_wrapper = dict( constructor='TSMOptimWrapperConstructor', paramwise_cfg=dict(fc_lr5=True), optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=2e-05), clip_grad=dict(max_norm=20, norm_type=2)) auto_scale_lr = dict(enable=True, base_batch_size=128) launcher = 'pytorch' work_dir = 'work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb' randomness = dict(seed=None, diff_rank_seed=False, deterministic=False) 2023/04/13 19:56:19 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (BELOW_NORMAL) LoggerHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (NORMAL ) SyncBuffersHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val_epoch: (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train: (VERY_LOW ) CheckpointHook -------------------- before_test_epoch: (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2023/04/13 19:56:20 - mmengine - INFO - LR is set based on batch size of 128 and the current batch size is 128. Scaling the original LR by 1.0. 2023/04/13 19:56:21 - mmengine - INFO - Name of parameter - Initialization information backbone.conv1.conv.weight - torch.Size([32, 3, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.conv1.bn.weight - torch.Size([32]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.conv1.bn.bias - torch.Size([32]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer1.0.conv.0.conv.weight - torch.Size([32, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer1.0.conv.0.bn.weight - torch.Size([32]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer1.0.conv.0.bn.bias - torch.Size([32]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer1.0.conv.1.conv.weight - torch.Size([16, 32, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer1.0.conv.1.bn.weight - torch.Size([16]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer1.0.conv.1.bn.bias - torch.Size([16]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.0.conv.0.conv.weight - torch.Size([96, 16, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.0.conv.0.bn.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.0.conv.0.bn.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.0.conv.1.conv.weight - torch.Size([96, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.0.conv.1.bn.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.0.conv.1.bn.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.0.conv.2.conv.weight - torch.Size([24, 96, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.0.conv.2.bn.weight - torch.Size([24]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.0.conv.2.bn.bias - torch.Size([24]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.1.conv.0.net.conv.weight - torch.Size([144, 24, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.1.conv.0.net.bn.weight - torch.Size([144]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.1.conv.0.net.bn.bias - torch.Size([144]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.1.conv.1.conv.weight - torch.Size([144, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.1.conv.1.bn.weight - torch.Size([144]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.1.conv.1.bn.bias - torch.Size([144]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.1.conv.2.conv.weight - torch.Size([24, 144, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.1.conv.2.bn.weight - torch.Size([24]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer2.1.conv.2.bn.bias - torch.Size([24]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.0.conv.0.conv.weight - torch.Size([144, 24, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.0.conv.0.bn.weight - torch.Size([144]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.0.conv.0.bn.bias - torch.Size([144]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.0.conv.1.conv.weight - torch.Size([144, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.0.conv.1.bn.weight - torch.Size([144]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.0.conv.1.bn.bias - torch.Size([144]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.0.conv.2.conv.weight - torch.Size([32, 144, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.0.conv.2.bn.weight - torch.Size([32]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.0.conv.2.bn.bias - torch.Size([32]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.1.conv.0.net.conv.weight - torch.Size([192, 32, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.1.conv.0.net.bn.weight - torch.Size([192]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.1.conv.0.net.bn.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.1.conv.1.conv.weight - torch.Size([192, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.1.conv.1.bn.weight - torch.Size([192]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.1.conv.1.bn.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.1.conv.2.conv.weight - torch.Size([32, 192, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.1.conv.2.bn.weight - torch.Size([32]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.1.conv.2.bn.bias - torch.Size([32]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.2.conv.0.net.conv.weight - torch.Size([192, 32, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.2.conv.0.net.bn.weight - torch.Size([192]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.2.conv.0.net.bn.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.2.conv.1.conv.weight - torch.Size([192, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.2.conv.1.bn.weight - torch.Size([192]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.2.conv.1.bn.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.2.conv.2.conv.weight - torch.Size([32, 192, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.2.conv.2.bn.weight - torch.Size([32]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer3.2.conv.2.bn.bias - torch.Size([32]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.0.conv.0.conv.weight - torch.Size([192, 32, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.0.conv.0.bn.weight - torch.Size([192]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.0.conv.0.bn.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.0.conv.1.conv.weight - torch.Size([192, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.0.conv.1.bn.weight - torch.Size([192]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.0.conv.1.bn.bias - torch.Size([192]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.0.conv.2.conv.weight - torch.Size([64, 192, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.0.conv.2.bn.weight - torch.Size([64]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.0.conv.2.bn.bias - torch.Size([64]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.1.conv.0.net.conv.weight - torch.Size([384, 64, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.1.conv.0.net.bn.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.1.conv.0.net.bn.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.1.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.1.conv.1.bn.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.1.conv.1.bn.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.1.conv.2.conv.weight - torch.Size([64, 384, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.1.conv.2.bn.weight - torch.Size([64]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.1.conv.2.bn.bias - torch.Size([64]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.2.conv.0.net.conv.weight - torch.Size([384, 64, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.2.conv.0.net.bn.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.2.conv.0.net.bn.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.2.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.2.conv.1.bn.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.2.conv.1.bn.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.2.conv.2.conv.weight - torch.Size([64, 384, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.2.conv.2.bn.weight - torch.Size([64]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.2.conv.2.bn.bias - torch.Size([64]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.3.conv.0.net.conv.weight - torch.Size([384, 64, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.3.conv.0.net.bn.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.3.conv.0.net.bn.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.3.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.3.conv.1.bn.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.3.conv.1.bn.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.3.conv.2.conv.weight - torch.Size([64, 384, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.3.conv.2.bn.weight - torch.Size([64]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer4.3.conv.2.bn.bias - torch.Size([64]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.0.conv.0.conv.weight - torch.Size([384, 64, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.0.conv.0.bn.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.0.conv.0.bn.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.0.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.0.conv.1.bn.weight - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.0.conv.1.bn.bias - torch.Size([384]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.0.conv.2.conv.weight - torch.Size([96, 384, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.0.conv.2.bn.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.0.conv.2.bn.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.1.conv.0.net.conv.weight - torch.Size([576, 96, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.1.conv.0.net.bn.weight - torch.Size([576]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.1.conv.0.net.bn.bias - torch.Size([576]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.1.conv.1.conv.weight - torch.Size([576, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.1.conv.1.bn.weight - torch.Size([576]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.1.conv.1.bn.bias - torch.Size([576]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.1.conv.2.conv.weight - torch.Size([96, 576, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.1.conv.2.bn.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.1.conv.2.bn.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.2.conv.0.net.conv.weight - torch.Size([576, 96, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.2.conv.0.net.bn.weight - torch.Size([576]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.2.conv.0.net.bn.bias - torch.Size([576]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.2.conv.1.conv.weight - torch.Size([576, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.2.conv.1.bn.weight - torch.Size([576]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.2.conv.1.bn.bias - torch.Size([576]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.2.conv.2.conv.weight - torch.Size([96, 576, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.2.conv.2.bn.weight - torch.Size([96]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer5.2.conv.2.bn.bias - torch.Size([96]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.0.conv.0.conv.weight - torch.Size([576, 96, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.0.conv.0.bn.weight - torch.Size([576]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.0.conv.0.bn.bias - torch.Size([576]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.0.conv.1.conv.weight - torch.Size([576, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.0.conv.1.bn.weight - torch.Size([576]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.0.conv.1.bn.bias - torch.Size([576]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.0.conv.2.conv.weight - torch.Size([160, 576, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.0.conv.2.bn.weight - torch.Size([160]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.0.conv.2.bn.bias - torch.Size([160]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.1.conv.0.net.conv.weight - torch.Size([960, 160, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.1.conv.0.net.bn.weight - torch.Size([960]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.1.conv.0.net.bn.bias - torch.Size([960]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.1.conv.1.conv.weight - torch.Size([960, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.1.conv.1.bn.weight - torch.Size([960]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.1.conv.1.bn.bias - torch.Size([960]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.1.conv.2.conv.weight - torch.Size([160, 960, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.1.conv.2.bn.weight - torch.Size([160]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.1.conv.2.bn.bias - torch.Size([160]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.2.conv.0.net.conv.weight - torch.Size([960, 160, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.2.conv.0.net.bn.weight - torch.Size([960]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.2.conv.0.net.bn.bias - torch.Size([960]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.2.conv.1.conv.weight - torch.Size([960, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.2.conv.1.bn.weight - torch.Size([960]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.2.conv.1.bn.bias - torch.Size([960]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.2.conv.2.conv.weight - torch.Size([160, 960, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.2.conv.2.bn.weight - torch.Size([160]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer6.2.conv.2.bn.bias - torch.Size([160]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer7.0.conv.0.conv.weight - torch.Size([960, 160, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer7.0.conv.0.bn.weight - torch.Size([960]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer7.0.conv.0.bn.bias - torch.Size([960]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer7.0.conv.1.conv.weight - torch.Size([960, 1, 3, 3]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer7.0.conv.1.bn.weight - torch.Size([960]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer7.0.conv.1.bn.bias - torch.Size([960]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer7.0.conv.2.conv.weight - torch.Size([320, 960, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer7.0.conv.2.bn.weight - torch.Size([320]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.layer7.0.conv.2.bn.bias - torch.Size([320]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.conv2.conv.weight - torch.Size([1280, 320, 1, 1]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.conv2.bn.weight - torch.Size([1280]): Initialized by user-defined `init_weights` in MobileNetV2TSM backbone.conv2.bn.bias - torch.Size([1280]): Initialized by user-defined `init_weights` in MobileNetV2TSM cls_head.fc_cls.weight - torch.Size([400, 1280]): Initialized by user-defined `init_weights` in TSMHead cls_head.fc_cls.bias - torch.Size([400]): Initialized by user-defined `init_weights` in TSMHead 2023/04/13 19:56:21 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2023/04/13 19:56:21 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/04/13 19:56:21 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb. 2023/04/13 19:56:42 - mmengine - INFO - Epoch(train) [1][ 20/1879] lr: 2.0000e-02 eta: 2 days, 5:34:18 time: 1.0265 data_time: 0.3083 memory: 6717 grad_norm: 3.5554 loss: 6.1053 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 6.1053 2023/04/13 19:56:48 - mmengine - INFO - Epoch(train) [1][ 40/1879] lr: 2.0000e-02 eta: 1 day, 10:40:23 time: 0.3024 data_time: 0.0116 memory: 6717 grad_norm: 4.2905 loss: 5.9183 top1_acc: 0.0625 top5_acc: 0.1250 loss_cls: 5.9183 2023/04/13 19:56:56 - mmengine - INFO - Epoch(train) [1][ 60/1879] lr: 2.0000e-02 eta: 1 day, 6:05:08 time: 0.4009 data_time: 0.0152 memory: 6717 grad_norm: 4.1176 loss: 5.6065 top1_acc: 0.0625 top5_acc: 0.1875 loss_cls: 5.6065 2023/04/13 19:57:02 - mmengine - INFO - Epoch(train) [1][ 80/1879] lr: 2.0000e-02 eta: 1 day, 2:53:35 time: 0.3321 data_time: 0.0121 memory: 6717 grad_norm: 3.1424 loss: 4.9048 top1_acc: 0.0625 top5_acc: 0.1250 loss_cls: 4.9048 2023/04/13 19:57:10 - mmengine - INFO - Epoch(train) [1][ 100/1879] lr: 2.0000e-02 eta: 1 day, 1:25:02 time: 0.3743 data_time: 0.0143 memory: 6717 grad_norm: 3.3182 loss: 4.5667 top1_acc: 0.1250 top5_acc: 0.3125 loss_cls: 4.5667 2023/04/13 19:57:17 - mmengine - INFO - Epoch(train) [1][ 120/1879] lr: 2.0000e-02 eta: 1 day, 0:11:39 time: 0.3468 data_time: 0.0113 memory: 6717 grad_norm: 3.4546 loss: 4.4975 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 4.4975 2023/04/13 19:57:25 - mmengine - INFO - Epoch(train) [1][ 140/1879] lr: 2.0000e-02 eta: 23:41:12 time: 0.3961 data_time: 0.0151 memory: 6717 grad_norm: 3.6329 loss: 4.1608 top1_acc: 0.1250 top5_acc: 0.3125 loss_cls: 4.1608 2023/04/13 19:57:31 - mmengine - INFO - Epoch(train) [1][ 160/1879] lr: 2.0000e-02 eta: 22:50:33 time: 0.3250 data_time: 0.0123 memory: 6717 grad_norm: 3.3876 loss: 4.1892 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.1892 2023/04/13 19:57:39 - mmengine - INFO - Epoch(train) [1][ 180/1879] lr: 2.0000e-02 eta: 22:44:31 time: 0.4211 data_time: 0.0160 memory: 6717 grad_norm: 3.4419 loss: 4.2235 top1_acc: 0.1875 top5_acc: 0.1875 loss_cls: 4.2235 2023/04/13 19:57:46 - mmengine - INFO - Epoch(train) [1][ 200/1879] lr: 2.0000e-02 eta: 22:06:46 time: 0.3160 data_time: 0.0121 memory: 6717 grad_norm: 3.5244 loss: 4.2293 top1_acc: 0.0000 top5_acc: 0.3125 loss_cls: 4.2293 2023/04/13 19:57:55 - mmengine - INFO - Epoch(train) [1][ 220/1879] lr: 2.0000e-02 eta: 22:11:59 time: 0.4430 data_time: 0.0141 memory: 6717 grad_norm: 3.4678 loss: 4.1043 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 4.1043 2023/04/13 19:58:01 - mmengine - INFO - Epoch(train) [1][ 240/1879] lr: 2.0000e-02 eta: 21:43:19 time: 0.3163 data_time: 0.0122 memory: 6717 grad_norm: 3.5551 loss: 4.1515 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 4.1515 2023/04/13 19:58:09 - mmengine - INFO - Epoch(train) [1][ 260/1879] lr: 2.0000e-02 eta: 21:38:23 time: 0.3968 data_time: 0.0138 memory: 6717 grad_norm: 3.4215 loss: 4.1393 top1_acc: 0.1250 top5_acc: 0.1875 loss_cls: 4.1393 2023/04/13 19:58:16 - mmengine - INFO - Epoch(train) [1][ 280/1879] lr: 2.0000e-02 eta: 21:19:33 time: 0.3315 data_time: 0.0140 memory: 6717 grad_norm: 3.7435 loss: 3.8568 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 3.8568 2023/04/13 19:58:24 - mmengine - INFO - Epoch(train) [1][ 300/1879] lr: 2.0000e-02 eta: 21:19:22 time: 0.4090 data_time: 0.0155 memory: 6717 grad_norm: 3.6498 loss: 3.9092 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.9092 2023/04/13 19:58:30 - mmengine - INFO - Epoch(train) [1][ 320/1879] lr: 2.0000e-02 eta: 21:00:07 time: 0.3113 data_time: 0.0114 memory: 6717 grad_norm: 3.5516 loss: 3.9330 top1_acc: 0.1250 top5_acc: 0.4375 loss_cls: 3.9330 2023/04/13 19:58:38 - mmengine - INFO - Epoch(train) [1][ 340/1879] lr: 2.0000e-02 eta: 21:02:10 time: 0.4150 data_time: 0.0154 memory: 6717 grad_norm: 3.5971 loss: 4.0406 top1_acc: 0.1250 top5_acc: 0.1250 loss_cls: 4.0406 2023/04/13 19:58:45 - mmengine - INFO - Epoch(train) [1][ 360/1879] lr: 2.0000e-02 eta: 20:49:26 time: 0.3311 data_time: 0.0123 memory: 6717 grad_norm: 3.5802 loss: 4.0429 top1_acc: 0.0625 top5_acc: 0.2500 loss_cls: 4.0429 2023/04/13 19:58:53 - mmengine - INFO - Epoch(train) [1][ 380/1879] lr: 2.0000e-02 eta: 20:52:02 time: 0.4164 data_time: 0.0140 memory: 6717 grad_norm: 3.5579 loss: 3.9227 top1_acc: 0.1875 top5_acc: 0.3125 loss_cls: 3.9227 2023/04/13 19:58:59 - mmengine - INFO - Epoch(train) [1][ 400/1879] lr: 2.0000e-02 eta: 20:35:03 time: 0.2928 data_time: 0.0128 memory: 6717 grad_norm: 3.7366 loss: 3.9516 top1_acc: 0.0625 top5_acc: 0.3750 loss_cls: 3.9516 2023/04/13 19:59:08 - mmengine - INFO - Epoch(train) [1][ 420/1879] lr: 2.0000e-02 eta: 20:40:08 time: 0.4302 data_time: 0.0149 memory: 6717 grad_norm: 3.6015 loss: 3.6917 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.6917 2023/04/13 19:59:14 - mmengine - INFO - Epoch(train) [1][ 440/1879] lr: 2.0000e-02 eta: 20:26:22 time: 0.3009 data_time: 0.0590 memory: 6717 grad_norm: 3.7996 loss: 3.6401 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 3.6401 2023/04/13 19:59:21 - mmengine - INFO - Epoch(train) [1][ 460/1879] lr: 2.0000e-02 eta: 20:24:27 time: 0.3794 data_time: 0.0774 memory: 6717 grad_norm: 3.4869 loss: 3.4502 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 3.4502 2023/04/13 19:59:29 - mmengine - INFO - Epoch(train) [1][ 480/1879] lr: 2.0000e-02 eta: 20:20:27 time: 0.3622 data_time: 0.0125 memory: 6717 grad_norm: 3.7078 loss: 3.5011 top1_acc: 0.0625 top5_acc: 0.3125 loss_cls: 3.5011 2023/04/13 19:59:36 - mmengine - INFO - Epoch(train) [1][ 500/1879] lr: 2.0000e-02 eta: 20:19:15 time: 0.3822 data_time: 0.0139 memory: 6717 grad_norm: 3.5824 loss: 3.4840 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 3.4840 2023/04/13 19:59:43 - mmengine - INFO - Epoch(train) [1][ 520/1879] lr: 2.0000e-02 eta: 20:14:36 time: 0.3527 data_time: 0.0128 memory: 6717 grad_norm: 3.9014 loss: 3.6385 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 3.6385 2023/04/13 19:59:51 - mmengine - INFO - Epoch(train) [1][ 540/1879] lr: 2.0000e-02 eta: 20:13:31 time: 0.3806 data_time: 0.0142 memory: 6717 grad_norm: 3.5865 loss: 3.4778 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.4778 2023/04/13 19:59:57 - mmengine - INFO - Epoch(train) [1][ 560/1879] lr: 2.0000e-02 eta: 20:06:17 time: 0.3250 data_time: 0.0124 memory: 6717 grad_norm: 3.7109 loss: 3.5039 top1_acc: 0.3125 top5_acc: 0.4375 loss_cls: 3.5039 2023/04/13 20:00:05 - mmengine - INFO - Epoch(train) [1][ 580/1879] lr: 2.0000e-02 eta: 20:08:10 time: 0.4049 data_time: 0.0143 memory: 6717 grad_norm: 4.4494 loss: 3.4977 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 3.4977 2023/04/13 20:00:12 - mmengine - INFO - Epoch(train) [1][ 600/1879] lr: 2.0000e-02 eta: 20:04:06 time: 0.3492 data_time: 0.0128 memory: 6717 grad_norm: 3.5191 loss: 3.8234 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.8234 2023/04/13 20:00:20 - mmengine - INFO - Epoch(train) [1][ 620/1879] lr: 2.0000e-02 eta: 20:05:06 time: 0.3969 data_time: 0.0141 memory: 6717 grad_norm: 3.6153 loss: 3.4790 top1_acc: 0.1875 top5_acc: 0.3125 loss_cls: 3.4790 2023/04/13 20:00:27 - mmengine - INFO - Epoch(train) [1][ 640/1879] lr: 2.0000e-02 eta: 19:58:40 time: 0.3216 data_time: 0.0123 memory: 6717 grad_norm: 3.5641 loss: 3.4574 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 3.4574 2023/04/13 20:00:35 - mmengine - INFO - Epoch(train) [1][ 660/1879] lr: 2.0000e-02 eta: 20:01:22 time: 0.4139 data_time: 0.0147 memory: 6717 grad_norm: 3.5145 loss: 3.4544 top1_acc: 0.3125 top5_acc: 0.4375 loss_cls: 3.4544 2023/04/13 20:00:42 - mmengine - INFO - Epoch(train) [1][ 680/1879] lr: 2.0000e-02 eta: 19:59:04 time: 0.3613 data_time: 0.0139 memory: 6717 grad_norm: 3.5154 loss: 3.4799 top1_acc: 0.1250 top5_acc: 0.1875 loss_cls: 3.4799 2023/04/13 20:00:50 - mmengine - INFO - Epoch(train) [1][ 700/1879] lr: 2.0000e-02 eta: 19:59:31 time: 0.3908 data_time: 0.0125 memory: 6717 grad_norm: 3.5450 loss: 3.4833 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.4833 2023/04/13 20:00:57 - mmengine - INFO - Epoch(train) [1][ 720/1879] lr: 2.0000e-02 eta: 19:54:45 time: 0.3310 data_time: 0.0146 memory: 6717 grad_norm: 3.5182 loss: 3.5227 top1_acc: 0.0625 top5_acc: 0.6250 loss_cls: 3.5227 2023/04/13 20:01:05 - mmengine - INFO - Epoch(train) [1][ 740/1879] lr: 2.0000e-02 eta: 19:58:41 time: 0.4311 data_time: 0.0134 memory: 6717 grad_norm: 3.4574 loss: 3.4874 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 3.4874 2023/04/13 20:01:12 - mmengine - INFO - Epoch(train) [1][ 760/1879] lr: 2.0000e-02 eta: 19:53:31 time: 0.3228 data_time: 0.0136 memory: 6717 grad_norm: 3.5323 loss: 3.4296 top1_acc: 0.1250 top5_acc: 0.4375 loss_cls: 3.4296 2023/04/13 20:01:20 - mmengine - INFO - Epoch(train) [1][ 780/1879] lr: 2.0000e-02 eta: 19:54:36 time: 0.3978 data_time: 0.0135 memory: 6717 grad_norm: 3.5687 loss: 3.4996 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 3.4996 2023/04/13 20:01:27 - mmengine - INFO - Epoch(train) [1][ 800/1879] lr: 2.0000e-02 eta: 19:52:13 time: 0.3542 data_time: 0.0129 memory: 6717 grad_norm: 4.2632 loss: 3.4975 top1_acc: 0.4375 top5_acc: 0.5000 loss_cls: 3.4975 2023/04/13 20:01:34 - mmengine - INFO - Epoch(train) [1][ 820/1879] lr: 2.0000e-02 eta: 19:50:19 time: 0.3589 data_time: 0.0163 memory: 6717 grad_norm: 3.5826 loss: 3.6530 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 3.6530 2023/04/13 20:01:42 - mmengine - INFO - Epoch(train) [1][ 840/1879] lr: 2.0000e-02 eta: 19:50:02 time: 0.3798 data_time: 0.0136 memory: 6717 grad_norm: 3.5133 loss: 3.4190 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 3.4190 2023/04/13 20:01:48 - mmengine - INFO - Epoch(train) [1][ 860/1879] lr: 2.0000e-02 eta: 19:47:04 time: 0.3425 data_time: 0.0139 memory: 6717 grad_norm: 3.4336 loss: 3.5711 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 3.5711 2023/04/13 20:01:56 - mmengine - INFO - Epoch(train) [1][ 880/1879] lr: 2.0000e-02 eta: 19:48:15 time: 0.3993 data_time: 0.0127 memory: 6717 grad_norm: 3.5491 loss: 3.5423 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 3.5423 2023/04/13 20:02:03 - mmengine - INFO - Epoch(train) [1][ 900/1879] lr: 2.0000e-02 eta: 19:44:54 time: 0.3345 data_time: 0.0150 memory: 6717 grad_norm: 3.5694 loss: 3.3535 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 3.3535 2023/04/13 20:02:11 - mmengine - INFO - Epoch(train) [1][ 920/1879] lr: 2.0000e-02 eta: 19:46:02 time: 0.3989 data_time: 0.0149 memory: 6717 grad_norm: 3.5195 loss: 3.4240 top1_acc: 0.1250 top5_acc: 0.4375 loss_cls: 3.4240 2023/04/13 20:02:17 - mmengine - INFO - Epoch(train) [1][ 940/1879] lr: 2.0000e-02 eta: 19:41:09 time: 0.3088 data_time: 0.0137 memory: 6717 grad_norm: 3.6502 loss: 3.5349 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 3.5349 2023/04/13 20:02:25 - mmengine - INFO - Epoch(train) [1][ 960/1879] lr: 2.0000e-02 eta: 19:42:06 time: 0.3957 data_time: 0.0151 memory: 6717 grad_norm: 3.6293 loss: 3.3463 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 3.3463 2023/04/13 20:02:32 - mmengine - INFO - Epoch(train) [1][ 980/1879] lr: 2.0000e-02 eta: 19:40:37 time: 0.3580 data_time: 0.0122 memory: 6717 grad_norm: 3.5272 loss: 3.6116 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 3.6116 2023/04/13 20:02:41 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 20:02:41 - mmengine - INFO - Epoch(train) [1][1000/1879] lr: 2.0000e-02 eta: 19:43:46 time: 0.4316 data_time: 0.0144 memory: 6717 grad_norm: 3.5968 loss: 3.1913 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.1913 2023/04/13 20:02:47 - mmengine - INFO - Epoch(train) [1][1020/1879] lr: 2.0000e-02 eta: 19:40:06 time: 0.3220 data_time: 0.0118 memory: 6717 grad_norm: 3.3865 loss: 3.2614 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.2614 2023/04/13 20:02:55 - mmengine - INFO - Epoch(train) [1][1040/1879] lr: 2.0000e-02 eta: 19:39:42 time: 0.3742 data_time: 0.0154 memory: 6717 grad_norm: 3.3972 loss: 3.3537 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 3.3537 2023/04/13 20:03:01 - mmengine - INFO - Epoch(train) [1][1060/1879] lr: 2.0000e-02 eta: 19:36:33 time: 0.3274 data_time: 0.0134 memory: 6717 grad_norm: 3.5513 loss: 3.0134 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 3.0134 2023/04/13 20:03:09 - mmengine - INFO - Epoch(train) [1][1080/1879] lr: 2.0000e-02 eta: 19:36:55 time: 0.3864 data_time: 0.0150 memory: 6717 grad_norm: 3.5728 loss: 3.0634 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 3.0634 2023/04/13 20:03:17 - mmengine - INFO - Epoch(train) [1][1100/1879] lr: 2.0000e-02 eta: 19:36:24 time: 0.3711 data_time: 0.0124 memory: 6717 grad_norm: 3.8056 loss: 3.3522 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 3.3522 2023/04/13 20:03:24 - mmengine - INFO - Epoch(train) [1][1120/1879] lr: 2.0000e-02 eta: 19:34:59 time: 0.3547 data_time: 0.0132 memory: 6717 grad_norm: 3.4735 loss: 3.2935 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 3.2935 2023/04/13 20:03:31 - mmengine - INFO - Epoch(train) [1][1140/1879] lr: 2.0000e-02 eta: 19:33:02 time: 0.3441 data_time: 0.0132 memory: 6717 grad_norm: 3.5419 loss: 3.0124 top1_acc: 0.1250 top5_acc: 0.5625 loss_cls: 3.0124 2023/04/13 20:03:39 - mmengine - INFO - Epoch(train) [1][1160/1879] lr: 2.0000e-02 eta: 19:35:09 time: 0.4185 data_time: 0.0135 memory: 6717 grad_norm: 3.4216 loss: 3.1279 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 3.1279 2023/04/13 20:03:46 - mmengine - INFO - Epoch(train) [1][1180/1879] lr: 2.0000e-02 eta: 19:33:27 time: 0.3480 data_time: 0.0135 memory: 6717 grad_norm: 3.4659 loss: 3.1727 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 3.1727 2023/04/13 20:03:54 - mmengine - INFO - Epoch(train) [1][1200/1879] lr: 2.0000e-02 eta: 19:34:38 time: 0.4022 data_time: 0.0120 memory: 6717 grad_norm: 3.4350 loss: 3.2372 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 3.2372 2023/04/13 20:04:00 - mmengine - INFO - Epoch(train) [1][1220/1879] lr: 2.0000e-02 eta: 19:31:38 time: 0.3210 data_time: 0.0152 memory: 6717 grad_norm: 3.4624 loss: 3.1682 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 3.1682 2023/04/13 20:04:08 - mmengine - INFO - Epoch(train) [1][1240/1879] lr: 2.0000e-02 eta: 19:32:33 time: 0.3973 data_time: 0.0172 memory: 6717 grad_norm: 3.4590 loss: 3.0695 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 3.0695 2023/04/13 20:04:15 - mmengine - INFO - Epoch(train) [1][1260/1879] lr: 2.0000e-02 eta: 19:30:49 time: 0.3445 data_time: 0.0137 memory: 6717 grad_norm: 3.5830 loss: 3.0928 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 3.0928 2023/04/13 20:04:23 - mmengine - INFO - Epoch(train) [1][1280/1879] lr: 2.0000e-02 eta: 19:32:20 time: 0.4100 data_time: 0.0131 memory: 6717 grad_norm: 3.5377 loss: 3.2498 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 3.2498 2023/04/13 20:04:30 - mmengine - INFO - Epoch(train) [1][1300/1879] lr: 2.0000e-02 eta: 19:28:57 time: 0.3089 data_time: 0.0150 memory: 6717 grad_norm: 3.5608 loss: 3.3272 top1_acc: 0.1875 top5_acc: 0.2500 loss_cls: 3.3272 2023/04/13 20:04:38 - mmengine - INFO - Epoch(train) [1][1320/1879] lr: 2.0000e-02 eta: 19:30:43 time: 0.4161 data_time: 0.0148 memory: 6717 grad_norm: 3.5820 loss: 3.3397 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 3.3397 2023/04/13 20:04:44 - mmengine - INFO - Epoch(train) [1][1340/1879] lr: 2.0000e-02 eta: 19:28:06 time: 0.3227 data_time: 0.0186 memory: 6717 grad_norm: 3.4272 loss: 3.2983 top1_acc: 0.0625 top5_acc: 0.3750 loss_cls: 3.2983 2023/04/13 20:04:52 - mmengine - INFO - Epoch(train) [1][1360/1879] lr: 2.0000e-02 eta: 19:28:13 time: 0.3809 data_time: 0.0191 memory: 6717 grad_norm: 3.4310 loss: 3.2224 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 3.2224 2023/04/13 20:04:58 - mmengine - INFO - Epoch(train) [1][1380/1879] lr: 2.0000e-02 eta: 19:25:34 time: 0.3197 data_time: 0.0126 memory: 6717 grad_norm: 3.4627 loss: 3.1363 top1_acc: 0.1250 top5_acc: 0.4375 loss_cls: 3.1363 2023/04/13 20:05:08 - mmengine - INFO - Epoch(train) [1][1400/1879] lr: 2.0000e-02 eta: 19:29:07 time: 0.4578 data_time: 0.0151 memory: 6717 grad_norm: 3.4983 loss: 3.2577 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 3.2577 2023/04/13 20:05:14 - mmengine - INFO - Epoch(train) [1][1420/1879] lr: 2.0000e-02 eta: 19:26:45 time: 0.3249 data_time: 0.0121 memory: 6717 grad_norm: 3.5455 loss: 3.1927 top1_acc: 0.1875 top5_acc: 0.3125 loss_cls: 3.1927 2023/04/13 20:05:22 - mmengine - INFO - Epoch(train) [1][1440/1879] lr: 2.0000e-02 eta: 19:28:07 time: 0.4101 data_time: 0.0126 memory: 6717 grad_norm: 3.7047 loss: 3.1159 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 3.1159 2023/04/13 20:05:28 - mmengine - INFO - Epoch(train) [1][1460/1879] lr: 2.0000e-02 eta: 19:24:52 time: 0.3025 data_time: 0.0146 memory: 6717 grad_norm: 3.4378 loss: 3.2106 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 3.2106 2023/04/13 20:05:36 - mmengine - INFO - Epoch(train) [1][1480/1879] lr: 2.0000e-02 eta: 19:25:59 time: 0.4043 data_time: 0.0130 memory: 6717 grad_norm: 3.5850 loss: 3.1287 top1_acc: 0.3125 top5_acc: 0.4375 loss_cls: 3.1287 2023/04/13 20:05:42 - mmengine - INFO - Epoch(train) [1][1500/1879] lr: 2.0000e-02 eta: 19:22:52 time: 0.3030 data_time: 0.0139 memory: 6717 grad_norm: 3.5589 loss: 3.2607 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.2607 2023/04/13 20:05:51 - mmengine - INFO - Epoch(train) [1][1520/1879] lr: 2.0000e-02 eta: 19:24:22 time: 0.4141 data_time: 0.0137 memory: 6717 grad_norm: 3.5639 loss: 3.0831 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 3.0831 2023/04/13 20:05:57 - mmengine - INFO - Epoch(train) [1][1540/1879] lr: 2.0000e-02 eta: 19:22:24 time: 0.3290 data_time: 0.0137 memory: 6717 grad_norm: 3.5236 loss: 2.9114 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.9114 2023/04/13 20:06:06 - mmengine - INFO - Epoch(train) [1][1560/1879] lr: 2.0000e-02 eta: 19:24:56 time: 0.4411 data_time: 0.0128 memory: 6717 grad_norm: 3.3945 loss: 3.1725 top1_acc: 0.1875 top5_acc: 0.3750 loss_cls: 3.1725 2023/04/13 20:06:12 - mmengine - INFO - Epoch(train) [1][1580/1879] lr: 2.0000e-02 eta: 19:21:35 time: 0.2932 data_time: 0.0135 memory: 6717 grad_norm: 3.5529 loss: 3.0411 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 3.0411 2023/04/13 20:06:21 - mmengine - INFO - Epoch(train) [1][1600/1879] lr: 2.0000e-02 eta: 19:23:36 time: 0.4292 data_time: 0.0138 memory: 6717 grad_norm: 3.5373 loss: 3.0282 top1_acc: 0.0625 top5_acc: 0.4375 loss_cls: 3.0282 2023/04/13 20:06:27 - mmengine - INFO - Epoch(train) [1][1620/1879] lr: 2.0000e-02 eta: 19:21:43 time: 0.3290 data_time: 0.0134 memory: 6717 grad_norm: 3.4713 loss: 2.9940 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.9940 2023/04/13 20:06:36 - mmengine - INFO - Epoch(train) [1][1640/1879] lr: 2.0000e-02 eta: 19:23:39 time: 0.4286 data_time: 0.0127 memory: 6717 grad_norm: 3.5098 loss: 3.0943 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 3.0943 2023/04/13 20:06:42 - mmengine - INFO - Epoch(train) [1][1660/1879] lr: 2.0000e-02 eta: 19:21:07 time: 0.3104 data_time: 0.0155 memory: 6717 grad_norm: 3.4186 loss: 3.1310 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 3.1310 2023/04/13 20:06:50 - mmengine - INFO - Epoch(train) [1][1680/1879] lr: 2.0000e-02 eta: 19:21:39 time: 0.3916 data_time: 0.0131 memory: 6717 grad_norm: 3.5429 loss: 3.0149 top1_acc: 0.0625 top5_acc: 0.4375 loss_cls: 3.0149 2023/04/13 20:06:56 - mmengine - INFO - Epoch(train) [1][1700/1879] lr: 2.0000e-02 eta: 19:19:14 time: 0.3115 data_time: 0.0139 memory: 6717 grad_norm: 3.4940 loss: 3.0865 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 3.0865 2023/04/13 20:07:04 - mmengine - INFO - Epoch(train) [1][1720/1879] lr: 2.0000e-02 eta: 19:20:34 time: 0.4142 data_time: 0.0140 memory: 6717 grad_norm: 3.4768 loss: 2.7978 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.7978 2023/04/13 20:07:11 - mmengine - INFO - Epoch(train) [1][1740/1879] lr: 2.0000e-02 eta: 19:19:24 time: 0.3449 data_time: 0.0137 memory: 6717 grad_norm: 3.5512 loss: 3.0488 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 3.0488 2023/04/13 20:07:19 - mmengine - INFO - Epoch(train) [1][1760/1879] lr: 2.0000e-02 eta: 19:20:32 time: 0.4092 data_time: 0.0152 memory: 6717 grad_norm: 3.5548 loss: 3.3909 top1_acc: 0.1875 top5_acc: 0.6875 loss_cls: 3.3909 2023/04/13 20:07:25 - mmengine - INFO - Epoch(train) [1][1780/1879] lr: 2.0000e-02 eta: 19:17:29 time: 0.2903 data_time: 0.0123 memory: 6717 grad_norm: 3.5429 loss: 3.0398 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 3.0398 2023/04/13 20:07:34 - mmengine - INFO - Epoch(train) [1][1800/1879] lr: 2.0000e-02 eta: 19:18:52 time: 0.4170 data_time: 0.0132 memory: 6717 grad_norm: 3.4304 loss: 2.9443 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.9443 2023/04/13 20:07:40 - mmengine - INFO - Epoch(train) [1][1820/1879] lr: 2.0000e-02 eta: 19:17:19 time: 0.3316 data_time: 0.0139 memory: 6717 grad_norm: 3.3951 loss: 3.0047 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 3.0047 2023/04/13 20:07:47 - mmengine - INFO - Epoch(train) [1][1840/1879] lr: 2.0000e-02 eta: 19:16:52 time: 0.3637 data_time: 0.0143 memory: 6717 grad_norm: 4.8312 loss: 3.0779 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 3.0779 2023/04/13 20:07:54 - mmengine - INFO - Epoch(train) [1][1860/1879] lr: 2.0000e-02 eta: 19:15:20 time: 0.3306 data_time: 0.0134 memory: 6717 grad_norm: 3.4529 loss: 3.2522 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 3.2522 2023/04/13 20:08:01 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 20:08:01 - mmengine - INFO - Epoch(train) [1][1879/1879] lr: 2.0000e-02 eta: 19:15:20 time: 0.3643 data_time: 0.0121 memory: 6717 grad_norm: 3.5338 loss: 3.1073 top1_acc: 0.2857 top5_acc: 0.4286 loss_cls: 3.1073 2023/04/13 20:08:16 - mmengine - INFO - Epoch(val) [1][ 20/155] eta: 0:01:36 time: 0.7152 data_time: 0.6833 memory: 1391 2023/04/13 20:08:24 - mmengine - INFO - Epoch(val) [1][ 40/155] eta: 0:01:05 time: 0.4159 data_time: 0.3834 memory: 1391 2023/04/13 20:08:30 - mmengine - INFO - Epoch(val) [1][ 60/155] eta: 0:00:45 time: 0.3096 data_time: 0.2775 memory: 1391 2023/04/13 20:08:39 - mmengine - INFO - Epoch(val) [1][ 80/155] eta: 0:00:35 time: 0.4304 data_time: 0.3984 memory: 1391 2023/04/13 20:08:45 - mmengine - INFO - Epoch(val) [1][100/155] eta: 0:00:24 time: 0.3207 data_time: 0.2888 memory: 1391 2023/04/13 20:08:53 - mmengine - INFO - Epoch(val) [1][120/155] eta: 0:00:15 time: 0.4177 data_time: 0.3863 memory: 1391 2023/04/13 20:09:00 - mmengine - INFO - Epoch(val) [1][140/155] eta: 0:00:06 time: 0.3257 data_time: 0.2942 memory: 1391 2023/04/13 20:09:07 - mmengine - INFO - Epoch(val) [1][155/155] acc/top1: 0.3239 acc/top5: 0.6093 acc/mean1: 0.3238 data_time: 0.2716 time: 0.3032 2023/04/13 20:09:08 - mmengine - INFO - The best checkpoint with 0.3239 acc/top1 at 1 epoch is saved to best_acc_top1_epoch_1.pth. 2023/04/13 20:09:17 - mmengine - INFO - Epoch(train) [2][ 20/1879] lr: 2.0000e-02 eta: 19:18:49 time: 0.4828 data_time: 0.3492 memory: 6717 grad_norm: 3.5944 loss: 3.0835 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.0835 2023/04/13 20:09:24 - mmengine - INFO - Epoch(train) [2][ 40/1879] lr: 2.0000e-02 eta: 19:16:55 time: 0.3192 data_time: 0.1425 memory: 6717 grad_norm: 3.5497 loss: 2.9919 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.9919 2023/04/13 20:09:32 - mmengine - INFO - Epoch(train) [2][ 60/1879] lr: 2.0000e-02 eta: 19:18:08 time: 0.4149 data_time: 0.1349 memory: 6717 grad_norm: 3.4533 loss: 3.0552 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 3.0552 2023/04/13 20:09:39 - mmengine - INFO - Epoch(train) [2][ 80/1879] lr: 2.0000e-02 eta: 19:16:54 time: 0.3386 data_time: 0.0577 memory: 6717 grad_norm: 3.4001 loss: 3.0078 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 3.0078 2023/04/13 20:09:47 - mmengine - INFO - Epoch(train) [2][ 100/1879] lr: 2.0000e-02 eta: 19:17:10 time: 0.3862 data_time: 0.1260 memory: 6717 grad_norm: 3.5409 loss: 2.6702 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.6702 2023/04/13 20:09:53 - mmengine - INFO - Epoch(train) [2][ 120/1879] lr: 2.0000e-02 eta: 19:16:06 time: 0.3430 data_time: 0.0914 memory: 6717 grad_norm: 3.4914 loss: 2.8258 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.8258 2023/04/13 20:09:55 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 20:10:01 - mmengine - INFO - Epoch(train) [2][ 140/1879] lr: 2.0000e-02 eta: 19:16:47 time: 0.3994 data_time: 0.0899 memory: 6717 grad_norm: 3.4473 loss: 2.9274 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.9274 2023/04/13 20:10:08 - mmengine - INFO - Epoch(train) [2][ 160/1879] lr: 2.0000e-02 eta: 19:15:02 time: 0.3198 data_time: 0.1035 memory: 6717 grad_norm: 3.4471 loss: 2.8089 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.8089 2023/04/13 20:10:17 - mmengine - INFO - Epoch(train) [2][ 180/1879] lr: 2.0000e-02 eta: 19:16:49 time: 0.4362 data_time: 0.1058 memory: 6717 grad_norm: 3.5484 loss: 2.8515 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 2.8515 2023/04/13 20:10:23 - mmengine - INFO - Epoch(train) [2][ 200/1879] lr: 2.0000e-02 eta: 19:14:39 time: 0.3049 data_time: 0.0129 memory: 6717 grad_norm: 3.4515 loss: 2.6805 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 2.6805 2023/04/13 20:10:31 - mmengine - INFO - Epoch(train) [2][ 220/1879] lr: 2.0000e-02 eta: 19:15:38 time: 0.4105 data_time: 0.0232 memory: 6717 grad_norm: 3.4970 loss: 2.9786 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 2.9786 2023/04/13 20:10:36 - mmengine - INFO - Epoch(train) [2][ 240/1879] lr: 2.0000e-02 eta: 19:12:43 time: 0.2776 data_time: 0.0125 memory: 6717 grad_norm: 4.3857 loss: 2.9304 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 2.9304 2023/04/13 20:10:45 - mmengine - INFO - Epoch(train) [2][ 260/1879] lr: 2.0000e-02 eta: 19:14:13 time: 0.4286 data_time: 0.0151 memory: 6717 grad_norm: 3.4853 loss: 2.9570 top1_acc: 0.1875 top5_acc: 0.3750 loss_cls: 2.9570 2023/04/13 20:10:51 - mmengine - INFO - Epoch(train) [2][ 280/1879] lr: 2.0000e-02 eta: 19:12:04 time: 0.3021 data_time: 0.0126 memory: 6717 grad_norm: 3.3354 loss: 3.3224 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 3.3224 2023/04/13 20:10:59 - mmengine - INFO - Epoch(train) [2][ 300/1879] lr: 2.0000e-02 eta: 19:12:44 time: 0.3996 data_time: 0.0148 memory: 6717 grad_norm: 3.3876 loss: 2.8866 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.8866 2023/04/13 20:11:06 - mmengine - INFO - Epoch(train) [2][ 320/1879] lr: 2.0000e-02 eta: 19:11:40 time: 0.3393 data_time: 0.0120 memory: 6717 grad_norm: 3.3907 loss: 2.9837 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.9837 2023/04/13 20:11:15 - mmengine - INFO - Epoch(train) [2][ 340/1879] lr: 2.0000e-02 eta: 19:13:22 time: 0.4377 data_time: 0.0147 memory: 6717 grad_norm: 3.4656 loss: 2.8728 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.8728 2023/04/13 20:11:21 - mmengine - INFO - Epoch(train) [2][ 360/1879] lr: 2.0000e-02 eta: 19:11:49 time: 0.3207 data_time: 0.0124 memory: 6717 grad_norm: 3.3586 loss: 2.8202 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.8202 2023/04/13 20:11:29 - mmengine - INFO - Epoch(train) [2][ 380/1879] lr: 2.0000e-02 eta: 19:12:37 time: 0.4061 data_time: 0.0147 memory: 6717 grad_norm: 3.4345 loss: 2.9506 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.9506 2023/04/13 20:11:36 - mmengine - INFO - Epoch(train) [2][ 400/1879] lr: 2.0000e-02 eta: 19:12:24 time: 0.3692 data_time: 0.0122 memory: 6717 grad_norm: 3.4667 loss: 2.9975 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.9975 2023/04/13 20:11:44 - mmengine - INFO - Epoch(train) [2][ 420/1879] lr: 2.0000e-02 eta: 19:12:06 time: 0.3661 data_time: 0.0134 memory: 6717 grad_norm: 3.4013 loss: 2.8291 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.8291 2023/04/13 20:11:51 - mmengine - INFO - Epoch(train) [2][ 440/1879] lr: 2.0000e-02 eta: 19:11:17 time: 0.3460 data_time: 0.0145 memory: 6717 grad_norm: 3.4295 loss: 2.6913 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.6913 2023/04/13 20:11:58 - mmengine - INFO - Epoch(train) [2][ 460/1879] lr: 2.0000e-02 eta: 19:10:41 time: 0.3547 data_time: 0.0124 memory: 6717 grad_norm: 3.6594 loss: 2.9047 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.9047 2023/04/13 20:12:05 - mmengine - INFO - Epoch(train) [2][ 480/1879] lr: 2.0000e-02 eta: 19:10:11 time: 0.3578 data_time: 0.0141 memory: 6717 grad_norm: 3.3889 loss: 3.0752 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 3.0752 2023/04/13 20:12:13 - mmengine - INFO - Epoch(train) [2][ 500/1879] lr: 2.0000e-02 eta: 19:10:52 time: 0.4027 data_time: 0.0231 memory: 6717 grad_norm: 3.4957 loss: 2.8638 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.8638 2023/04/13 20:12:20 - mmengine - INFO - Epoch(train) [2][ 520/1879] lr: 2.0000e-02 eta: 19:09:47 time: 0.3352 data_time: 0.0398 memory: 6717 grad_norm: 3.3819 loss: 2.9522 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.9522 2023/04/13 20:12:28 - mmengine - INFO - Epoch(train) [2][ 540/1879] lr: 2.0000e-02 eta: 19:11:14 time: 0.4336 data_time: 0.0142 memory: 6717 grad_norm: 3.3761 loss: 2.8269 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.8269 2023/04/13 20:12:35 - mmengine - INFO - Epoch(train) [2][ 560/1879] lr: 2.0000e-02 eta: 19:10:14 time: 0.3374 data_time: 0.0124 memory: 6717 grad_norm: 3.4183 loss: 2.7704 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.7704 2023/04/13 20:12:43 - mmengine - INFO - Epoch(train) [2][ 580/1879] lr: 2.0000e-02 eta: 19:11:09 time: 0.4137 data_time: 0.0128 memory: 6717 grad_norm: 3.3314 loss: 2.9217 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.9217 2023/04/13 20:12:50 - mmengine - INFO - Epoch(train) [2][ 600/1879] lr: 2.0000e-02 eta: 19:10:20 time: 0.3446 data_time: 0.0142 memory: 6717 grad_norm: 3.4757 loss: 2.7102 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.7102 2023/04/13 20:12:58 - mmengine - INFO - Epoch(train) [2][ 620/1879] lr: 2.0000e-02 eta: 19:10:55 time: 0.4013 data_time: 0.0154 memory: 6717 grad_norm: 3.4631 loss: 2.9030 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.9030 2023/04/13 20:13:05 - mmengine - INFO - Epoch(train) [2][ 640/1879] lr: 2.0000e-02 eta: 19:10:02 time: 0.3410 data_time: 0.0135 memory: 6717 grad_norm: 3.3617 loss: 2.8641 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.8641 2023/04/13 20:13:13 - mmengine - INFO - Epoch(train) [2][ 660/1879] lr: 2.0000e-02 eta: 19:10:44 time: 0.4063 data_time: 0.0159 memory: 6717 grad_norm: 3.4011 loss: 2.8090 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.8090 2023/04/13 20:13:20 - mmengine - INFO - Epoch(train) [2][ 680/1879] lr: 2.0000e-02 eta: 19:09:45 time: 0.3370 data_time: 0.0121 memory: 6717 grad_norm: 3.5306 loss: 2.9572 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 2.9572 2023/04/13 20:13:28 - mmengine - INFO - Epoch(train) [2][ 700/1879] lr: 2.0000e-02 eta: 19:10:22 time: 0.4032 data_time: 0.0151 memory: 6717 grad_norm: 3.4335 loss: 2.9085 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.9085 2023/04/13 20:13:35 - mmengine - INFO - Epoch(train) [2][ 720/1879] lr: 2.0000e-02 eta: 19:09:44 time: 0.3510 data_time: 0.0113 memory: 6717 grad_norm: 3.4196 loss: 2.9615 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.9615 2023/04/13 20:13:44 - mmengine - INFO - Epoch(train) [2][ 740/1879] lr: 2.0000e-02 eta: 19:10:47 time: 0.4222 data_time: 0.0138 memory: 6717 grad_norm: 3.4390 loss: 2.7964 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.7964 2023/04/13 20:13:50 - mmengine - INFO - Epoch(train) [2][ 760/1879] lr: 2.0000e-02 eta: 19:09:06 time: 0.3055 data_time: 0.0137 memory: 6717 grad_norm: 3.4607 loss: 2.8686 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.8686 2023/04/13 20:13:58 - mmengine - INFO - Epoch(train) [2][ 780/1879] lr: 2.0000e-02 eta: 19:09:52 time: 0.4105 data_time: 0.0128 memory: 6717 grad_norm: 3.3809 loss: 2.8244 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 2.8244 2023/04/13 20:14:05 - mmengine - INFO - Epoch(train) [2][ 800/1879] lr: 2.0000e-02 eta: 19:09:02 time: 0.3421 data_time: 0.0133 memory: 6717 grad_norm: 3.4459 loss: 2.7897 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.7897 2023/04/13 20:14:13 - mmengine - INFO - Epoch(train) [2][ 820/1879] lr: 2.0000e-02 eta: 19:09:36 time: 0.4019 data_time: 0.0126 memory: 6717 grad_norm: 3.3680 loss: 2.8748 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.8748 2023/04/13 20:14:19 - mmengine - INFO - Epoch(train) [2][ 840/1879] lr: 2.0000e-02 eta: 19:08:04 time: 0.3103 data_time: 0.0135 memory: 6717 grad_norm: 3.4185 loss: 2.9396 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.9396 2023/04/13 20:14:27 - mmengine - INFO - Epoch(train) [2][ 860/1879] lr: 2.0000e-02 eta: 19:08:18 time: 0.3884 data_time: 0.0142 memory: 6717 grad_norm: 3.3926 loss: 2.7152 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7152 2023/04/13 20:14:34 - mmengine - INFO - Epoch(train) [2][ 880/1879] lr: 2.0000e-02 eta: 19:08:08 time: 0.3698 data_time: 0.0137 memory: 6717 grad_norm: 3.4570 loss: 2.6503 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.6503 2023/04/13 20:14:42 - mmengine - INFO - Epoch(train) [2][ 900/1879] lr: 2.0000e-02 eta: 19:08:56 time: 0.4136 data_time: 0.0145 memory: 6717 grad_norm: 3.4532 loss: 2.7897 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.7897 2023/04/13 20:14:48 - mmengine - INFO - Epoch(train) [2][ 920/1879] lr: 2.0000e-02 eta: 19:07:18 time: 0.3039 data_time: 0.0123 memory: 6717 grad_norm: 3.4539 loss: 2.6463 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 2.6463 2023/04/13 20:14:56 - mmengine - INFO - Epoch(train) [2][ 940/1879] lr: 2.0000e-02 eta: 19:07:35 time: 0.3906 data_time: 0.0146 memory: 6717 grad_norm: 3.3716 loss: 2.6647 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 2.6647 2023/04/13 20:15:03 - mmengine - INFO - Epoch(train) [2][ 960/1879] lr: 2.0000e-02 eta: 19:06:49 time: 0.3428 data_time: 0.0126 memory: 6717 grad_norm: 3.5684 loss: 2.8311 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.8311 2023/04/13 20:15:10 - mmengine - INFO - Epoch(train) [2][ 980/1879] lr: 2.0000e-02 eta: 19:06:34 time: 0.3656 data_time: 0.0133 memory: 6717 grad_norm: 3.4790 loss: 2.6289 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.6289 2023/04/13 20:15:19 - mmengine - INFO - Epoch(train) [2][1000/1879] lr: 2.0000e-02 eta: 19:07:23 time: 0.4159 data_time: 0.0138 memory: 6717 grad_norm: 3.4074 loss: 2.9603 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.9603 2023/04/13 20:15:26 - mmengine - INFO - Epoch(train) [2][1020/1879] lr: 2.0000e-02 eta: 19:06:35 time: 0.3401 data_time: 0.0125 memory: 6717 grad_norm: 3.3306 loss: 2.7257 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.7257 2023/04/13 20:15:33 - mmengine - INFO - Epoch(train) [2][1040/1879] lr: 2.0000e-02 eta: 19:06:18 time: 0.3648 data_time: 0.0148 memory: 6717 grad_norm: 3.3641 loss: 2.9303 top1_acc: 0.3125 top5_acc: 0.3125 loss_cls: 2.9303 2023/04/13 20:15:40 - mmengine - INFO - Epoch(train) [2][1060/1879] lr: 2.0000e-02 eta: 19:06:02 time: 0.3647 data_time: 0.0118 memory: 6717 grad_norm: 3.3973 loss: 2.9020 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.9020 2023/04/13 20:15:48 - mmengine - INFO - Epoch(train) [2][1080/1879] lr: 2.0000e-02 eta: 19:06:19 time: 0.3913 data_time: 0.0128 memory: 6717 grad_norm: 3.3383 loss: 2.7695 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.7695 2023/04/13 20:15:55 - mmengine - INFO - Epoch(train) [2][1100/1879] lr: 2.0000e-02 eta: 19:06:13 time: 0.3735 data_time: 0.0134 memory: 6717 grad_norm: 3.3835 loss: 2.8725 top1_acc: 0.1875 top5_acc: 0.8125 loss_cls: 2.8725 2023/04/13 20:16:02 - mmengine - INFO - Epoch(train) [2][1120/1879] lr: 2.0000e-02 eta: 19:05:25 time: 0.3387 data_time: 0.0135 memory: 6717 grad_norm: 3.4189 loss: 3.0054 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.0054 2023/04/13 20:16:02 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 20:16:10 - mmengine - INFO - Epoch(train) [2][1140/1879] lr: 2.0000e-02 eta: 19:05:36 time: 0.3870 data_time: 0.0148 memory: 6717 grad_norm: 3.3500 loss: 2.8258 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.8258 2023/04/13 20:16:17 - mmengine - INFO - Epoch(train) [2][1160/1879] lr: 2.0000e-02 eta: 19:04:47 time: 0.3376 data_time: 0.0123 memory: 6717 grad_norm: 3.3281 loss: 2.7675 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.7675 2023/04/13 20:16:25 - mmengine - INFO - Epoch(train) [2][1180/1879] lr: 2.0000e-02 eta: 19:05:17 time: 0.4022 data_time: 0.0230 memory: 6717 grad_norm: 3.4040 loss: 2.9041 top1_acc: 0.1875 top5_acc: 0.8750 loss_cls: 2.9041 2023/04/13 20:16:31 - mmengine - INFO - Epoch(train) [2][1200/1879] lr: 2.0000e-02 eta: 19:04:15 time: 0.3261 data_time: 0.0120 memory: 6717 grad_norm: 3.4058 loss: 2.9390 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.9390 2023/04/13 20:16:40 - mmengine - INFO - Epoch(train) [2][1220/1879] lr: 2.0000e-02 eta: 19:04:59 time: 0.4149 data_time: 0.0141 memory: 6717 grad_norm: 3.4117 loss: 2.7490 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.7490 2023/04/13 20:16:47 - mmengine - INFO - Epoch(train) [2][1240/1879] lr: 2.0000e-02 eta: 19:04:41 time: 0.3628 data_time: 0.0121 memory: 6717 grad_norm: 3.3836 loss: 2.8365 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.8365 2023/04/13 20:16:54 - mmengine - INFO - Epoch(train) [2][1260/1879] lr: 2.0000e-02 eta: 19:04:07 time: 0.3492 data_time: 0.0140 memory: 6717 grad_norm: 3.4221 loss: 2.7015 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.7015 2023/04/13 20:17:01 - mmengine - INFO - Epoch(train) [2][1280/1879] lr: 2.0000e-02 eta: 19:03:47 time: 0.3608 data_time: 0.0131 memory: 6717 grad_norm: 3.2842 loss: 2.6422 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6422 2023/04/13 20:17:10 - mmengine - INFO - Epoch(train) [2][1300/1879] lr: 2.0000e-02 eta: 19:04:49 time: 0.4308 data_time: 0.0128 memory: 6717 grad_norm: 3.4307 loss: 2.6736 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 2.6736 2023/04/13 20:17:16 - mmengine - INFO - Epoch(train) [2][1320/1879] lr: 2.0000e-02 eta: 19:03:53 time: 0.3305 data_time: 0.0144 memory: 6717 grad_norm: 3.4049 loss: 2.5654 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.5654 2023/04/13 20:17:24 - mmengine - INFO - Epoch(train) [2][1340/1879] lr: 2.0000e-02 eta: 19:04:30 time: 0.4100 data_time: 0.0131 memory: 6717 grad_norm: 3.3287 loss: 2.7203 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 2.7203 2023/04/13 20:17:31 - mmengine - INFO - Epoch(train) [2][1360/1879] lr: 2.0000e-02 eta: 19:03:29 time: 0.3245 data_time: 0.0123 memory: 6717 grad_norm: 3.2972 loss: 2.6188 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.6188 2023/04/13 20:17:39 - mmengine - INFO - Epoch(train) [2][1380/1879] lr: 2.0000e-02 eta: 19:04:04 time: 0.4098 data_time: 0.0129 memory: 6717 grad_norm: 3.4071 loss: 2.7028 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.7028 2023/04/13 20:17:45 - mmengine - INFO - Epoch(train) [2][1400/1879] lr: 2.0000e-02 eta: 19:02:49 time: 0.3114 data_time: 0.0134 memory: 6717 grad_norm: 4.1851 loss: 2.5793 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5793 2023/04/13 20:17:53 - mmengine - INFO - Epoch(train) [2][1420/1879] lr: 2.0000e-02 eta: 19:03:16 time: 0.4025 data_time: 0.0122 memory: 6717 grad_norm: 3.4856 loss: 2.9553 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.9553 2023/04/13 20:18:00 - mmengine - INFO - Epoch(train) [2][1440/1879] lr: 2.0000e-02 eta: 19:02:39 time: 0.3447 data_time: 0.0137 memory: 6717 grad_norm: 3.3996 loss: 2.6837 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 2.6837 2023/04/13 20:18:08 - mmengine - INFO - Epoch(train) [2][1460/1879] lr: 2.0000e-02 eta: 19:03:01 time: 0.3977 data_time: 0.0128 memory: 6717 grad_norm: 3.2770 loss: 2.7210 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.7210 2023/04/13 20:18:15 - mmengine - INFO - Epoch(train) [2][1480/1879] lr: 2.0000e-02 eta: 19:02:06 time: 0.3283 data_time: 0.0145 memory: 6717 grad_norm: 3.3257 loss: 2.9017 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.9017 2023/04/13 20:18:24 - mmengine - INFO - Epoch(train) [2][1500/1879] lr: 2.0000e-02 eta: 19:03:15 time: 0.4417 data_time: 0.0115 memory: 6717 grad_norm: 3.3766 loss: 3.0396 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 3.0396 2023/04/13 20:18:30 - mmengine - INFO - Epoch(train) [2][1520/1879] lr: 2.0000e-02 eta: 19:02:15 time: 0.3232 data_time: 0.0157 memory: 6717 grad_norm: 3.4133 loss: 2.5098 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5098 2023/04/13 20:18:39 - mmengine - INFO - Epoch(train) [2][1540/1879] lr: 2.0000e-02 eta: 19:03:14 time: 0.4332 data_time: 0.0125 memory: 6717 grad_norm: 3.3466 loss: 2.7476 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.7476 2023/04/13 20:18:45 - mmengine - INFO - Epoch(train) [2][1560/1879] lr: 2.0000e-02 eta: 19:01:59 time: 0.3082 data_time: 0.0143 memory: 6717 grad_norm: 3.2602 loss: 2.7617 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 2.7617 2023/04/13 20:18:53 - mmengine - INFO - Epoch(train) [2][1580/1879] lr: 2.0000e-02 eta: 19:02:18 time: 0.3964 data_time: 0.0128 memory: 6717 grad_norm: 3.3506 loss: 2.7083 top1_acc: 0.4375 top5_acc: 0.5000 loss_cls: 2.7083 2023/04/13 20:19:00 - mmengine - INFO - Epoch(train) [2][1600/1879] lr: 2.0000e-02 eta: 19:01:32 time: 0.3360 data_time: 0.0140 memory: 6717 grad_norm: 3.3024 loss: 2.7437 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 2.7437 2023/04/13 20:19:08 - mmengine - INFO - Epoch(train) [2][1620/1879] lr: 2.0000e-02 eta: 19:01:56 time: 0.4010 data_time: 0.0133 memory: 6717 grad_norm: 3.2862 loss: 2.8068 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.8068 2023/04/13 20:19:14 - mmengine - INFO - Epoch(train) [2][1640/1879] lr: 2.0000e-02 eta: 19:01:16 time: 0.3405 data_time: 0.0142 memory: 6717 grad_norm: 3.3111 loss: 2.7571 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7571 2023/04/13 20:19:23 - mmengine - INFO - Epoch(train) [2][1660/1879] lr: 2.0000e-02 eta: 19:01:54 time: 0.4145 data_time: 0.0128 memory: 6717 grad_norm: 3.3383 loss: 2.8588 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.8588 2023/04/13 20:19:29 - mmengine - INFO - Epoch(train) [2][1680/1879] lr: 2.0000e-02 eta: 19:00:44 time: 0.3118 data_time: 0.0146 memory: 6717 grad_norm: 3.3396 loss: 2.5751 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5751 2023/04/13 20:19:37 - mmengine - INFO - Epoch(train) [2][1700/1879] lr: 2.0000e-02 eta: 19:00:47 time: 0.3814 data_time: 0.0133 memory: 6717 grad_norm: 3.3796 loss: 2.7516 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.7516 2023/04/13 20:19:44 - mmengine - INFO - Epoch(train) [2][1720/1879] lr: 2.0000e-02 eta: 19:00:25 time: 0.3566 data_time: 0.0148 memory: 6717 grad_norm: 3.3496 loss: 2.8551 top1_acc: 0.3125 top5_acc: 0.3750 loss_cls: 2.8551 2023/04/13 20:19:52 - mmengine - INFO - Epoch(train) [2][1740/1879] lr: 2.0000e-02 eta: 19:00:43 time: 0.3969 data_time: 0.0183 memory: 6717 grad_norm: 3.3413 loss: 2.8228 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.8228 2023/04/13 20:19:58 - mmengine - INFO - Epoch(train) [2][1760/1879] lr: 2.0000e-02 eta: 19:00:00 time: 0.3363 data_time: 0.0145 memory: 6717 grad_norm: 3.3693 loss: 2.5238 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5238 2023/04/13 20:20:07 - mmengine - INFO - Epoch(train) [2][1780/1879] lr: 2.0000e-02 eta: 19:00:31 time: 0.4093 data_time: 0.0128 memory: 6717 grad_norm: 3.3814 loss: 2.8677 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.8677 2023/04/13 20:20:13 - mmengine - INFO - Epoch(train) [2][1800/1879] lr: 2.0000e-02 eta: 18:59:54 time: 0.3419 data_time: 0.0135 memory: 6717 grad_norm: 3.2813 loss: 2.8128 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.8128 2023/04/13 20:20:21 - mmengine - INFO - Epoch(train) [2][1820/1879] lr: 2.0000e-02 eta: 19:00:04 time: 0.3879 data_time: 0.0131 memory: 6717 grad_norm: 3.2876 loss: 2.4073 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.4073 2023/04/13 20:20:28 - mmengine - INFO - Epoch(train) [2][1840/1879] lr: 2.0000e-02 eta: 18:59:06 time: 0.3210 data_time: 0.0130 memory: 6717 grad_norm: 3.3080 loss: 2.8687 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.8687 2023/04/13 20:20:36 - mmengine - INFO - Epoch(train) [2][1860/1879] lr: 2.0000e-02 eta: 18:59:28 time: 0.4009 data_time: 0.0126 memory: 6717 grad_norm: 3.3249 loss: 2.7036 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.7036 2023/04/13 20:20:42 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 20:20:42 - mmengine - INFO - Epoch(train) [2][1879/1879] lr: 2.0000e-02 eta: 18:58:28 time: 0.3179 data_time: 0.0118 memory: 6717 grad_norm: 3.4533 loss: 2.6716 top1_acc: 0.4286 top5_acc: 0.7143 loss_cls: 2.6716 2023/04/13 20:20:51 - mmengine - INFO - Epoch(val) [2][ 20/155] eta: 0:01:01 time: 0.4590 data_time: 0.4268 memory: 1391 2023/04/13 20:20:57 - mmengine - INFO - Epoch(val) [2][ 40/155] eta: 0:00:44 time: 0.3190 data_time: 0.2869 memory: 1391 2023/04/13 20:21:06 - mmengine - INFO - Epoch(val) [2][ 60/155] eta: 0:00:38 time: 0.4358 data_time: 0.4029 memory: 1391 2023/04/13 20:21:12 - mmengine - INFO - Epoch(val) [2][ 80/155] eta: 0:00:28 time: 0.3158 data_time: 0.2836 memory: 1391 2023/04/13 20:21:21 - mmengine - INFO - Epoch(val) [2][100/155] eta: 0:00:21 time: 0.4559 data_time: 0.4240 memory: 1391 2023/04/13 20:21:27 - mmengine - INFO - Epoch(val) [2][120/155] eta: 0:00:13 time: 0.2986 data_time: 0.2666 memory: 1391 2023/04/13 20:21:36 - mmengine - INFO - Epoch(val) [2][140/155] eta: 0:00:05 time: 0.4438 data_time: 0.4113 memory: 1391 2023/04/13 20:21:43 - mmengine - INFO - Epoch(val) [2][155/155] acc/top1: 0.4173 acc/top5: 0.6922 acc/mean1: 0.4171 data_time: 0.3577 time: 0.3894 2023/04/13 20:21:43 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_1.pth is removed 2023/04/13 20:21:43 - mmengine - INFO - The best checkpoint with 0.4173 acc/top1 at 2 epoch is saved to best_acc_top1_epoch_2.pth. 2023/04/13 20:21:53 - mmengine - INFO - Epoch(train) [3][ 20/1879] lr: 2.0000e-02 eta: 19:00:12 time: 0.4852 data_time: 0.3529 memory: 6717 grad_norm: 4.3136 loss: 2.8238 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.8238 2023/04/13 20:21:59 - mmengine - INFO - Epoch(train) [3][ 40/1879] lr: 2.0000e-02 eta: 18:58:59 time: 0.3042 data_time: 0.1720 memory: 6717 grad_norm: 3.3653 loss: 2.8314 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.8314 2023/04/13 20:22:08 - mmengine - INFO - Epoch(train) [3][ 60/1879] lr: 2.0000e-02 eta: 19:00:03 time: 0.4452 data_time: 0.3142 memory: 6717 grad_norm: 3.4254 loss: 2.8551 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.8551 2023/04/13 20:22:15 - mmengine - INFO - Epoch(train) [3][ 80/1879] lr: 2.0000e-02 eta: 18:59:05 time: 0.3183 data_time: 0.1757 memory: 6717 grad_norm: 3.4211 loss: 2.7364 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.7364 2023/04/13 20:22:23 - mmengine - INFO - Epoch(train) [3][ 100/1879] lr: 2.0000e-02 eta: 18:59:37 time: 0.4133 data_time: 0.2267 memory: 6717 grad_norm: 3.2943 loss: 2.6965 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.6965 2023/04/13 20:22:30 - mmengine - INFO - Epoch(train) [3][ 120/1879] lr: 2.0000e-02 eta: 18:59:01 time: 0.3414 data_time: 0.1756 memory: 6717 grad_norm: 3.4068 loss: 2.7004 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 2.7004 2023/04/13 20:22:38 - mmengine - INFO - Epoch(train) [3][ 140/1879] lr: 2.0000e-02 eta: 18:59:35 time: 0.4152 data_time: 0.2788 memory: 6717 grad_norm: 3.3309 loss: 2.8167 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.8167 2023/04/13 20:22:45 - mmengine - INFO - Epoch(train) [3][ 160/1879] lr: 2.0000e-02 eta: 18:59:06 time: 0.3480 data_time: 0.2146 memory: 6717 grad_norm: 3.3727 loss: 2.8100 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.8100 2023/04/13 20:22:53 - mmengine - INFO - Epoch(train) [3][ 180/1879] lr: 2.0000e-02 eta: 18:59:32 time: 0.4074 data_time: 0.2698 memory: 6717 grad_norm: 3.3344 loss: 2.4694 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 2.4694 2023/04/13 20:22:59 - mmengine - INFO - Epoch(train) [3][ 200/1879] lr: 2.0000e-02 eta: 18:58:24 time: 0.3066 data_time: 0.1713 memory: 6717 grad_norm: 3.3102 loss: 2.7796 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7796 2023/04/13 20:23:06 - mmengine - INFO - Epoch(train) [3][ 220/1879] lr: 2.0000e-02 eta: 18:58:10 time: 0.3642 data_time: 0.2322 memory: 6717 grad_norm: 3.3292 loss: 2.5464 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.5464 2023/04/13 20:23:13 - mmengine - INFO - Epoch(train) [3][ 240/1879] lr: 2.0000e-02 eta: 18:57:10 time: 0.3150 data_time: 0.1661 memory: 6717 grad_norm: 3.3351 loss: 2.5905 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.5905 2023/04/13 20:23:14 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 20:23:22 - mmengine - INFO - Epoch(train) [3][ 260/1879] lr: 2.0000e-02 eta: 18:58:07 time: 0.4413 data_time: 0.2954 memory: 6717 grad_norm: 3.4750 loss: 2.5199 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.5199 2023/04/13 20:23:28 - mmengine - INFO - Epoch(train) [3][ 280/1879] lr: 2.0000e-02 eta: 18:57:05 time: 0.3112 data_time: 0.1717 memory: 6717 grad_norm: 3.3295 loss: 2.6440 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.6440 2023/04/13 20:23:36 - mmengine - INFO - Epoch(train) [3][ 300/1879] lr: 2.0000e-02 eta: 18:57:41 time: 0.4187 data_time: 0.1601 memory: 6717 grad_norm: 3.4127 loss: 2.6057 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.6057 2023/04/13 20:23:43 - mmengine - INFO - Epoch(train) [3][ 320/1879] lr: 2.0000e-02 eta: 18:57:02 time: 0.3368 data_time: 0.0752 memory: 6717 grad_norm: 3.2646 loss: 2.6205 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.6205 2023/04/13 20:23:51 - mmengine - INFO - Epoch(train) [3][ 340/1879] lr: 2.0000e-02 eta: 18:57:29 time: 0.4094 data_time: 0.0139 memory: 6717 grad_norm: 3.3270 loss: 2.6305 top1_acc: 0.1875 top5_acc: 0.6875 loss_cls: 2.6305 2023/04/13 20:23:57 - mmengine - INFO - Epoch(train) [3][ 360/1879] lr: 2.0000e-02 eta: 18:56:32 time: 0.3154 data_time: 0.0136 memory: 6717 grad_norm: 3.4323 loss: 2.5626 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.5626 2023/04/13 20:24:06 - mmengine - INFO - Epoch(train) [3][ 380/1879] lr: 2.0000e-02 eta: 18:57:00 time: 0.4109 data_time: 0.0129 memory: 6717 grad_norm: 3.3702 loss: 2.6316 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6316 2023/04/13 20:24:13 - mmengine - INFO - Epoch(train) [3][ 400/1879] lr: 2.0000e-02 eta: 18:56:30 time: 0.3460 data_time: 0.0478 memory: 6717 grad_norm: 3.3660 loss: 2.8834 top1_acc: 0.1875 top5_acc: 0.5000 loss_cls: 2.8834 2023/04/13 20:24:20 - mmengine - INFO - Epoch(train) [3][ 420/1879] lr: 2.0000e-02 eta: 18:56:44 time: 0.3950 data_time: 0.1195 memory: 6717 grad_norm: 3.3719 loss: 2.6479 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.6479 2023/04/13 20:24:28 - mmengine - INFO - Epoch(train) [3][ 440/1879] lr: 2.0000e-02 eta: 18:56:29 time: 0.3627 data_time: 0.0149 memory: 6717 grad_norm: 3.4333 loss: 2.7327 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 2.7327 2023/04/13 20:24:35 - mmengine - INFO - Epoch(train) [3][ 460/1879] lr: 2.0000e-02 eta: 18:56:20 time: 0.3693 data_time: 0.0148 memory: 6717 grad_norm: 3.3159 loss: 2.5580 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.5580 2023/04/13 20:24:43 - mmengine - INFO - Epoch(train) [3][ 480/1879] lr: 2.0000e-02 eta: 18:56:17 time: 0.3768 data_time: 0.0129 memory: 6717 grad_norm: 3.3016 loss: 2.6926 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6926 2023/04/13 20:24:49 - mmengine - INFO - Epoch(train) [3][ 500/1879] lr: 2.0000e-02 eta: 18:55:38 time: 0.3339 data_time: 0.0148 memory: 6717 grad_norm: 3.3374 loss: 2.8042 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 2.8042 2023/04/13 20:24:57 - mmengine - INFO - Epoch(train) [3][ 520/1879] lr: 2.0000e-02 eta: 18:55:36 time: 0.3778 data_time: 0.0163 memory: 6717 grad_norm: 3.2789 loss: 2.7234 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.7234 2023/04/13 20:25:05 - mmengine - INFO - Epoch(train) [3][ 540/1879] lr: 2.0000e-02 eta: 18:55:43 time: 0.3881 data_time: 0.0179 memory: 6717 grad_norm: 3.3366 loss: 2.5904 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.5904 2023/04/13 20:25:11 - mmengine - INFO - Epoch(train) [3][ 560/1879] lr: 2.0000e-02 eta: 18:55:10 time: 0.3410 data_time: 0.0372 memory: 6717 grad_norm: 3.3365 loss: 2.6893 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.6893 2023/04/13 20:25:20 - mmengine - INFO - Epoch(train) [3][ 580/1879] lr: 2.0000e-02 eta: 18:55:30 time: 0.4039 data_time: 0.0192 memory: 6717 grad_norm: 3.3339 loss: 2.7894 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7894 2023/04/13 20:25:27 - mmengine - INFO - Epoch(train) [3][ 600/1879] lr: 2.0000e-02 eta: 18:55:22 time: 0.3695 data_time: 0.0120 memory: 6717 grad_norm: 3.2825 loss: 2.6353 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.6353 2023/04/13 20:25:36 - mmengine - INFO - Epoch(train) [3][ 620/1879] lr: 2.0000e-02 eta: 18:56:13 time: 0.4414 data_time: 0.0130 memory: 6717 grad_norm: 3.3642 loss: 2.6063 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.6063 2023/04/13 20:25:42 - mmengine - INFO - Epoch(train) [3][ 640/1879] lr: 2.0000e-02 eta: 18:54:59 time: 0.2920 data_time: 0.0125 memory: 6717 grad_norm: 3.3546 loss: 2.6911 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.6911 2023/04/13 20:25:49 - mmengine - INFO - Epoch(train) [3][ 660/1879] lr: 2.0000e-02 eta: 18:55:00 time: 0.3804 data_time: 0.0126 memory: 6717 grad_norm: 3.4264 loss: 2.4165 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.4165 2023/04/13 20:25:56 - mmengine - INFO - Epoch(train) [3][ 680/1879] lr: 2.0000e-02 eta: 18:54:24 time: 0.3372 data_time: 0.0141 memory: 6717 grad_norm: 4.1748 loss: 2.6362 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.6362 2023/04/13 20:26:03 - mmengine - INFO - Epoch(train) [3][ 700/1879] lr: 2.0000e-02 eta: 18:54:19 time: 0.3742 data_time: 0.0131 memory: 6717 grad_norm: 3.4395 loss: 2.4796 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.4796 2023/04/13 20:26:11 - mmengine - INFO - Epoch(train) [3][ 720/1879] lr: 2.0000e-02 eta: 18:54:02 time: 0.3596 data_time: 0.0129 memory: 6717 grad_norm: 3.2941 loss: 2.6927 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6927 2023/04/13 20:26:18 - mmengine - INFO - Epoch(train) [3][ 740/1879] lr: 2.0000e-02 eta: 18:54:04 time: 0.3825 data_time: 0.0150 memory: 6717 grad_norm: 3.3799 loss: 2.5085 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.5085 2023/04/13 20:26:25 - mmengine - INFO - Epoch(train) [3][ 760/1879] lr: 2.0000e-02 eta: 18:53:42 time: 0.3522 data_time: 0.0118 memory: 6717 grad_norm: 3.2791 loss: 2.6037 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.6037 2023/04/13 20:26:33 - mmengine - INFO - Epoch(train) [3][ 780/1879] lr: 2.0000e-02 eta: 18:53:51 time: 0.3914 data_time: 0.0150 memory: 6717 grad_norm: 3.3515 loss: 2.6979 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.6979 2023/04/13 20:26:39 - mmengine - INFO - Epoch(train) [3][ 800/1879] lr: 2.0000e-02 eta: 18:52:43 time: 0.2964 data_time: 0.0126 memory: 6717 grad_norm: 3.4076 loss: 2.4442 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4442 2023/04/13 20:26:47 - mmengine - INFO - Epoch(train) [3][ 820/1879] lr: 2.0000e-02 eta: 18:53:02 time: 0.4033 data_time: 0.0146 memory: 6717 grad_norm: 3.3421 loss: 2.7683 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.7683 2023/04/13 20:26:55 - mmengine - INFO - Epoch(train) [3][ 840/1879] lr: 2.0000e-02 eta: 18:52:54 time: 0.3697 data_time: 0.0118 memory: 6717 grad_norm: 3.3511 loss: 2.8128 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.8128 2023/04/13 20:27:02 - mmengine - INFO - Epoch(train) [3][ 860/1879] lr: 2.0000e-02 eta: 18:52:40 time: 0.3629 data_time: 0.0699 memory: 6717 grad_norm: 3.3815 loss: 2.3962 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.3962 2023/04/13 20:27:09 - mmengine - INFO - Epoch(train) [3][ 880/1879] lr: 2.0000e-02 eta: 18:52:29 time: 0.3660 data_time: 0.0738 memory: 6717 grad_norm: 3.3966 loss: 2.4574 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 2.4574 2023/04/13 20:27:16 - mmengine - INFO - Epoch(train) [3][ 900/1879] lr: 2.0000e-02 eta: 18:52:01 time: 0.3449 data_time: 0.1234 memory: 6717 grad_norm: 3.3554 loss: 2.4783 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4783 2023/04/13 20:27:24 - mmengine - INFO - Epoch(train) [3][ 920/1879] lr: 2.0000e-02 eta: 18:52:18 time: 0.4022 data_time: 0.0145 memory: 6717 grad_norm: 3.2736 loss: 2.5287 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.5287 2023/04/13 20:27:30 - mmengine - INFO - Epoch(train) [3][ 940/1879] lr: 2.0000e-02 eta: 18:51:33 time: 0.3220 data_time: 0.0144 memory: 6717 grad_norm: 3.3609 loss: 2.6485 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.6485 2023/04/13 20:27:38 - mmengine - INFO - Epoch(train) [3][ 960/1879] lr: 2.0000e-02 eta: 18:51:41 time: 0.3902 data_time: 0.0120 memory: 6717 grad_norm: 3.3555 loss: 2.7270 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.7270 2023/04/13 20:27:45 - mmengine - INFO - Epoch(train) [3][ 980/1879] lr: 2.0000e-02 eta: 18:51:08 time: 0.3377 data_time: 0.0410 memory: 6717 grad_norm: 3.2809 loss: 2.4148 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.4148 2023/04/13 20:27:54 - mmengine - INFO - Epoch(train) [3][1000/1879] lr: 2.0000e-02 eta: 18:51:59 time: 0.4470 data_time: 0.0115 memory: 6717 grad_norm: 3.3623 loss: 2.6045 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.6045 2023/04/13 20:28:01 - mmengine - INFO - Epoch(train) [3][1020/1879] lr: 2.0000e-02 eta: 18:51:34 time: 0.3481 data_time: 0.0140 memory: 6717 grad_norm: 3.2563 loss: 2.5390 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5390 2023/04/13 20:28:09 - mmengine - INFO - Epoch(train) [3][1040/1879] lr: 2.0000e-02 eta: 18:51:52 time: 0.4040 data_time: 0.0133 memory: 6717 grad_norm: 3.3247 loss: 2.5033 top1_acc: 0.3125 top5_acc: 0.3750 loss_cls: 2.5033 2023/04/13 20:28:16 - mmengine - INFO - Epoch(train) [3][1060/1879] lr: 2.0000e-02 eta: 18:51:15 time: 0.3318 data_time: 0.0145 memory: 6717 grad_norm: 3.4323 loss: 2.5122 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.5122 2023/04/13 20:28:24 - mmengine - INFO - Epoch(train) [3][1080/1879] lr: 2.0000e-02 eta: 18:51:29 time: 0.3987 data_time: 0.0144 memory: 6717 grad_norm: 3.3872 loss: 2.4104 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.4104 2023/04/13 20:28:30 - mmengine - INFO - Epoch(train) [3][1100/1879] lr: 2.0000e-02 eta: 18:50:32 time: 0.3051 data_time: 0.0148 memory: 6717 grad_norm: 3.3445 loss: 2.6965 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.6965 2023/04/13 20:28:38 - mmengine - INFO - Epoch(train) [3][1120/1879] lr: 2.0000e-02 eta: 18:50:56 time: 0.4124 data_time: 0.0137 memory: 6717 grad_norm: 3.3241 loss: 2.6348 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.6348 2023/04/13 20:28:45 - mmengine - INFO - Epoch(train) [3][1140/1879] lr: 2.0000e-02 eta: 18:50:17 time: 0.3283 data_time: 0.0172 memory: 6717 grad_norm: 3.3151 loss: 2.5065 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.5065 2023/04/13 20:28:53 - mmengine - INFO - Epoch(train) [3][1160/1879] lr: 2.0000e-02 eta: 18:50:45 time: 0.4187 data_time: 0.0119 memory: 6717 grad_norm: 3.5577 loss: 2.4333 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4333 2023/04/13 20:29:00 - mmengine - INFO - Epoch(train) [3][1180/1879] lr: 2.0000e-02 eta: 18:50:13 time: 0.3381 data_time: 0.0334 memory: 6717 grad_norm: 3.2614 loss: 2.5619 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.5619 2023/04/13 20:29:08 - mmengine - INFO - Epoch(train) [3][1200/1879] lr: 2.0000e-02 eta: 18:50:26 time: 0.3981 data_time: 0.0263 memory: 6717 grad_norm: 3.3100 loss: 2.7551 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.7551 2023/04/13 20:29:14 - mmengine - INFO - Epoch(train) [3][1220/1879] lr: 2.0000e-02 eta: 18:49:45 time: 0.3245 data_time: 0.0166 memory: 6717 grad_norm: 3.2932 loss: 2.5021 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.5021 2023/04/13 20:29:22 - mmengine - INFO - Epoch(train) [3][1240/1879] lr: 2.0000e-02 eta: 18:50:04 time: 0.4069 data_time: 0.0121 memory: 6717 grad_norm: 3.2866 loss: 2.6469 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.6469 2023/04/13 20:29:23 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 20:29:29 - mmengine - INFO - Epoch(train) [3][1260/1879] lr: 2.0000e-02 eta: 18:49:25 time: 0.3277 data_time: 0.0147 memory: 6717 grad_norm: 3.2067 loss: 2.5965 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.5965 2023/04/13 20:29:36 - mmengine - INFO - Epoch(train) [3][1280/1879] lr: 2.0000e-02 eta: 18:49:18 time: 0.3713 data_time: 0.0131 memory: 6717 grad_norm: 3.2672 loss: 2.5781 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.5781 2023/04/13 20:29:44 - mmengine - INFO - Epoch(train) [3][1300/1879] lr: 2.0000e-02 eta: 18:49:09 time: 0.3677 data_time: 0.0215 memory: 6717 grad_norm: 3.3716 loss: 2.5041 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.5041 2023/04/13 20:29:51 - mmengine - INFO - Epoch(train) [3][1320/1879] lr: 2.0000e-02 eta: 18:48:44 time: 0.3461 data_time: 0.0532 memory: 6717 grad_norm: 3.2855 loss: 2.7073 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.7073 2023/04/13 20:29:59 - mmengine - INFO - Epoch(train) [3][1340/1879] lr: 2.0000e-02 eta: 18:49:12 time: 0.4198 data_time: 0.0425 memory: 6717 grad_norm: 3.2416 loss: 2.6671 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6671 2023/04/13 20:30:06 - mmengine - INFO - Epoch(train) [3][1360/1879] lr: 2.0000e-02 eta: 18:48:38 time: 0.3342 data_time: 0.0351 memory: 6717 grad_norm: 3.3444 loss: 2.4137 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4137 2023/04/13 20:30:14 - mmengine - INFO - Epoch(train) [3][1380/1879] lr: 2.0000e-02 eta: 18:48:53 time: 0.4016 data_time: 0.0144 memory: 6717 grad_norm: 3.3029 loss: 2.6673 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.6673 2023/04/13 20:30:20 - mmengine - INFO - Epoch(train) [3][1400/1879] lr: 2.0000e-02 eta: 18:48:08 time: 0.3176 data_time: 0.0136 memory: 6717 grad_norm: 3.2051 loss: 2.5284 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.5284 2023/04/13 20:30:28 - mmengine - INFO - Epoch(train) [3][1420/1879] lr: 2.0000e-02 eta: 18:48:15 time: 0.3902 data_time: 0.0145 memory: 6717 grad_norm: 3.2829 loss: 2.5351 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.5351 2023/04/13 20:30:35 - mmengine - INFO - Epoch(train) [3][1440/1879] lr: 2.0000e-02 eta: 18:47:51 time: 0.3472 data_time: 0.0134 memory: 6717 grad_norm: 3.2148 loss: 2.4838 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4838 2023/04/13 20:30:43 - mmengine - INFO - Epoch(train) [3][1460/1879] lr: 2.0000e-02 eta: 18:48:11 time: 0.4096 data_time: 0.0165 memory: 6717 grad_norm: 3.2651 loss: 2.6394 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6394 2023/04/13 20:30:50 - mmengine - INFO - Epoch(train) [3][1480/1879] lr: 2.0000e-02 eta: 18:47:38 time: 0.3338 data_time: 0.0203 memory: 6717 grad_norm: 3.3430 loss: 2.7500 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7500 2023/04/13 20:30:58 - mmengine - INFO - Epoch(train) [3][1500/1879] lr: 2.0000e-02 eta: 18:47:56 time: 0.4062 data_time: 0.0777 memory: 6717 grad_norm: 3.2673 loss: 2.4749 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.4749 2023/04/13 20:31:04 - mmengine - INFO - Epoch(train) [3][1520/1879] lr: 2.0000e-02 eta: 18:47:12 time: 0.3181 data_time: 0.0259 memory: 6717 grad_norm: 3.3960 loss: 2.7230 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.7230 2023/04/13 20:31:12 - mmengine - INFO - Epoch(train) [3][1540/1879] lr: 2.0000e-02 eta: 18:47:29 time: 0.4063 data_time: 0.0154 memory: 6717 grad_norm: 3.3793 loss: 2.7311 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.7311 2023/04/13 20:31:19 - mmengine - INFO - Epoch(train) [3][1560/1879] lr: 2.0000e-02 eta: 18:47:01 time: 0.3408 data_time: 0.0127 memory: 6717 grad_norm: 3.3454 loss: 2.5590 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.5590 2023/04/13 20:31:27 - mmengine - INFO - Epoch(train) [3][1580/1879] lr: 2.0000e-02 eta: 18:47:07 time: 0.3896 data_time: 0.0137 memory: 6717 grad_norm: 3.2709 loss: 2.6983 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.6983 2023/04/13 20:31:34 - mmengine - INFO - Epoch(train) [3][1600/1879] lr: 2.0000e-02 eta: 18:46:45 time: 0.3491 data_time: 0.0139 memory: 6717 grad_norm: 3.3037 loss: 2.5521 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.5521 2023/04/13 20:31:42 - mmengine - INFO - Epoch(train) [3][1620/1879] lr: 2.0000e-02 eta: 18:46:59 time: 0.4016 data_time: 0.0139 memory: 6717 grad_norm: 3.3391 loss: 2.7577 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.7577 2023/04/13 20:31:49 - mmengine - INFO - Epoch(train) [3][1640/1879] lr: 2.0000e-02 eta: 18:46:57 time: 0.3785 data_time: 0.0140 memory: 6717 grad_norm: 3.2863 loss: 2.7059 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7059 2023/04/13 20:31:57 - mmengine - INFO - Epoch(train) [3][1660/1879] lr: 2.0000e-02 eta: 18:47:04 time: 0.3923 data_time: 0.0136 memory: 6717 grad_norm: 3.2346 loss: 2.4214 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4214 2023/04/13 20:32:04 - mmengine - INFO - Epoch(train) [3][1680/1879] lr: 2.0000e-02 eta: 18:46:21 time: 0.3165 data_time: 0.0132 memory: 6717 grad_norm: 3.2477 loss: 2.4869 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.4869 2023/04/13 20:32:12 - mmengine - INFO - Epoch(train) [3][1700/1879] lr: 2.0000e-02 eta: 18:46:36 time: 0.4047 data_time: 0.0135 memory: 6717 grad_norm: 3.1992 loss: 2.6438 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.6438 2023/04/13 20:32:19 - mmengine - INFO - Epoch(train) [3][1720/1879] lr: 2.0000e-02 eta: 18:46:12 time: 0.3460 data_time: 0.0127 memory: 6717 grad_norm: 3.2996 loss: 2.6452 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6452 2023/04/13 20:32:27 - mmengine - INFO - Epoch(train) [3][1740/1879] lr: 2.0000e-02 eta: 18:46:24 time: 0.3995 data_time: 0.0161 memory: 6717 grad_norm: 3.3206 loss: 2.5491 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.5491 2023/04/13 20:32:34 - mmengine - INFO - Epoch(train) [3][1760/1879] lr: 2.0000e-02 eta: 18:46:20 time: 0.3753 data_time: 0.0115 memory: 6717 grad_norm: 3.2816 loss: 2.6807 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.6807 2023/04/13 20:32:42 - mmengine - INFO - Epoch(train) [3][1780/1879] lr: 2.0000e-02 eta: 18:46:44 time: 0.4181 data_time: 0.0148 memory: 6717 grad_norm: 3.2304 loss: 2.6557 top1_acc: 0.1875 top5_acc: 0.6875 loss_cls: 2.6557 2023/04/13 20:32:49 - mmengine - INFO - Epoch(train) [3][1800/1879] lr: 2.0000e-02 eta: 18:46:14 time: 0.3361 data_time: 0.0131 memory: 6717 grad_norm: 3.1918 loss: 2.5413 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.5413 2023/04/13 20:32:57 - mmengine - INFO - Epoch(train) [3][1820/1879] lr: 2.0000e-02 eta: 18:46:18 time: 0.3883 data_time: 0.0146 memory: 6717 grad_norm: 3.2611 loss: 2.5720 top1_acc: 0.1250 top5_acc: 0.5625 loss_cls: 2.5720 2023/04/13 20:33:03 - mmengine - INFO - Epoch(train) [3][1840/1879] lr: 2.0000e-02 eta: 18:45:37 time: 0.3198 data_time: 0.0131 memory: 6717 grad_norm: 3.3147 loss: 2.6918 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.6918 2023/04/13 20:33:12 - mmengine - INFO - Epoch(train) [3][1860/1879] lr: 2.0000e-02 eta: 18:45:58 time: 0.4133 data_time: 0.0173 memory: 6717 grad_norm: 3.2223 loss: 2.6959 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6959 2023/04/13 20:33:17 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 20:33:17 - mmengine - INFO - Epoch(train) [3][1879/1879] lr: 2.0000e-02 eta: 18:45:00 time: 0.2803 data_time: 0.0119 memory: 6717 grad_norm: 3.6248 loss: 2.8491 top1_acc: 0.1429 top5_acc: 0.5714 loss_cls: 2.8491 2023/04/13 20:33:17 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/04/13 20:33:27 - mmengine - INFO - Epoch(val) [3][ 20/155] eta: 0:01:02 time: 0.4665 data_time: 0.4334 memory: 1391 2023/04/13 20:33:33 - mmengine - INFO - Epoch(val) [3][ 40/155] eta: 0:00:44 time: 0.3120 data_time: 0.2797 memory: 1391 2023/04/13 20:33:42 - mmengine - INFO - Epoch(val) [3][ 60/155] eta: 0:00:38 time: 0.4291 data_time: 0.3965 memory: 1391 2023/04/13 20:33:48 - mmengine - INFO - Epoch(val) [3][ 80/155] eta: 0:00:28 time: 0.3123 data_time: 0.2794 memory: 1391 2023/04/13 20:33:57 - mmengine - INFO - Epoch(val) [3][100/155] eta: 0:00:21 time: 0.4529 data_time: 0.4205 memory: 1391 2023/04/13 20:34:03 - mmengine - INFO - Epoch(val) [3][120/155] eta: 0:00:13 time: 0.2987 data_time: 0.2655 memory: 1391 2023/04/13 20:34:12 - mmengine - INFO - Epoch(val) [3][140/155] eta: 0:00:05 time: 0.4418 data_time: 0.4088 memory: 1391 2023/04/13 20:34:19 - mmengine - INFO - Epoch(val) [3][155/155] acc/top1: 0.4382 acc/top5: 0.7087 acc/mean1: 0.4381 data_time: 0.3771 time: 0.4093 2023/04/13 20:34:19 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_2.pth is removed 2023/04/13 20:34:19 - mmengine - INFO - The best checkpoint with 0.4382 acc/top1 at 3 epoch is saved to best_acc_top1_epoch_3.pth. 2023/04/13 20:34:29 - mmengine - INFO - Epoch(train) [4][ 20/1879] lr: 2.0000e-02 eta: 18:46:01 time: 0.4767 data_time: 0.2939 memory: 6717 grad_norm: 3.2606 loss: 2.7206 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.7206 2023/04/13 20:34:35 - mmengine - INFO - Epoch(train) [4][ 40/1879] lr: 2.0000e-02 eta: 18:45:32 time: 0.3372 data_time: 0.0866 memory: 6717 grad_norm: 3.1934 loss: 2.5740 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.5740 2023/04/13 20:34:44 - mmengine - INFO - Epoch(train) [4][ 60/1879] lr: 2.0000e-02 eta: 18:45:53 time: 0.4147 data_time: 0.1499 memory: 6717 grad_norm: 3.2696 loss: 2.4492 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.4492 2023/04/13 20:34:50 - mmengine - INFO - Epoch(train) [4][ 80/1879] lr: 2.0000e-02 eta: 18:45:17 time: 0.3261 data_time: 0.1010 memory: 6717 grad_norm: 3.3100 loss: 2.6264 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.6264 2023/04/13 20:34:58 - mmengine - INFO - Epoch(train) [4][ 100/1879] lr: 2.0000e-02 eta: 18:45:37 time: 0.4137 data_time: 0.1372 memory: 6717 grad_norm: 3.2867 loss: 2.5194 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.5194 2023/04/13 20:35:05 - mmengine - INFO - Epoch(train) [4][ 120/1879] lr: 2.0000e-02 eta: 18:44:51 time: 0.3092 data_time: 0.1336 memory: 6717 grad_norm: 3.3588 loss: 2.5305 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.5305 2023/04/13 20:35:13 - mmengine - INFO - Epoch(train) [4][ 140/1879] lr: 2.0000e-02 eta: 18:45:05 time: 0.4055 data_time: 0.1591 memory: 6717 grad_norm: 3.3105 loss: 2.5074 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.5074 2023/04/13 20:35:19 - mmengine - INFO - Epoch(train) [4][ 160/1879] lr: 2.0000e-02 eta: 18:44:36 time: 0.3360 data_time: 0.1161 memory: 6717 grad_norm: 3.2848 loss: 2.4759 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.4759 2023/04/13 20:35:27 - mmengine - INFO - Epoch(train) [4][ 180/1879] lr: 2.0000e-02 eta: 18:44:41 time: 0.3910 data_time: 0.1793 memory: 6717 grad_norm: 3.3206 loss: 2.4852 top1_acc: 0.1875 top5_acc: 0.4375 loss_cls: 2.4852 2023/04/13 20:35:34 - mmengine - INFO - Epoch(train) [4][ 200/1879] lr: 2.0000e-02 eta: 18:44:08 time: 0.3294 data_time: 0.0856 memory: 6717 grad_norm: 3.2316 loss: 2.7071 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.7071 2023/04/13 20:35:42 - mmengine - INFO - Epoch(train) [4][ 220/1879] lr: 2.0000e-02 eta: 18:44:29 time: 0.4164 data_time: 0.1186 memory: 6717 grad_norm: 3.2684 loss: 2.2708 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2708 2023/04/13 20:35:49 - mmengine - INFO - Epoch(train) [4][ 240/1879] lr: 2.0000e-02 eta: 18:44:03 time: 0.3392 data_time: 0.0637 memory: 6717 grad_norm: 3.3118 loss: 2.4870 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4870 2023/04/13 20:35:57 - mmengine - INFO - Epoch(train) [4][ 260/1879] lr: 2.0000e-02 eta: 18:44:15 time: 0.4025 data_time: 0.0864 memory: 6717 grad_norm: 3.2390 loss: 2.5756 top1_acc: 0.5625 top5_acc: 0.5625 loss_cls: 2.5756 2023/04/13 20:36:03 - mmengine - INFO - Epoch(train) [4][ 280/1879] lr: 2.0000e-02 eta: 18:43:34 time: 0.3155 data_time: 0.0618 memory: 6717 grad_norm: 3.5770 loss: 2.3662 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3662 2023/04/13 20:36:12 - mmengine - INFO - Epoch(train) [4][ 300/1879] lr: 2.0000e-02 eta: 18:43:59 time: 0.4238 data_time: 0.1216 memory: 6717 grad_norm: 3.2106 loss: 2.4220 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.4220 2023/04/13 20:36:19 - mmengine - INFO - Epoch(train) [4][ 320/1879] lr: 2.0000e-02 eta: 18:43:38 time: 0.3479 data_time: 0.1263 memory: 6717 grad_norm: 3.2796 loss: 2.5161 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5161 2023/04/13 20:36:27 - mmengine - INFO - Epoch(train) [4][ 340/1879] lr: 2.0000e-02 eta: 18:43:44 time: 0.3926 data_time: 0.1864 memory: 6717 grad_norm: 3.1337 loss: 2.3348 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3348 2023/04/13 20:36:44 - mmengine - INFO - Epoch(train) [4][ 360/1879] lr: 2.0000e-02 eta: 18:48:38 time: 0.8674 data_time: 0.2311 memory: 6717 grad_norm: 3.2105 loss: 2.5355 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.5355 2023/04/13 20:36:44 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 20:36:50 - mmengine - INFO - Epoch(train) [4][ 380/1879] lr: 2.0000e-02 eta: 18:47:48 time: 0.3031 data_time: 0.0104 memory: 6717 grad_norm: 3.2196 loss: 2.5876 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5876 2023/04/13 20:36:57 - mmengine - INFO - Epoch(train) [4][ 400/1879] lr: 2.0000e-02 eta: 18:47:40 time: 0.3698 data_time: 0.0138 memory: 6717 grad_norm: 3.1937 loss: 2.5424 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.5424 2023/04/13 20:37:03 - mmengine - INFO - Epoch(train) [4][ 420/1879] lr: 2.0000e-02 eta: 18:46:43 time: 0.2900 data_time: 0.0136 memory: 6717 grad_norm: 3.1890 loss: 2.3817 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3817 2023/04/13 20:37:12 - mmengine - INFO - Epoch(train) [4][ 440/1879] lr: 2.0000e-02 eta: 18:47:09 time: 0.4279 data_time: 0.0683 memory: 6717 grad_norm: 3.3451 loss: 2.5798 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.5798 2023/04/13 20:37:19 - mmengine - INFO - Epoch(train) [4][ 460/1879] lr: 2.0000e-02 eta: 18:46:43 time: 0.3418 data_time: 0.1363 memory: 6717 grad_norm: 3.1973 loss: 2.3597 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3597 2023/04/13 20:37:27 - mmengine - INFO - Epoch(train) [4][ 480/1879] lr: 2.0000e-02 eta: 18:46:50 time: 0.3947 data_time: 0.1999 memory: 6717 grad_norm: 3.2980 loss: 2.6117 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.6117 2023/04/13 20:37:33 - mmengine - INFO - Epoch(train) [4][ 500/1879] lr: 2.0000e-02 eta: 18:46:17 time: 0.3289 data_time: 0.0468 memory: 6717 grad_norm: 3.2714 loss: 2.4817 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.4817 2023/04/13 20:37:41 - mmengine - INFO - Epoch(train) [4][ 520/1879] lr: 2.0000e-02 eta: 18:46:21 time: 0.3921 data_time: 0.0149 memory: 6717 grad_norm: 3.2815 loss: 2.4642 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4642 2023/04/13 20:37:48 - mmengine - INFO - Epoch(train) [4][ 540/1879] lr: 2.0000e-02 eta: 18:46:10 time: 0.3653 data_time: 0.0128 memory: 6717 grad_norm: 3.3058 loss: 2.6992 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.6992 2023/04/13 20:37:56 - mmengine - INFO - Epoch(train) [4][ 560/1879] lr: 2.0000e-02 eta: 18:46:05 time: 0.3767 data_time: 0.0155 memory: 6717 grad_norm: 3.3175 loss: 2.5573 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.5573 2023/04/13 20:38:03 - mmengine - INFO - Epoch(train) [4][ 580/1879] lr: 2.0000e-02 eta: 18:45:52 time: 0.3613 data_time: 0.0132 memory: 6717 grad_norm: 3.1876 loss: 2.5210 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5210 2023/04/13 20:38:11 - mmengine - INFO - Epoch(train) [4][ 600/1879] lr: 2.0000e-02 eta: 18:45:56 time: 0.3909 data_time: 0.0207 memory: 6717 grad_norm: 3.2827 loss: 2.5111 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.5111 2023/04/13 20:38:18 - mmengine - INFO - Epoch(train) [4][ 620/1879] lr: 2.0000e-02 eta: 18:45:37 time: 0.3531 data_time: 0.0127 memory: 6717 grad_norm: 3.2666 loss: 2.4781 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.4781 2023/04/13 20:38:26 - mmengine - INFO - Epoch(train) [4][ 640/1879] lr: 2.0000e-02 eta: 18:45:57 time: 0.4184 data_time: 0.0158 memory: 6717 grad_norm: 3.2630 loss: 2.9295 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.9295 2023/04/13 20:38:33 - mmengine - INFO - Epoch(train) [4][ 660/1879] lr: 2.0000e-02 eta: 18:45:28 time: 0.3345 data_time: 0.0115 memory: 6717 grad_norm: 3.2916 loss: 2.3943 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3943 2023/04/13 20:38:41 - mmengine - INFO - Epoch(train) [4][ 680/1879] lr: 2.0000e-02 eta: 18:45:43 time: 0.4122 data_time: 0.0183 memory: 6717 grad_norm: 3.2689 loss: 2.4205 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.4205 2023/04/13 20:38:48 - mmengine - INFO - Epoch(train) [4][ 700/1879] lr: 2.0000e-02 eta: 18:45:11 time: 0.3281 data_time: 0.0128 memory: 6717 grad_norm: 3.3677 loss: 2.4860 top1_acc: 0.3125 top5_acc: 0.8750 loss_cls: 2.4860 2023/04/13 20:38:56 - mmengine - INFO - Epoch(train) [4][ 720/1879] lr: 2.0000e-02 eta: 18:45:28 time: 0.4149 data_time: 0.0142 memory: 6717 grad_norm: 3.2492 loss: 2.5704 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.5704 2023/04/13 20:39:02 - mmengine - INFO - Epoch(train) [4][ 740/1879] lr: 2.0000e-02 eta: 18:44:49 time: 0.3162 data_time: 0.0122 memory: 6717 grad_norm: 3.2521 loss: 2.3724 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3724 2023/04/13 20:39:10 - mmengine - INFO - Epoch(train) [4][ 760/1879] lr: 2.0000e-02 eta: 18:44:51 time: 0.3882 data_time: 0.0143 memory: 6717 grad_norm: 3.2357 loss: 2.7466 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.7466 2023/04/13 20:39:17 - mmengine - INFO - Epoch(train) [4][ 780/1879] lr: 2.0000e-02 eta: 18:44:14 time: 0.3193 data_time: 0.0130 memory: 6717 grad_norm: 3.2030 loss: 2.3438 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3438 2023/04/13 20:39:25 - mmengine - INFO - Epoch(train) [4][ 800/1879] lr: 2.0000e-02 eta: 18:44:39 time: 0.4296 data_time: 0.0136 memory: 6717 grad_norm: 3.2380 loss: 2.5516 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.5516 2023/04/13 20:39:32 - mmengine - INFO - Epoch(train) [4][ 820/1879] lr: 2.0000e-02 eta: 18:44:08 time: 0.3297 data_time: 0.0134 memory: 6717 grad_norm: 3.2476 loss: 2.7756 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.7756 2023/04/13 20:39:40 - mmengine - INFO - Epoch(train) [4][ 840/1879] lr: 2.0000e-02 eta: 18:44:28 time: 0.4213 data_time: 0.0138 memory: 6717 grad_norm: 3.2336 loss: 2.4905 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.4905 2023/04/13 20:39:47 - mmengine - INFO - Epoch(train) [4][ 860/1879] lr: 2.0000e-02 eta: 18:43:52 time: 0.3201 data_time: 0.0142 memory: 6717 grad_norm: 3.3228 loss: 2.5767 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.5767 2023/04/13 20:39:55 - mmengine - INFO - Epoch(train) [4][ 880/1879] lr: 2.0000e-02 eta: 18:44:08 time: 0.4152 data_time: 0.0125 memory: 6717 grad_norm: 3.9610 loss: 2.8219 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 2.8219 2023/04/13 20:40:01 - mmengine - INFO - Epoch(train) [4][ 900/1879] lr: 2.0000e-02 eta: 18:43:16 time: 0.2904 data_time: 0.0141 memory: 6717 grad_norm: 3.5480 loss: 2.4608 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4608 2023/04/13 20:40:09 - mmengine - INFO - Epoch(train) [4][ 920/1879] lr: 2.0000e-02 eta: 18:43:20 time: 0.3917 data_time: 0.0125 memory: 6717 grad_norm: 3.3727 loss: 2.4430 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.4430 2023/04/13 20:40:16 - mmengine - INFO - Epoch(train) [4][ 940/1879] lr: 2.0000e-02 eta: 18:43:00 time: 0.3494 data_time: 0.0152 memory: 6717 grad_norm: 3.2550 loss: 2.3508 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3508 2023/04/13 20:40:24 - mmengine - INFO - Epoch(train) [4][ 960/1879] lr: 2.0000e-02 eta: 18:43:14 time: 0.4104 data_time: 0.0235 memory: 6717 grad_norm: 3.3368 loss: 2.5538 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5538 2023/04/13 20:40:30 - mmengine - INFO - Epoch(train) [4][ 980/1879] lr: 2.0000e-02 eta: 18:42:46 time: 0.3356 data_time: 0.0287 memory: 6717 grad_norm: 3.3824 loss: 2.5914 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5914 2023/04/13 20:40:39 - mmengine - INFO - Epoch(train) [4][1000/1879] lr: 2.0000e-02 eta: 18:42:58 time: 0.4066 data_time: 0.0121 memory: 6717 grad_norm: 3.1859 loss: 2.4560 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 2.4560 2023/04/13 20:40:45 - mmengine - INFO - Epoch(train) [4][1020/1879] lr: 2.0000e-02 eta: 18:42:27 time: 0.3275 data_time: 0.0182 memory: 6717 grad_norm: 3.3160 loss: 2.3926 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3926 2023/04/13 20:40:53 - mmengine - INFO - Epoch(train) [4][1040/1879] lr: 2.0000e-02 eta: 18:42:41 time: 0.4125 data_time: 0.0382 memory: 6717 grad_norm: 3.2560 loss: 2.6517 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.6517 2023/04/13 20:41:00 - mmengine - INFO - Epoch(train) [4][1060/1879] lr: 2.0000e-02 eta: 18:42:19 time: 0.3440 data_time: 0.0325 memory: 6717 grad_norm: 3.6146 loss: 2.5123 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.5123 2023/04/13 20:41:08 - mmengine - INFO - Epoch(train) [4][1080/1879] lr: 2.0000e-02 eta: 18:42:24 time: 0.3940 data_time: 0.0310 memory: 6717 grad_norm: 3.2688 loss: 2.4206 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4206 2023/04/13 20:41:15 - mmengine - INFO - Epoch(train) [4][1100/1879] lr: 2.0000e-02 eta: 18:42:11 time: 0.3613 data_time: 0.1670 memory: 6717 grad_norm: 3.2415 loss: 2.6115 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.6115 2023/04/13 20:41:23 - mmengine - INFO - Epoch(train) [4][1120/1879] lr: 2.0000e-02 eta: 18:42:00 time: 0.3649 data_time: 0.1873 memory: 6717 grad_norm: 3.2468 loss: 2.5456 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.5456 2023/04/13 20:41:30 - mmengine - INFO - Epoch(train) [4][1140/1879] lr: 2.0000e-02 eta: 18:41:54 time: 0.3746 data_time: 0.1827 memory: 6717 grad_norm: 3.2742 loss: 2.5451 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.5451 2023/04/13 20:41:37 - mmengine - INFO - Epoch(train) [4][1160/1879] lr: 2.0000e-02 eta: 18:41:34 time: 0.3492 data_time: 0.1175 memory: 6717 grad_norm: 3.2380 loss: 2.5813 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.5813 2023/04/13 20:41:44 - mmengine - INFO - Epoch(train) [4][1180/1879] lr: 2.0000e-02 eta: 18:41:15 time: 0.3483 data_time: 0.1338 memory: 6717 grad_norm: 3.1903 loss: 2.5235 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.5235 2023/04/13 20:41:51 - mmengine - INFO - Epoch(train) [4][1200/1879] lr: 2.0000e-02 eta: 18:40:56 time: 0.3513 data_time: 0.1764 memory: 6717 grad_norm: 3.2476 loss: 2.3824 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3824 2023/04/13 20:41:59 - mmengine - INFO - Epoch(train) [4][1220/1879] lr: 2.0000e-02 eta: 18:40:47 time: 0.3684 data_time: 0.1565 memory: 6717 grad_norm: 3.1974 loss: 2.3523 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.3523 2023/04/13 20:42:06 - mmengine - INFO - Epoch(train) [4][1240/1879] lr: 2.0000e-02 eta: 18:40:39 time: 0.3705 data_time: 0.0540 memory: 6717 grad_norm: 3.2131 loss: 2.4257 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.4257 2023/04/13 20:42:13 - mmengine - INFO - Epoch(train) [4][1260/1879] lr: 2.0000e-02 eta: 18:40:19 time: 0.3465 data_time: 0.0562 memory: 6717 grad_norm: 3.2450 loss: 2.2927 top1_acc: 0.5625 top5_acc: 0.5625 loss_cls: 2.2927 2023/04/13 20:42:21 - mmengine - INFO - Epoch(train) [4][1280/1879] lr: 2.0000e-02 eta: 18:40:31 time: 0.4078 data_time: 0.0713 memory: 6717 grad_norm: 3.2050 loss: 2.5746 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.5746 2023/04/13 20:42:28 - mmengine - INFO - Epoch(train) [4][1300/1879] lr: 2.0000e-02 eta: 18:40:14 time: 0.3544 data_time: 0.1125 memory: 6717 grad_norm: 3.2458 loss: 2.3815 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3815 2023/04/13 20:42:35 - mmengine - INFO - Epoch(train) [4][1320/1879] lr: 2.0000e-02 eta: 18:39:52 time: 0.3427 data_time: 0.1192 memory: 6717 grad_norm: 3.2004 loss: 2.3471 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3471 2023/04/13 20:42:43 - mmengine - INFO - Epoch(train) [4][1340/1879] lr: 2.0000e-02 eta: 18:40:00 time: 0.4022 data_time: 0.2336 memory: 6717 grad_norm: 3.2705 loss: 2.4094 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.4094 2023/04/13 20:42:50 - mmengine - INFO - Epoch(train) [4][1360/1879] lr: 2.0000e-02 eta: 18:39:34 time: 0.3350 data_time: 0.1131 memory: 6717 grad_norm: 3.2483 loss: 2.7010 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7010 2023/04/13 20:42:50 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 20:42:58 - mmengine - INFO - Epoch(train) [4][1380/1879] lr: 2.0000e-02 eta: 18:39:44 time: 0.4042 data_time: 0.1960 memory: 6717 grad_norm: 3.2489 loss: 2.3785 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3785 2023/04/13 20:43:04 - mmengine - INFO - Epoch(train) [4][1400/1879] lr: 2.0000e-02 eta: 18:39:17 time: 0.3334 data_time: 0.1476 memory: 6717 grad_norm: 3.2953 loss: 2.3943 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.3943 2023/04/13 20:43:12 - mmengine - INFO - Epoch(train) [4][1420/1879] lr: 2.0000e-02 eta: 18:39:18 time: 0.3891 data_time: 0.1047 memory: 6717 grad_norm: 3.1844 loss: 2.3794 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3794 2023/04/13 20:43:19 - mmengine - INFO - Epoch(train) [4][1440/1879] lr: 2.0000e-02 eta: 18:39:05 time: 0.3604 data_time: 0.0264 memory: 6717 grad_norm: 3.2745 loss: 2.3038 top1_acc: 0.1875 top5_acc: 0.8125 loss_cls: 2.3038 2023/04/13 20:43:27 - mmengine - INFO - Epoch(train) [4][1460/1879] lr: 2.0000e-02 eta: 18:39:04 time: 0.3840 data_time: 0.0153 memory: 6717 grad_norm: 3.3217 loss: 2.4724 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.4724 2023/04/13 20:43:34 - mmengine - INFO - Epoch(train) [4][1480/1879] lr: 2.0000e-02 eta: 18:38:47 time: 0.3526 data_time: 0.0119 memory: 6717 grad_norm: 3.2121 loss: 2.4036 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.4036 2023/04/13 20:43:41 - mmengine - INFO - Epoch(train) [4][1500/1879] lr: 2.0000e-02 eta: 18:38:26 time: 0.3431 data_time: 0.0154 memory: 6717 grad_norm: 3.2086 loss: 2.3202 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.3202 2023/04/13 20:43:49 - mmengine - INFO - Epoch(train) [4][1520/1879] lr: 2.0000e-02 eta: 18:38:37 time: 0.4082 data_time: 0.0119 memory: 6717 grad_norm: 3.1662 loss: 2.1793 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1793 2023/04/13 20:43:56 - mmengine - INFO - Epoch(train) [4][1540/1879] lr: 2.0000e-02 eta: 18:38:06 time: 0.3239 data_time: 0.0139 memory: 6717 grad_norm: 3.1671 loss: 2.4473 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4473 2023/04/13 20:44:03 - mmengine - INFO - Epoch(train) [4][1560/1879] lr: 2.0000e-02 eta: 18:38:00 time: 0.3751 data_time: 0.0152 memory: 6717 grad_norm: 3.2470 loss: 2.4037 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.4037 2023/04/13 20:44:11 - mmengine - INFO - Epoch(train) [4][1580/1879] lr: 2.0000e-02 eta: 18:37:55 time: 0.3758 data_time: 0.0155 memory: 6717 grad_norm: 3.3376 loss: 2.3784 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3784 2023/04/13 20:44:18 - mmengine - INFO - Epoch(train) [4][1600/1879] lr: 2.0000e-02 eta: 18:37:45 time: 0.3667 data_time: 0.0127 memory: 6717 grad_norm: 3.2002 loss: 2.4624 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.4624 2023/04/13 20:44:25 - mmengine - INFO - Epoch(train) [4][1620/1879] lr: 2.0000e-02 eta: 18:37:33 time: 0.3614 data_time: 0.0136 memory: 6717 grad_norm: 3.2005 loss: 2.8477 top1_acc: 0.3125 top5_acc: 0.4375 loss_cls: 2.8477 2023/04/13 20:44:32 - mmengine - INFO - Epoch(train) [4][1640/1879] lr: 2.0000e-02 eta: 18:37:12 time: 0.3442 data_time: 0.0139 memory: 6717 grad_norm: 3.1432 loss: 2.4467 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.4467 2023/04/13 20:44:40 - mmengine - INFO - Epoch(train) [4][1660/1879] lr: 2.0000e-02 eta: 18:37:19 time: 0.3998 data_time: 0.0144 memory: 6717 grad_norm: 3.2451 loss: 2.4317 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4317 2023/04/13 20:44:47 - mmengine - INFO - Epoch(train) [4][1680/1879] lr: 2.0000e-02 eta: 18:37:00 time: 0.3483 data_time: 0.0119 memory: 6717 grad_norm: 3.1923 loss: 2.3459 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.3459 2023/04/13 20:44:54 - mmengine - INFO - Epoch(train) [4][1700/1879] lr: 2.0000e-02 eta: 18:36:50 time: 0.3650 data_time: 0.0148 memory: 6717 grad_norm: 3.1730 loss: 2.3797 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3797 2023/04/13 20:45:02 - mmengine - INFO - Epoch(train) [4][1720/1879] lr: 2.0000e-02 eta: 18:36:41 time: 0.3690 data_time: 0.0116 memory: 6717 grad_norm: 3.1520 loss: 2.2832 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.2832 2023/04/13 20:45:10 - mmengine - INFO - Epoch(train) [4][1740/1879] lr: 2.0000e-02 eta: 18:36:47 time: 0.3978 data_time: 0.0127 memory: 6717 grad_norm: 3.2805 loss: 2.2535 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2535 2023/04/13 20:45:16 - mmengine - INFO - Epoch(train) [4][1760/1879] lr: 2.0000e-02 eta: 18:36:15 time: 0.3205 data_time: 0.0143 memory: 6717 grad_norm: 3.1863 loss: 2.4035 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 2.4035 2023/04/13 20:45:25 - mmengine - INFO - Epoch(train) [4][1780/1879] lr: 2.0000e-02 eta: 18:36:34 time: 0.4256 data_time: 0.0146 memory: 6717 grad_norm: 3.2118 loss: 2.1775 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.1775 2023/04/13 20:45:31 - mmengine - INFO - Epoch(train) [4][1800/1879] lr: 2.0000e-02 eta: 18:35:59 time: 0.3145 data_time: 0.0133 memory: 6717 grad_norm: 3.2640 loss: 2.3847 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.3847 2023/04/13 20:45:39 - mmengine - INFO - Epoch(train) [4][1820/1879] lr: 2.0000e-02 eta: 18:36:18 time: 0.4259 data_time: 0.0128 memory: 6717 grad_norm: 3.1556 loss: 2.4837 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.4837 2023/04/13 20:45:47 - mmengine - INFO - Epoch(train) [4][1840/1879] lr: 2.0000e-02 eta: 18:36:09 time: 0.3677 data_time: 0.0140 memory: 6717 grad_norm: 4.1546 loss: 2.4637 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.4637 2023/04/13 20:45:55 - mmengine - INFO - Epoch(train) [4][1860/1879] lr: 2.0000e-02 eta: 18:36:24 time: 0.4183 data_time: 0.0149 memory: 6717 grad_norm: 3.3499 loss: 2.6656 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.6656 2023/04/13 20:46:01 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 20:46:01 - mmengine - INFO - Epoch(train) [4][1879/1879] lr: 2.0000e-02 eta: 18:35:47 time: 0.2965 data_time: 0.0123 memory: 6717 grad_norm: 3.2565 loss: 2.6342 top1_acc: 0.0000 top5_acc: 0.5714 loss_cls: 2.6342 2023/04/13 20:46:10 - mmengine - INFO - Epoch(val) [4][ 20/155] eta: 0:01:01 time: 0.4589 data_time: 0.4265 memory: 1391 2023/04/13 20:46:17 - mmengine - INFO - Epoch(val) [4][ 40/155] eta: 0:00:44 time: 0.3217 data_time: 0.2883 memory: 1391 2023/04/13 20:46:25 - mmengine - INFO - Epoch(val) [4][ 60/155] eta: 0:00:38 time: 0.4203 data_time: 0.3873 memory: 1391 2023/04/13 20:46:31 - mmengine - INFO - Epoch(val) [4][ 80/155] eta: 0:00:28 time: 0.3134 data_time: 0.2810 memory: 1391 2023/04/13 20:46:40 - mmengine - INFO - Epoch(val) [4][100/155] eta: 0:00:21 time: 0.4357 data_time: 0.4026 memory: 1391 2023/04/13 20:46:47 - mmengine - INFO - Epoch(val) [4][120/155] eta: 0:00:13 time: 0.3280 data_time: 0.2954 memory: 1391 2023/04/13 20:46:56 - mmengine - INFO - Epoch(val) [4][140/155] eta: 0:00:05 time: 0.4828 data_time: 0.4505 memory: 1391 2023/04/13 20:47:03 - mmengine - INFO - Epoch(val) [4][155/155] acc/top1: 0.4588 acc/top5: 0.7252 acc/mean1: 0.4588 data_time: 0.4200 time: 0.4519 2023/04/13 20:47:03 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_3.pth is removed 2023/04/13 20:47:04 - mmengine - INFO - The best checkpoint with 0.4588 acc/top1 at 4 epoch is saved to best_acc_top1_epoch_4.pth. 2023/04/13 20:47:14 - mmengine - INFO - Epoch(train) [5][ 20/1879] lr: 2.0000e-02 eta: 18:36:47 time: 0.5127 data_time: 0.3297 memory: 6717 grad_norm: 3.1691 loss: 2.3715 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.3715 2023/04/13 20:47:20 - mmengine - INFO - Epoch(train) [5][ 40/1879] lr: 2.0000e-02 eta: 18:36:06 time: 0.3009 data_time: 0.1050 memory: 6717 grad_norm: 3.2106 loss: 2.4586 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.4586 2023/04/13 20:47:29 - mmengine - INFO - Epoch(train) [5][ 60/1879] lr: 2.0000e-02 eta: 18:36:24 time: 0.4247 data_time: 0.1487 memory: 6717 grad_norm: 3.2318 loss: 2.3414 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.3414 2023/04/13 20:47:35 - mmengine - INFO - Epoch(train) [5][ 80/1879] lr: 2.0000e-02 eta: 18:35:51 time: 0.3181 data_time: 0.0829 memory: 6717 grad_norm: 3.2648 loss: 2.5233 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5233 2023/04/13 20:47:43 - mmengine - INFO - Epoch(train) [5][ 100/1879] lr: 2.0000e-02 eta: 18:36:02 time: 0.4105 data_time: 0.1198 memory: 6717 grad_norm: 3.2830 loss: 2.4845 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.4845 2023/04/13 20:47:50 - mmengine - INFO - Epoch(train) [5][ 120/1879] lr: 2.0000e-02 eta: 18:35:41 time: 0.3417 data_time: 0.0308 memory: 6717 grad_norm: 3.3472 loss: 2.4105 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 2.4105 2023/04/13 20:47:58 - mmengine - INFO - Epoch(train) [5][ 140/1879] lr: 2.0000e-02 eta: 18:35:53 time: 0.4124 data_time: 0.0367 memory: 6717 grad_norm: 3.2494 loss: 2.3815 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 2.3815 2023/04/13 20:48:05 - mmengine - INFO - Epoch(train) [5][ 160/1879] lr: 2.0000e-02 eta: 18:35:19 time: 0.3157 data_time: 0.0748 memory: 6717 grad_norm: 3.2160 loss: 2.2810 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.2810 2023/04/13 20:48:14 - mmengine - INFO - Epoch(train) [5][ 180/1879] lr: 2.0000e-02 eta: 18:35:58 time: 0.4695 data_time: 0.0887 memory: 6717 grad_norm: 3.2492 loss: 2.4182 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.4182 2023/04/13 20:48:21 - mmengine - INFO - Epoch(train) [5][ 200/1879] lr: 2.0000e-02 eta: 18:35:34 time: 0.3370 data_time: 0.0122 memory: 6717 grad_norm: 3.2160 loss: 2.4732 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4732 2023/04/13 20:48:29 - mmengine - INFO - Epoch(train) [5][ 220/1879] lr: 2.0000e-02 eta: 18:35:41 time: 0.4015 data_time: 0.0123 memory: 6717 grad_norm: 3.1927 loss: 2.3973 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3973 2023/04/13 20:48:36 - mmengine - INFO - Epoch(train) [5][ 240/1879] lr: 2.0000e-02 eta: 18:35:32 time: 0.3696 data_time: 0.0150 memory: 6717 grad_norm: 3.2173 loss: 2.3400 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3400 2023/04/13 20:48:44 - mmengine - INFO - Epoch(train) [5][ 260/1879] lr: 2.0000e-02 eta: 18:35:34 time: 0.3922 data_time: 0.0133 memory: 6717 grad_norm: 3.2278 loss: 2.3270 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.3270 2023/04/13 20:48:51 - mmengine - INFO - Epoch(train) [5][ 280/1879] lr: 2.0000e-02 eta: 18:35:26 time: 0.3701 data_time: 0.0144 memory: 6717 grad_norm: 3.1780 loss: 2.4288 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4288 2023/04/13 20:49:00 - mmengine - INFO - Epoch(train) [5][ 300/1879] lr: 2.0000e-02 eta: 18:35:40 time: 0.4178 data_time: 0.0163 memory: 6717 grad_norm: 3.3078 loss: 2.3624 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3624 2023/04/13 20:49:06 - mmengine - INFO - Epoch(train) [5][ 320/1879] lr: 2.0000e-02 eta: 18:35:04 time: 0.3094 data_time: 0.0119 memory: 6717 grad_norm: 3.2666 loss: 2.3312 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3312 2023/04/13 20:49:14 - mmengine - INFO - Epoch(train) [5][ 340/1879] lr: 2.0000e-02 eta: 18:35:07 time: 0.3938 data_time: 0.0127 memory: 6717 grad_norm: 3.2421 loss: 2.3022 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.3022 2023/04/13 20:49:20 - mmengine - INFO - Epoch(train) [5][ 360/1879] lr: 2.0000e-02 eta: 18:34:27 time: 0.3020 data_time: 0.0151 memory: 6717 grad_norm: 3.1823 loss: 2.4378 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 2.4378 2023/04/13 20:49:28 - mmengine - INFO - Epoch(train) [5][ 380/1879] lr: 2.0000e-02 eta: 18:34:37 time: 0.4081 data_time: 0.0134 memory: 6717 grad_norm: 3.2183 loss: 2.3376 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.3376 2023/04/13 20:49:34 - mmengine - INFO - Epoch(train) [5][ 400/1879] lr: 2.0000e-02 eta: 18:34:06 time: 0.3202 data_time: 0.0132 memory: 6717 grad_norm: 3.1843 loss: 2.5007 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5007 2023/04/13 20:49:42 - mmengine - INFO - Epoch(train) [5][ 420/1879] lr: 2.0000e-02 eta: 18:34:07 time: 0.3895 data_time: 0.0158 memory: 6717 grad_norm: 3.3880 loss: 2.3356 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3356 2023/04/13 20:49:49 - mmengine - INFO - Epoch(train) [5][ 440/1879] lr: 2.0000e-02 eta: 18:33:55 time: 0.3609 data_time: 0.0127 memory: 6717 grad_norm: 3.1983 loss: 2.2916 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.2916 2023/04/13 20:49:57 - mmengine - INFO - Epoch(train) [5][ 460/1879] lr: 2.0000e-02 eta: 18:33:48 time: 0.3743 data_time: 0.0147 memory: 6717 grad_norm: 3.1920 loss: 2.4618 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.4618 2023/04/13 20:50:05 - mmengine - INFO - Epoch(train) [5][ 480/1879] lr: 2.0000e-02 eta: 18:33:48 time: 0.3868 data_time: 0.0148 memory: 6717 grad_norm: 3.1971 loss: 2.3588 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.3588 2023/04/13 20:50:05 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 20:50:11 - mmengine - INFO - Epoch(train) [5][ 500/1879] lr: 2.0000e-02 eta: 18:33:27 time: 0.3418 data_time: 0.0127 memory: 6717 grad_norm: 3.2701 loss: 2.1793 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.1793 2023/04/13 20:50:19 - mmengine - INFO - Epoch(train) [5][ 520/1879] lr: 2.0000e-02 eta: 18:33:15 time: 0.3615 data_time: 0.0148 memory: 6717 grad_norm: 3.1710 loss: 2.2806 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.2806 2023/04/13 20:50:27 - mmengine - INFO - Epoch(train) [5][ 540/1879] lr: 2.0000e-02 eta: 18:33:19 time: 0.3965 data_time: 0.0136 memory: 6717 grad_norm: 3.3781 loss: 2.3353 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.3353 2023/04/13 20:50:33 - mmengine - INFO - Epoch(train) [5][ 560/1879] lr: 2.0000e-02 eta: 18:32:56 time: 0.3368 data_time: 0.0161 memory: 6717 grad_norm: 3.3262 loss: 2.3325 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3325 2023/04/13 20:50:42 - mmengine - INFO - Epoch(train) [5][ 580/1879] lr: 2.0000e-02 eta: 18:33:07 time: 0.4124 data_time: 0.0139 memory: 6717 grad_norm: 3.1372 loss: 2.5260 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.5260 2023/04/13 20:50:48 - mmengine - INFO - Epoch(train) [5][ 600/1879] lr: 2.0000e-02 eta: 18:32:40 time: 0.3284 data_time: 0.0130 memory: 6717 grad_norm: 3.1443 loss: 2.2398 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2398 2023/04/13 20:50:56 - mmengine - INFO - Epoch(train) [5][ 620/1879] lr: 2.0000e-02 eta: 18:32:32 time: 0.3686 data_time: 0.0135 memory: 6717 grad_norm: 3.1997 loss: 2.4228 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.4228 2023/04/13 20:51:02 - mmengine - INFO - Epoch(train) [5][ 640/1879] lr: 2.0000e-02 eta: 18:32:07 time: 0.3312 data_time: 0.0144 memory: 6717 grad_norm: 3.1615 loss: 2.3308 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 2.3308 2023/04/13 20:51:11 - mmengine - INFO - Epoch(train) [5][ 660/1879] lr: 2.0000e-02 eta: 18:32:27 time: 0.4339 data_time: 0.0137 memory: 6717 grad_norm: 3.1905 loss: 2.3195 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3195 2023/04/13 20:51:17 - mmengine - INFO - Epoch(train) [5][ 680/1879] lr: 2.0000e-02 eta: 18:31:57 time: 0.3207 data_time: 0.0141 memory: 6717 grad_norm: 3.1225 loss: 2.2089 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.2089 2023/04/13 20:51:26 - mmengine - INFO - Epoch(train) [5][ 700/1879] lr: 2.0000e-02 eta: 18:32:17 time: 0.4335 data_time: 0.0186 memory: 6717 grad_norm: 3.2731 loss: 2.5348 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.5348 2023/04/13 20:51:32 - mmengine - INFO - Epoch(train) [5][ 720/1879] lr: 2.0000e-02 eta: 18:31:50 time: 0.3271 data_time: 0.0128 memory: 6717 grad_norm: 3.2133 loss: 2.2410 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.2410 2023/04/13 20:51:41 - mmengine - INFO - Epoch(train) [5][ 740/1879] lr: 2.0000e-02 eta: 18:32:08 time: 0.4293 data_time: 0.0150 memory: 6717 grad_norm: 3.2426 loss: 2.3595 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.3595 2023/04/13 20:51:48 - mmengine - INFO - Epoch(train) [5][ 760/1879] lr: 2.0000e-02 eta: 18:31:41 time: 0.3272 data_time: 0.0134 memory: 6717 grad_norm: 3.2280 loss: 2.4343 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.4343 2023/04/13 20:51:56 - mmengine - INFO - Epoch(train) [5][ 780/1879] lr: 2.0000e-02 eta: 18:31:59 time: 0.4280 data_time: 0.0145 memory: 6717 grad_norm: 3.1108 loss: 2.3075 top1_acc: 0.2500 top5_acc: 0.8125 loss_cls: 2.3075 2023/04/13 20:52:03 - mmengine - INFO - Epoch(train) [5][ 800/1879] lr: 2.0000e-02 eta: 18:31:32 time: 0.3275 data_time: 0.0115 memory: 6717 grad_norm: 3.2806 loss: 2.3471 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3471 2023/04/13 20:52:10 - mmengine - INFO - Epoch(train) [5][ 820/1879] lr: 2.0000e-02 eta: 18:31:32 time: 0.3884 data_time: 0.0144 memory: 6717 grad_norm: 3.1826 loss: 2.5126 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5126 2023/04/13 20:52:17 - mmengine - INFO - Epoch(train) [5][ 840/1879] lr: 2.0000e-02 eta: 18:30:58 time: 0.3103 data_time: 0.0139 memory: 6717 grad_norm: 3.1917 loss: 2.4350 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.4350 2023/04/13 20:52:26 - mmengine - INFO - Epoch(train) [5][ 860/1879] lr: 2.0000e-02 eta: 18:31:22 time: 0.4433 data_time: 0.0151 memory: 6717 grad_norm: 3.1375 loss: 2.3541 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.3541 2023/04/13 20:52:32 - mmengine - INFO - Epoch(train) [5][ 880/1879] lr: 2.0000e-02 eta: 18:30:50 time: 0.3141 data_time: 0.0119 memory: 6717 grad_norm: 3.3346 loss: 2.1912 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1912 2023/04/13 20:52:40 - mmengine - INFO - Epoch(train) [5][ 900/1879] lr: 2.0000e-02 eta: 18:31:09 time: 0.4331 data_time: 0.0156 memory: 6717 grad_norm: 3.0168 loss: 2.6071 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.6071 2023/04/13 20:52:48 - mmengine - INFO - Epoch(train) [5][ 920/1879] lr: 2.0000e-02 eta: 18:30:56 time: 0.3590 data_time: 0.0124 memory: 6717 grad_norm: 3.1240 loss: 2.3444 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.3444 2023/04/13 20:52:56 - mmengine - INFO - Epoch(train) [5][ 940/1879] lr: 2.0000e-02 eta: 18:31:04 time: 0.4073 data_time: 0.0141 memory: 6717 grad_norm: 3.1598 loss: 2.2878 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.2878 2023/04/13 20:53:02 - mmengine - INFO - Epoch(train) [5][ 960/1879] lr: 2.0000e-02 eta: 18:30:26 time: 0.2993 data_time: 0.0126 memory: 6717 grad_norm: 3.1578 loss: 2.5298 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.5298 2023/04/13 20:53:11 - mmengine - INFO - Epoch(train) [5][ 980/1879] lr: 2.0000e-02 eta: 18:30:50 time: 0.4459 data_time: 0.0152 memory: 6717 grad_norm: 3.1338 loss: 2.2321 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.2321 2023/04/13 20:53:17 - mmengine - INFO - Epoch(train) [5][1000/1879] lr: 2.0000e-02 eta: 18:30:28 time: 0.3362 data_time: 0.0117 memory: 6717 grad_norm: 3.2011 loss: 2.3041 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.3041 2023/04/13 20:53:25 - mmengine - INFO - Epoch(train) [5][1020/1879] lr: 2.0000e-02 eta: 18:30:34 time: 0.4032 data_time: 0.0128 memory: 6717 grad_norm: 3.2149 loss: 2.3955 top1_acc: 0.4375 top5_acc: 0.5000 loss_cls: 2.3955 2023/04/13 20:53:32 - mmengine - INFO - Epoch(train) [5][1040/1879] lr: 2.0000e-02 eta: 18:29:58 time: 0.3055 data_time: 0.0146 memory: 6717 grad_norm: 3.2394 loss: 2.2969 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.2969 2023/04/13 20:53:40 - mmengine - INFO - Epoch(train) [5][1060/1879] lr: 2.0000e-02 eta: 18:30:04 time: 0.4022 data_time: 0.0131 memory: 6717 grad_norm: 3.1683 loss: 2.4230 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.4230 2023/04/13 20:53:46 - mmengine - INFO - Epoch(train) [5][1080/1879] lr: 2.0000e-02 eta: 18:29:32 time: 0.3128 data_time: 0.0140 memory: 6717 grad_norm: 3.2135 loss: 2.3884 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3884 2023/04/13 20:53:54 - mmengine - INFO - Epoch(train) [5][1100/1879] lr: 2.0000e-02 eta: 18:29:45 time: 0.4200 data_time: 0.0153 memory: 6717 grad_norm: 3.1939 loss: 2.4588 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4588 2023/04/13 20:54:01 - mmengine - INFO - Epoch(train) [5][1120/1879] lr: 2.0000e-02 eta: 18:29:21 time: 0.3308 data_time: 0.0125 memory: 6717 grad_norm: 3.2889 loss: 2.2405 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.2405 2023/04/13 20:54:09 - mmengine - INFO - Epoch(train) [5][1140/1879] lr: 2.0000e-02 eta: 18:29:29 time: 0.4090 data_time: 0.0160 memory: 6717 grad_norm: 3.1719 loss: 2.1632 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.1632 2023/04/13 20:54:16 - mmengine - INFO - Epoch(train) [5][1160/1879] lr: 2.0000e-02 eta: 18:29:08 time: 0.3390 data_time: 0.0125 memory: 6717 grad_norm: 3.1496 loss: 2.4307 top1_acc: 0.3750 top5_acc: 0.4375 loss_cls: 2.4307 2023/04/13 20:54:23 - mmengine - INFO - Epoch(train) [5][1180/1879] lr: 2.0000e-02 eta: 18:28:59 time: 0.3681 data_time: 0.0151 memory: 6717 grad_norm: 3.2139 loss: 2.4279 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4279 2023/04/13 20:54:32 - mmengine - INFO - Epoch(train) [5][1200/1879] lr: 2.0000e-02 eta: 18:29:14 time: 0.4249 data_time: 0.0128 memory: 6717 grad_norm: 3.1577 loss: 2.5242 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.5242 2023/04/13 20:54:39 - mmengine - INFO - Epoch(train) [5][1220/1879] lr: 2.0000e-02 eta: 18:28:56 time: 0.3453 data_time: 0.0136 memory: 6717 grad_norm: 3.0988 loss: 2.4244 top1_acc: 0.2500 top5_acc: 0.3125 loss_cls: 2.4244 2023/04/13 20:54:47 - mmengine - INFO - Epoch(train) [5][1240/1879] lr: 2.0000e-02 eta: 18:29:08 time: 0.4189 data_time: 0.0128 memory: 6717 grad_norm: 3.1475 loss: 2.4757 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.4757 2023/04/13 20:54:53 - mmengine - INFO - Epoch(train) [5][1260/1879] lr: 2.0000e-02 eta: 18:28:38 time: 0.3172 data_time: 0.0150 memory: 6717 grad_norm: 3.1441 loss: 2.2224 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.2224 2023/04/13 20:55:01 - mmengine - INFO - Epoch(train) [5][1280/1879] lr: 2.0000e-02 eta: 18:28:37 time: 0.3860 data_time: 0.0119 memory: 6717 grad_norm: 3.1021 loss: 2.5399 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.5399 2023/04/13 20:55:08 - mmengine - INFO - Epoch(train) [5][1300/1879] lr: 2.0000e-02 eta: 18:28:13 time: 0.3314 data_time: 0.0151 memory: 6717 grad_norm: 3.1705 loss: 2.4450 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4450 2023/04/13 20:55:16 - mmengine - INFO - Epoch(train) [5][1320/1879] lr: 2.0000e-02 eta: 18:28:19 time: 0.4033 data_time: 0.0125 memory: 6717 grad_norm: 3.1057 loss: 2.3489 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.3489 2023/04/13 20:55:22 - mmengine - INFO - Epoch(train) [5][1340/1879] lr: 2.0000e-02 eta: 18:27:51 time: 0.3221 data_time: 0.0134 memory: 6717 grad_norm: 3.3617 loss: 2.2823 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.2823 2023/04/13 20:55:31 - mmengine - INFO - Epoch(train) [5][1360/1879] lr: 2.0000e-02 eta: 18:28:03 time: 0.4193 data_time: 0.0134 memory: 6717 grad_norm: 3.2194 loss: 2.3621 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.3621 2023/04/13 20:55:37 - mmengine - INFO - Epoch(train) [5][1380/1879] lr: 2.0000e-02 eta: 18:27:38 time: 0.3284 data_time: 0.0141 memory: 6717 grad_norm: 3.1795 loss: 2.3784 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3784 2023/04/13 20:55:46 - mmengine - INFO - Epoch(train) [5][1400/1879] lr: 2.0000e-02 eta: 18:27:50 time: 0.4184 data_time: 0.0128 memory: 6717 grad_norm: 3.1907 loss: 2.5486 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.5486 2023/04/13 20:55:52 - mmengine - INFO - Epoch(train) [5][1420/1879] lr: 2.0000e-02 eta: 18:27:26 time: 0.3303 data_time: 0.0150 memory: 6717 grad_norm: 3.1617 loss: 2.3058 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.3058 2023/04/13 20:56:00 - mmengine - INFO - Epoch(train) [5][1440/1879] lr: 2.0000e-02 eta: 18:27:28 time: 0.3945 data_time: 0.0121 memory: 6717 grad_norm: 3.1454 loss: 2.3944 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.3944 2023/04/13 20:56:07 - mmengine - INFO - Epoch(train) [5][1460/1879] lr: 2.0000e-02 eta: 18:27:15 time: 0.3573 data_time: 0.0139 memory: 6717 grad_norm: 3.0776 loss: 2.4511 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.4511 2023/04/13 20:56:15 - mmengine - INFO - Epoch(train) [5][1480/1879] lr: 2.0000e-02 eta: 18:27:15 time: 0.3908 data_time: 0.0143 memory: 6717 grad_norm: 3.2018 loss: 2.3916 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.3916 2023/04/13 20:56:16 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 20:56:22 - mmengine - INFO - Epoch(train) [5][1500/1879] lr: 2.0000e-02 eta: 18:27:01 time: 0.3540 data_time: 0.0142 memory: 6717 grad_norm: 3.0962 loss: 2.3738 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.3738 2023/04/13 20:56:30 - mmengine - INFO - Epoch(train) [5][1520/1879] lr: 2.0000e-02 eta: 18:26:59 time: 0.3857 data_time: 0.0134 memory: 6717 grad_norm: 3.2437 loss: 2.4754 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.4754 2023/04/13 20:56:36 - mmengine - INFO - Epoch(train) [5][1540/1879] lr: 2.0000e-02 eta: 18:26:33 time: 0.3240 data_time: 0.0139 memory: 6717 grad_norm: 3.2472 loss: 2.2444 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.2444 2023/04/13 20:56:44 - mmengine - INFO - Epoch(train) [5][1560/1879] lr: 2.0000e-02 eta: 18:26:38 time: 0.4029 data_time: 0.0127 memory: 6717 grad_norm: 3.1002 loss: 2.2498 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.2498 2023/04/13 20:56:50 - mmengine - INFO - Epoch(train) [5][1580/1879] lr: 2.0000e-02 eta: 18:26:04 time: 0.3029 data_time: 0.0134 memory: 6717 grad_norm: 3.1712 loss: 2.4306 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4306 2023/04/13 20:56:58 - mmengine - INFO - Epoch(train) [5][1600/1879] lr: 2.0000e-02 eta: 18:26:07 time: 0.3973 data_time: 0.0140 memory: 6717 grad_norm: 3.1321 loss: 2.3081 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3081 2023/04/13 20:57:05 - mmengine - INFO - Epoch(train) [5][1620/1879] lr: 2.0000e-02 eta: 18:25:45 time: 0.3336 data_time: 0.0139 memory: 6717 grad_norm: 3.1270 loss: 2.4028 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.4028 2023/04/13 20:57:13 - mmengine - INFO - Epoch(train) [5][1640/1879] lr: 2.0000e-02 eta: 18:25:57 time: 0.4222 data_time: 0.0159 memory: 6717 grad_norm: 3.1455 loss: 2.0216 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.0216 2023/04/13 20:57:20 - mmengine - INFO - Epoch(train) [5][1660/1879] lr: 2.0000e-02 eta: 18:25:24 time: 0.3043 data_time: 0.0126 memory: 6717 grad_norm: 3.1511 loss: 2.2131 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.2131 2023/04/13 20:57:28 - mmengine - INFO - Epoch(train) [5][1680/1879] lr: 2.0000e-02 eta: 18:25:33 time: 0.4138 data_time: 0.0144 memory: 6717 grad_norm: 3.1177 loss: 2.4038 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4038 2023/04/13 20:57:34 - mmengine - INFO - Epoch(train) [5][1700/1879] lr: 2.0000e-02 eta: 18:25:06 time: 0.3218 data_time: 0.0128 memory: 6717 grad_norm: 3.1926 loss: 2.1903 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 2.1903 2023/04/13 20:57:43 - mmengine - INFO - Epoch(train) [5][1720/1879] lr: 2.0000e-02 eta: 18:25:16 time: 0.4154 data_time: 0.0128 memory: 6717 grad_norm: 3.1495 loss: 2.2900 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2900 2023/04/13 20:57:50 - mmengine - INFO - Epoch(train) [5][1740/1879] lr: 2.0000e-02 eta: 18:25:03 time: 0.3563 data_time: 0.0127 memory: 6717 grad_norm: 3.1599 loss: 2.2682 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.2682 2023/04/13 20:57:57 - mmengine - INFO - Epoch(train) [5][1760/1879] lr: 2.0000e-02 eta: 18:24:58 time: 0.3773 data_time: 0.0141 memory: 6717 grad_norm: 3.1488 loss: 2.5054 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.5054 2023/04/13 20:58:04 - mmengine - INFO - Epoch(train) [5][1780/1879] lr: 2.0000e-02 eta: 18:24:33 time: 0.3265 data_time: 0.0136 memory: 6717 grad_norm: 3.1667 loss: 2.3116 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3116 2023/04/13 20:58:12 - mmengine - INFO - Epoch(train) [5][1800/1879] lr: 2.0000e-02 eta: 18:24:45 time: 0.4205 data_time: 0.0144 memory: 6717 grad_norm: 3.1556 loss: 2.4815 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 2.4815 2023/04/13 20:58:18 - mmengine - INFO - Epoch(train) [5][1820/1879] lr: 2.0000e-02 eta: 18:24:15 time: 0.3119 data_time: 0.0118 memory: 6717 grad_norm: 3.0700 loss: 2.1615 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.1615 2023/04/13 20:58:27 - mmengine - INFO - Epoch(train) [5][1840/1879] lr: 2.0000e-02 eta: 18:24:22 time: 0.4110 data_time: 0.0145 memory: 6717 grad_norm: 3.0646 loss: 2.4216 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.4216 2023/04/13 20:58:33 - mmengine - INFO - Epoch(train) [5][1860/1879] lr: 2.0000e-02 eta: 18:23:48 time: 0.2997 data_time: 0.0124 memory: 6717 grad_norm: 3.1070 loss: 2.3927 top1_acc: 0.5625 top5_acc: 0.5625 loss_cls: 2.3927 2023/04/13 20:58:40 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 20:58:40 - mmengine - INFO - Epoch(train) [5][1879/1879] lr: 2.0000e-02 eta: 18:23:49 time: 0.3824 data_time: 0.0120 memory: 6717 grad_norm: 3.1784 loss: 2.2662 top1_acc: 0.1429 top5_acc: 0.5714 loss_cls: 2.2662 2023/04/13 20:58:49 - mmengine - INFO - Epoch(val) [5][ 20/155] eta: 0:00:57 time: 0.4291 data_time: 0.3955 memory: 1391 2023/04/13 20:58:56 - mmengine - INFO - Epoch(val) [5][ 40/155] eta: 0:00:44 time: 0.3476 data_time: 0.3148 memory: 1391 2023/04/13 20:59:03 - mmengine - INFO - Epoch(val) [5][ 60/155] eta: 0:00:36 time: 0.3775 data_time: 0.3436 memory: 1391 2023/04/13 20:59:11 - mmengine - INFO - Epoch(val) [5][ 80/155] eta: 0:00:28 time: 0.3648 data_time: 0.3313 memory: 1391 2023/04/13 20:59:19 - mmengine - INFO - Epoch(val) [5][100/155] eta: 0:00:21 time: 0.4300 data_time: 0.3971 memory: 1391 2023/04/13 20:59:25 - mmengine - INFO - Epoch(val) [5][120/155] eta: 0:00:13 time: 0.3105 data_time: 0.2780 memory: 1391 2023/04/13 20:59:33 - mmengine - INFO - Epoch(val) [5][140/155] eta: 0:00:05 time: 0.3765 data_time: 0.3429 memory: 1391 2023/04/13 20:59:42 - mmengine - INFO - Epoch(val) [5][155/155] acc/top1: 0.4913 acc/top5: 0.7555 acc/mean1: 0.4910 data_time: 0.3088 time: 0.3418 2023/04/13 20:59:42 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_4.pth is removed 2023/04/13 20:59:42 - mmengine - INFO - The best checkpoint with 0.4913 acc/top1 at 5 epoch is saved to best_acc_top1_epoch_5.pth. 2023/04/13 20:59:51 - mmengine - INFO - Epoch(train) [6][ 20/1879] lr: 2.0000e-02 eta: 18:24:13 time: 0.4533 data_time: 0.3114 memory: 6717 grad_norm: 3.1329 loss: 2.3272 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.3272 2023/04/13 20:59:58 - mmengine - INFO - Epoch(train) [6][ 40/1879] lr: 2.0000e-02 eta: 18:23:52 time: 0.3356 data_time: 0.1909 memory: 6717 grad_norm: 3.1078 loss: 2.2163 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2163 2023/04/13 21:00:06 - mmengine - INFO - Epoch(train) [6][ 60/1879] lr: 2.0000e-02 eta: 18:24:05 time: 0.4239 data_time: 0.2528 memory: 6717 grad_norm: 3.1114 loss: 2.4488 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.4488 2023/04/13 21:00:13 - mmengine - INFO - Epoch(train) [6][ 80/1879] lr: 2.0000e-02 eta: 18:23:49 time: 0.3498 data_time: 0.1118 memory: 6717 grad_norm: 3.2739 loss: 2.2811 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2811 2023/04/13 21:00:22 - mmengine - INFO - Epoch(train) [6][ 100/1879] lr: 2.0000e-02 eta: 18:24:03 time: 0.4266 data_time: 0.0136 memory: 6717 grad_norm: 3.1762 loss: 2.3000 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3000 2023/04/13 21:00:28 - mmengine - INFO - Epoch(train) [6][ 120/1879] lr: 2.0000e-02 eta: 18:23:31 time: 0.3057 data_time: 0.0128 memory: 6717 grad_norm: 3.2016 loss: 2.2923 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.2923 2023/04/13 21:00:36 - mmengine - INFO - Epoch(train) [6][ 140/1879] lr: 2.0000e-02 eta: 18:23:32 time: 0.3944 data_time: 0.0583 memory: 6717 grad_norm: 3.1850 loss: 2.3026 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3026 2023/04/13 21:00:43 - mmengine - INFO - Epoch(train) [6][ 160/1879] lr: 2.0000e-02 eta: 18:23:15 time: 0.3450 data_time: 0.0487 memory: 6717 grad_norm: 3.1684 loss: 2.2239 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2239 2023/04/13 21:00:52 - mmengine - INFO - Epoch(train) [6][ 180/1879] lr: 2.0000e-02 eta: 18:23:36 time: 0.4494 data_time: 0.0347 memory: 6717 grad_norm: 3.1456 loss: 2.4985 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.4985 2023/04/13 21:00:58 - mmengine - INFO - Epoch(train) [6][ 200/1879] lr: 2.0000e-02 eta: 18:23:06 time: 0.3098 data_time: 0.0211 memory: 6717 grad_norm: 3.2105 loss: 2.4801 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.4801 2023/04/13 21:01:06 - mmengine - INFO - Epoch(train) [6][ 220/1879] lr: 2.0000e-02 eta: 18:23:17 time: 0.4191 data_time: 0.0729 memory: 6717 grad_norm: 3.1323 loss: 2.0838 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0838 2023/04/13 21:01:13 - mmengine - INFO - Epoch(train) [6][ 240/1879] lr: 2.0000e-02 eta: 18:22:56 time: 0.3355 data_time: 0.0364 memory: 6717 grad_norm: 3.2024 loss: 2.3849 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.3849 2023/04/13 21:01:22 - mmengine - INFO - Epoch(train) [6][ 260/1879] lr: 2.0000e-02 eta: 18:23:14 time: 0.4410 data_time: 0.0153 memory: 6717 grad_norm: 3.1175 loss: 2.5206 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.5206 2023/04/13 21:01:28 - mmengine - INFO - Epoch(train) [6][ 280/1879] lr: 2.0000e-02 eta: 18:22:52 time: 0.3321 data_time: 0.0125 memory: 6717 grad_norm: 3.2732 loss: 2.1349 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1349 2023/04/13 21:01:37 - mmengine - INFO - Epoch(train) [6][ 300/1879] lr: 2.0000e-02 eta: 18:23:11 time: 0.4421 data_time: 0.0130 memory: 6717 grad_norm: 3.1430 loss: 2.2964 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 2.2964 2023/04/13 21:01:43 - mmengine - INFO - Epoch(train) [6][ 320/1879] lr: 2.0000e-02 eta: 18:22:38 time: 0.3028 data_time: 0.0133 memory: 6717 grad_norm: 3.1384 loss: 2.0939 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0939 2023/04/13 21:01:52 - mmengine - INFO - Epoch(train) [6][ 340/1879] lr: 2.0000e-02 eta: 18:22:47 time: 0.4153 data_time: 0.0138 memory: 6717 grad_norm: 3.1303 loss: 2.3875 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3875 2023/04/13 21:01:58 - mmengine - INFO - Epoch(train) [6][ 360/1879] lr: 2.0000e-02 eta: 18:22:12 time: 0.2966 data_time: 0.0127 memory: 6717 grad_norm: 3.2247 loss: 2.1068 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1068 2023/04/13 21:02:06 - mmengine - INFO - Epoch(train) [6][ 380/1879] lr: 2.0000e-02 eta: 18:22:22 time: 0.4191 data_time: 0.0146 memory: 6717 grad_norm: 3.1592 loss: 2.2539 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2539 2023/04/13 21:02:13 - mmengine - INFO - Epoch(train) [6][ 400/1879] lr: 2.0000e-02 eta: 18:22:07 time: 0.3503 data_time: 0.0127 memory: 6717 grad_norm: 3.2075 loss: 2.3391 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3391 2023/04/13 21:02:21 - mmengine - INFO - Epoch(train) [6][ 420/1879] lr: 2.0000e-02 eta: 18:22:18 time: 0.4212 data_time: 0.0142 memory: 6717 grad_norm: 3.1699 loss: 2.1176 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.1176 2023/04/13 21:02:28 - mmengine - INFO - Epoch(train) [6][ 440/1879] lr: 2.0000e-02 eta: 18:21:58 time: 0.3365 data_time: 0.0127 memory: 6717 grad_norm: 3.1394 loss: 2.4122 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.4122 2023/04/13 21:02:36 - mmengine - INFO - Epoch(train) [6][ 460/1879] lr: 2.0000e-02 eta: 18:22:06 time: 0.4148 data_time: 0.0143 memory: 6717 grad_norm: 3.1756 loss: 2.1558 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1558 2023/04/13 21:02:43 - mmengine - INFO - Epoch(train) [6][ 480/1879] lr: 2.0000e-02 eta: 18:21:36 time: 0.3075 data_time: 0.0132 memory: 6717 grad_norm: 5.1945 loss: 2.2371 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 2.2371 2023/04/13 21:02:51 - mmengine - INFO - Epoch(train) [6][ 500/1879] lr: 2.0000e-02 eta: 18:21:44 time: 0.4141 data_time: 0.0140 memory: 6717 grad_norm: 3.3365 loss: 2.4660 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.4660 2023/04/13 21:02:57 - mmengine - INFO - Epoch(train) [6][ 520/1879] lr: 2.0000e-02 eta: 18:21:13 time: 0.3073 data_time: 0.0126 memory: 6717 grad_norm: 3.1250 loss: 2.3585 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.3585 2023/04/13 21:03:05 - mmengine - INFO - Epoch(train) [6][ 540/1879] lr: 2.0000e-02 eta: 18:21:19 time: 0.4088 data_time: 0.0152 memory: 6717 grad_norm: 3.1763 loss: 2.2564 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2564 2023/04/13 21:03:11 - mmengine - INFO - Epoch(train) [6][ 560/1879] lr: 2.0000e-02 eta: 18:20:48 time: 0.3056 data_time: 0.0126 memory: 6717 grad_norm: 3.1794 loss: 2.2793 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2793 2023/04/13 21:03:19 - mmengine - INFO - Epoch(train) [6][ 580/1879] lr: 2.0000e-02 eta: 18:20:49 time: 0.3933 data_time: 0.0153 memory: 6717 grad_norm: 3.1724 loss: 2.2996 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2996 2023/04/13 21:03:26 - mmengine - INFO - Epoch(train) [6][ 600/1879] lr: 2.0000e-02 eta: 18:20:34 time: 0.3495 data_time: 0.0129 memory: 6717 grad_norm: 3.1487 loss: 2.5797 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.5797 2023/04/13 21:03:28 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 21:03:34 - mmengine - INFO - Epoch(train) [6][ 620/1879] lr: 2.0000e-02 eta: 18:20:29 time: 0.3784 data_time: 0.0217 memory: 6717 grad_norm: 3.1289 loss: 2.3792 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3792 2023/04/13 21:03:41 - mmengine - INFO - Epoch(train) [6][ 640/1879] lr: 2.0000e-02 eta: 18:20:25 time: 0.3825 data_time: 0.0133 memory: 6717 grad_norm: 3.1616 loss: 2.2020 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.2020 2023/04/13 21:03:48 - mmengine - INFO - Epoch(train) [6][ 660/1879] lr: 2.0000e-02 eta: 18:20:10 time: 0.3480 data_time: 0.0144 memory: 6717 grad_norm: 3.0708 loss: 2.2237 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2237 2023/04/13 21:03:56 - mmengine - INFO - Epoch(train) [6][ 680/1879] lr: 2.0000e-02 eta: 18:20:11 time: 0.3957 data_time: 0.0134 memory: 6717 grad_norm: 3.1786 loss: 2.1735 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.1735 2023/04/13 21:04:03 - mmengine - INFO - Epoch(train) [6][ 700/1879] lr: 2.0000e-02 eta: 18:19:47 time: 0.3256 data_time: 0.0141 memory: 6717 grad_norm: 3.1359 loss: 2.3655 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3655 2023/04/13 21:04:10 - mmengine - INFO - Epoch(train) [6][ 720/1879] lr: 2.0000e-02 eta: 18:19:43 time: 0.3796 data_time: 0.0127 memory: 6717 grad_norm: 3.1285 loss: 2.1114 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 2.1114 2023/04/13 21:04:18 - mmengine - INFO - Epoch(train) [6][ 740/1879] lr: 2.0000e-02 eta: 18:19:41 time: 0.3869 data_time: 0.0147 memory: 6717 grad_norm: 3.2351 loss: 2.3283 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3283 2023/04/13 21:04:26 - mmengine - INFO - Epoch(train) [6][ 760/1879] lr: 2.0000e-02 eta: 18:19:43 time: 0.3977 data_time: 0.0133 memory: 6717 grad_norm: 3.0389 loss: 2.3993 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3993 2023/04/13 21:04:33 - mmengine - INFO - Epoch(train) [6][ 780/1879] lr: 2.0000e-02 eta: 18:19:23 time: 0.3366 data_time: 0.0142 memory: 6717 grad_norm: 3.1541 loss: 2.4328 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4328 2023/04/13 21:04:41 - mmengine - INFO - Epoch(train) [6][ 800/1879] lr: 2.0000e-02 eta: 18:19:29 time: 0.4098 data_time: 0.0123 memory: 6717 grad_norm: 3.1293 loss: 2.4466 top1_acc: 0.0625 top5_acc: 0.5625 loss_cls: 2.4466 2023/04/13 21:04:47 - mmengine - INFO - Epoch(train) [6][ 820/1879] lr: 2.0000e-02 eta: 18:18:52 time: 0.2843 data_time: 0.0137 memory: 6717 grad_norm: 3.1398 loss: 2.4919 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.4919 2023/04/13 21:04:56 - mmengine - INFO - Epoch(train) [6][ 840/1879] lr: 2.0000e-02 eta: 18:19:10 time: 0.4454 data_time: 0.0140 memory: 6717 grad_norm: 3.1342 loss: 2.4305 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.4305 2023/04/13 21:05:02 - mmengine - INFO - Epoch(train) [6][ 860/1879] lr: 2.0000e-02 eta: 18:18:43 time: 0.3145 data_time: 0.0130 memory: 6717 grad_norm: 3.6241 loss: 2.3064 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.3064 2023/04/13 21:05:10 - mmengine - INFO - Epoch(train) [6][ 880/1879] lr: 2.0000e-02 eta: 18:18:52 time: 0.4171 data_time: 0.0153 memory: 6717 grad_norm: 3.3080 loss: 2.3395 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3395 2023/04/13 21:05:16 - mmengine - INFO - Epoch(train) [6][ 900/1879] lr: 2.0000e-02 eta: 18:18:24 time: 0.3114 data_time: 0.0129 memory: 6717 grad_norm: 3.0695 loss: 2.2633 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.2633 2023/04/13 21:05:25 - mmengine - INFO - Epoch(train) [6][ 920/1879] lr: 2.0000e-02 eta: 18:18:28 time: 0.4063 data_time: 0.0152 memory: 6717 grad_norm: 3.0766 loss: 2.3080 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3080 2023/04/13 21:05:31 - mmengine - INFO - Epoch(train) [6][ 940/1879] lr: 2.0000e-02 eta: 18:18:08 time: 0.3332 data_time: 0.0126 memory: 6717 grad_norm: 3.1095 loss: 2.2005 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.2005 2023/04/13 21:05:40 - mmengine - INFO - Epoch(train) [6][ 960/1879] lr: 2.0000e-02 eta: 18:18:17 time: 0.4208 data_time: 0.0135 memory: 6717 grad_norm: 3.0570 loss: 2.4233 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 2.4233 2023/04/13 21:05:46 - mmengine - INFO - Epoch(train) [6][ 980/1879] lr: 2.0000e-02 eta: 18:17:59 time: 0.3391 data_time: 0.0131 memory: 6717 grad_norm: 3.1340 loss: 2.6105 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.6105 2023/04/13 21:05:54 - mmengine - INFO - Epoch(train) [6][1000/1879] lr: 2.0000e-02 eta: 18:17:47 time: 0.3585 data_time: 0.0134 memory: 6717 grad_norm: 3.0889 loss: 2.2368 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2368 2023/04/13 21:06:01 - mmengine - INFO - Epoch(train) [6][1020/1879] lr: 2.0000e-02 eta: 18:17:44 time: 0.3842 data_time: 0.0141 memory: 6717 grad_norm: 3.1532 loss: 2.3352 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.3352 2023/04/13 21:06:08 - mmengine - INFO - Epoch(train) [6][1040/1879] lr: 2.0000e-02 eta: 18:17:32 time: 0.3573 data_time: 0.0138 memory: 6717 grad_norm: 3.1238 loss: 2.4299 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4299 2023/04/13 21:06:16 - mmengine - INFO - Epoch(train) [6][1060/1879] lr: 2.0000e-02 eta: 18:17:28 time: 0.3812 data_time: 0.0143 memory: 6717 grad_norm: 3.1958 loss: 2.2972 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.2972 2023/04/13 21:06:23 - mmengine - INFO - Epoch(train) [6][1080/1879] lr: 2.0000e-02 eta: 18:17:06 time: 0.3278 data_time: 0.0148 memory: 6717 grad_norm: 3.1129 loss: 2.2817 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.2817 2023/04/13 21:06:31 - mmengine - INFO - Epoch(train) [6][1100/1879] lr: 2.0000e-02 eta: 18:17:21 time: 0.4367 data_time: 0.0133 memory: 6717 grad_norm: 3.1563 loss: 2.1812 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.1812 2023/04/13 21:06:38 - mmengine - INFO - Epoch(train) [6][1120/1879] lr: 2.0000e-02 eta: 18:17:02 time: 0.3368 data_time: 0.0131 memory: 6717 grad_norm: 3.2061 loss: 2.3691 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.3691 2023/04/13 21:06:46 - mmengine - INFO - Epoch(train) [6][1140/1879] lr: 2.0000e-02 eta: 18:16:58 time: 0.3799 data_time: 0.0133 memory: 6717 grad_norm: 3.0490 loss: 2.3151 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.3151 2023/04/13 21:06:52 - mmengine - INFO - Epoch(train) [6][1160/1879] lr: 2.0000e-02 eta: 18:16:29 time: 0.3074 data_time: 0.0134 memory: 6717 grad_norm: 3.1058 loss: 2.4293 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.4293 2023/04/13 21:07:00 - mmengine - INFO - Epoch(train) [6][1180/1879] lr: 2.0000e-02 eta: 18:16:26 time: 0.3843 data_time: 0.0148 memory: 6717 grad_norm: 3.0954 loss: 2.3937 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.3937 2023/04/13 21:07:07 - mmengine - INFO - Epoch(train) [6][1200/1879] lr: 2.0000e-02 eta: 18:16:14 time: 0.3587 data_time: 0.0130 memory: 6717 grad_norm: 3.0780 loss: 2.2065 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2065 2023/04/13 21:07:15 - mmengine - INFO - Epoch(train) [6][1220/1879] lr: 2.0000e-02 eta: 18:16:18 time: 0.4056 data_time: 0.0134 memory: 6717 grad_norm: 3.0874 loss: 2.0466 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0466 2023/04/13 21:07:23 - mmengine - INFO - Epoch(train) [6][1240/1879] lr: 2.0000e-02 eta: 18:16:18 time: 0.3919 data_time: 0.0136 memory: 6717 grad_norm: 2.9877 loss: 2.2665 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2665 2023/04/13 21:07:29 - mmengine - INFO - Epoch(train) [6][1260/1879] lr: 2.0000e-02 eta: 18:15:57 time: 0.3318 data_time: 0.0142 memory: 6717 grad_norm: 3.1018 loss: 2.4083 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.4083 2023/04/13 21:07:37 - mmengine - INFO - Epoch(train) [6][1280/1879] lr: 2.0000e-02 eta: 18:15:52 time: 0.3759 data_time: 0.0137 memory: 6717 grad_norm: 3.1482 loss: 2.3154 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3154 2023/04/13 21:07:44 - mmengine - INFO - Epoch(train) [6][1300/1879] lr: 2.0000e-02 eta: 18:15:44 time: 0.3695 data_time: 0.0139 memory: 6717 grad_norm: 3.0883 loss: 2.3871 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.3871 2023/04/13 21:07:51 - mmengine - INFO - Epoch(train) [6][1320/1879] lr: 2.0000e-02 eta: 18:15:26 time: 0.3388 data_time: 0.0143 memory: 6717 grad_norm: 3.0861 loss: 2.3281 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.3281 2023/04/13 21:07:58 - mmengine - INFO - Epoch(train) [6][1340/1879] lr: 2.0000e-02 eta: 18:15:17 time: 0.3667 data_time: 0.0163 memory: 6717 grad_norm: 3.0666 loss: 2.4007 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4007 2023/04/13 21:08:06 - mmengine - INFO - Epoch(train) [6][1360/1879] lr: 2.0000e-02 eta: 18:15:07 time: 0.3629 data_time: 0.0120 memory: 6717 grad_norm: 3.1305 loss: 2.1481 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1481 2023/04/13 21:08:14 - mmengine - INFO - Epoch(train) [6][1380/1879] lr: 2.0000e-02 eta: 18:15:08 time: 0.3984 data_time: 0.0139 memory: 6717 grad_norm: 3.0782 loss: 2.3839 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.3839 2023/04/13 21:08:20 - mmengine - INFO - Epoch(train) [6][1400/1879] lr: 2.0000e-02 eta: 18:14:45 time: 0.3213 data_time: 0.0153 memory: 6717 grad_norm: 3.1287 loss: 2.2439 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.2439 2023/04/13 21:08:28 - mmengine - INFO - Epoch(train) [6][1420/1879] lr: 2.0000e-02 eta: 18:14:44 time: 0.3913 data_time: 0.0141 memory: 6717 grad_norm: 3.0882 loss: 2.1961 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 2.1961 2023/04/13 21:08:34 - mmengine - INFO - Epoch(train) [6][1440/1879] lr: 2.0000e-02 eta: 18:14:22 time: 0.3260 data_time: 0.0130 memory: 6717 grad_norm: 3.0634 loss: 2.2188 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2188 2023/04/13 21:08:42 - mmengine - INFO - Epoch(train) [6][1460/1879] lr: 2.0000e-02 eta: 18:14:23 time: 0.3975 data_time: 0.0141 memory: 6717 grad_norm: 3.1411 loss: 2.1921 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1921 2023/04/13 21:08:49 - mmengine - INFO - Epoch(train) [6][1480/1879] lr: 2.0000e-02 eta: 18:14:10 time: 0.3538 data_time: 0.0128 memory: 6717 grad_norm: 3.0725 loss: 2.1975 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.1975 2023/04/13 21:08:57 - mmengine - INFO - Epoch(train) [6][1500/1879] lr: 2.0000e-02 eta: 18:14:08 time: 0.3885 data_time: 0.0186 memory: 6717 grad_norm: 3.0717 loss: 2.1066 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1066 2023/04/13 21:09:04 - mmengine - INFO - Epoch(train) [6][1520/1879] lr: 2.0000e-02 eta: 18:13:47 time: 0.3287 data_time: 0.0123 memory: 6717 grad_norm: 3.0779 loss: 2.1331 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.1331 2023/04/13 21:09:11 - mmengine - INFO - Epoch(train) [6][1540/1879] lr: 2.0000e-02 eta: 18:13:46 time: 0.3885 data_time: 0.0144 memory: 6717 grad_norm: 3.1466 loss: 2.4981 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4981 2023/04/13 21:09:18 - mmengine - INFO - Epoch(train) [6][1560/1879] lr: 2.0000e-02 eta: 18:13:29 time: 0.3433 data_time: 0.0136 memory: 6717 grad_norm: 3.0973 loss: 2.2515 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2515 2023/04/13 21:09:27 - mmengine - INFO - Epoch(train) [6][1580/1879] lr: 2.0000e-02 eta: 18:13:40 time: 0.4261 data_time: 0.0123 memory: 6717 grad_norm: 3.0291 loss: 2.2567 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 2.2567 2023/04/13 21:09:33 - mmengine - INFO - Epoch(train) [6][1600/1879] lr: 2.0000e-02 eta: 18:13:15 time: 0.3180 data_time: 0.0141 memory: 6717 grad_norm: 3.0461 loss: 2.0220 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0220 2023/04/13 21:09:35 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 21:09:41 - mmengine - INFO - Epoch(train) [6][1620/1879] lr: 2.0000e-02 eta: 18:13:15 time: 0.3921 data_time: 0.0141 memory: 6717 grad_norm: 3.1557 loss: 2.0589 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.0589 2023/04/13 21:09:48 - mmengine - INFO - Epoch(train) [6][1640/1879] lr: 2.0000e-02 eta: 18:12:54 time: 0.3306 data_time: 0.0144 memory: 6717 grad_norm: 3.0759 loss: 2.2684 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2684 2023/04/13 21:09:57 - mmengine - INFO - Epoch(train) [6][1660/1879] lr: 2.0000e-02 eta: 18:13:12 time: 0.4497 data_time: 0.0131 memory: 6717 grad_norm: 3.1172 loss: 2.3589 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3589 2023/04/13 21:10:03 - mmengine - INFO - Epoch(train) [6][1680/1879] lr: 2.0000e-02 eta: 18:12:53 time: 0.3339 data_time: 0.0134 memory: 6717 grad_norm: 3.0658 loss: 2.3848 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.3848 2023/04/13 21:10:11 - mmengine - INFO - Epoch(train) [6][1700/1879] lr: 2.0000e-02 eta: 18:12:52 time: 0.3904 data_time: 0.0141 memory: 6717 grad_norm: 3.0068 loss: 2.5069 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.5069 2023/04/13 21:10:18 - mmengine - INFO - Epoch(train) [6][1720/1879] lr: 2.0000e-02 eta: 18:12:36 time: 0.3439 data_time: 0.0140 memory: 6717 grad_norm: 3.1637 loss: 2.2925 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.2925 2023/04/13 21:10:26 - mmengine - INFO - Epoch(train) [6][1740/1879] lr: 2.0000e-02 eta: 18:12:39 time: 0.4031 data_time: 0.0147 memory: 6717 grad_norm: 3.0508 loss: 2.3564 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.3564 2023/04/13 21:10:32 - mmengine - INFO - Epoch(train) [6][1760/1879] lr: 2.0000e-02 eta: 18:12:10 time: 0.3051 data_time: 0.0142 memory: 6717 grad_norm: 3.1253 loss: 2.2369 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2369 2023/04/13 21:10:41 - mmengine - INFO - Epoch(train) [6][1780/1879] lr: 2.0000e-02 eta: 18:12:22 time: 0.4307 data_time: 0.0140 memory: 6717 grad_norm: 3.1154 loss: 2.2873 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.2873 2023/04/13 21:10:47 - mmengine - INFO - Epoch(train) [6][1800/1879] lr: 2.0000e-02 eta: 18:11:50 time: 0.2926 data_time: 0.0138 memory: 6717 grad_norm: 3.1760 loss: 2.2249 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2249 2023/04/13 21:10:54 - mmengine - INFO - Epoch(train) [6][1820/1879] lr: 2.0000e-02 eta: 18:11:45 time: 0.3779 data_time: 0.0128 memory: 6717 grad_norm: 3.1269 loss: 2.3071 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.3071 2023/04/13 21:11:01 - mmengine - INFO - Epoch(train) [6][1840/1879] lr: 2.0000e-02 eta: 18:11:26 time: 0.3360 data_time: 0.0457 memory: 6717 grad_norm: 3.0561 loss: 2.3084 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 2.3084 2023/04/13 21:11:09 - mmengine - INFO - Epoch(train) [6][1860/1879] lr: 2.0000e-02 eta: 18:11:30 time: 0.4060 data_time: 0.0884 memory: 6717 grad_norm: 3.0827 loss: 2.2593 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.2593 2023/04/13 21:11:15 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 21:11:15 - mmengine - INFO - Epoch(train) [6][1879/1879] lr: 2.0000e-02 eta: 18:11:03 time: 0.2964 data_time: 0.0739 memory: 6717 grad_norm: 3.2092 loss: 2.3232 top1_acc: 0.2857 top5_acc: 0.5714 loss_cls: 2.3232 2023/04/13 21:11:15 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/04/13 21:11:24 - mmengine - INFO - Epoch(val) [6][ 20/155] eta: 0:00:56 time: 0.4170 data_time: 0.3839 memory: 1391 2023/04/13 21:11:30 - mmengine - INFO - Epoch(val) [6][ 40/155] eta: 0:00:42 time: 0.3203 data_time: 0.2869 memory: 1391 2023/04/13 21:11:39 - mmengine - INFO - Epoch(val) [6][ 60/155] eta: 0:00:37 time: 0.4374 data_time: 0.4045 memory: 1391 2023/04/13 21:11:45 - mmengine - INFO - Epoch(val) [6][ 80/155] eta: 0:00:27 time: 0.3159 data_time: 0.2829 memory: 1391 2023/04/13 21:11:54 - mmengine - INFO - Epoch(val) [6][100/155] eta: 0:00:21 time: 0.4493 data_time: 0.4163 memory: 1391 2023/04/13 21:12:00 - mmengine - INFO - Epoch(val) [6][120/155] eta: 0:00:13 time: 0.2956 data_time: 0.2626 memory: 1391 2023/04/13 21:12:09 - mmengine - INFO - Epoch(val) [6][140/155] eta: 0:00:05 time: 0.4746 data_time: 0.4417 memory: 1391 2023/04/13 21:12:21 - mmengine - INFO - Epoch(val) [6][155/155] acc/top1: 0.5032 acc/top5: 0.7678 acc/mean1: 0.5030 data_time: 0.4338 time: 0.4660 2023/04/13 21:12:21 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_5.pth is removed 2023/04/13 21:12:21 - mmengine - INFO - The best checkpoint with 0.5032 acc/top1 at 6 epoch is saved to best_acc_top1_epoch_6.pth. 2023/04/13 21:12:31 - mmengine - INFO - Epoch(train) [7][ 20/1879] lr: 2.0000e-02 eta: 18:11:27 time: 0.4707 data_time: 0.2970 memory: 6717 grad_norm: 3.1551 loss: 2.2473 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2473 2023/04/13 21:12:37 - mmengine - INFO - Epoch(train) [7][ 40/1879] lr: 2.0000e-02 eta: 18:11:03 time: 0.3172 data_time: 0.1243 memory: 6717 grad_norm: 3.0415 loss: 2.1471 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1471 2023/04/13 21:12:46 - mmengine - INFO - Epoch(train) [7][ 60/1879] lr: 2.0000e-02 eta: 18:11:14 time: 0.4297 data_time: 0.0224 memory: 6717 grad_norm: 3.1090 loss: 2.1477 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1477 2023/04/13 21:12:52 - mmengine - INFO - Epoch(train) [7][ 80/1879] lr: 2.0000e-02 eta: 18:10:52 time: 0.3228 data_time: 0.0126 memory: 6717 grad_norm: 3.1430 loss: 2.2471 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 2.2471 2023/04/13 21:13:00 - mmengine - INFO - Epoch(train) [7][ 100/1879] lr: 2.0000e-02 eta: 18:10:56 time: 0.4080 data_time: 0.0146 memory: 6717 grad_norm: 3.2449 loss: 2.2385 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 2.2385 2023/04/13 21:13:07 - mmengine - INFO - Epoch(train) [7][ 120/1879] lr: 2.0000e-02 eta: 18:10:36 time: 0.3303 data_time: 0.0122 memory: 6717 grad_norm: 3.1584 loss: 2.1778 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1778 2023/04/13 21:13:15 - mmengine - INFO - Epoch(train) [7][ 140/1879] lr: 2.0000e-02 eta: 18:10:37 time: 0.3972 data_time: 0.0146 memory: 6717 grad_norm: 3.1368 loss: 2.2084 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2084 2023/04/13 21:13:22 - mmengine - INFO - Epoch(train) [7][ 160/1879] lr: 2.0000e-02 eta: 18:10:20 time: 0.3409 data_time: 0.0131 memory: 6717 grad_norm: 3.0998 loss: 2.3982 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3982 2023/04/13 21:13:30 - mmengine - INFO - Epoch(train) [7][ 180/1879] lr: 2.0000e-02 eta: 18:10:24 time: 0.4077 data_time: 0.0193 memory: 6717 grad_norm: 3.0730 loss: 2.2261 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2261 2023/04/13 21:13:36 - mmengine - INFO - Epoch(train) [7][ 200/1879] lr: 2.0000e-02 eta: 18:09:55 time: 0.3002 data_time: 0.0122 memory: 6717 grad_norm: 3.0939 loss: 2.1606 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1606 2023/04/13 21:13:44 - mmengine - INFO - Epoch(train) [7][ 220/1879] lr: 2.0000e-02 eta: 18:09:57 time: 0.4010 data_time: 0.0158 memory: 6717 grad_norm: 3.2400 loss: 2.3941 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.3941 2023/04/13 21:13:50 - mmengine - INFO - Epoch(train) [7][ 240/1879] lr: 2.0000e-02 eta: 18:09:30 time: 0.3077 data_time: 0.0119 memory: 6717 grad_norm: 3.0865 loss: 2.3261 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 2.3261 2023/04/13 21:13:59 - mmengine - INFO - Epoch(train) [7][ 260/1879] lr: 2.0000e-02 eta: 18:09:41 time: 0.4301 data_time: 0.0136 memory: 6717 grad_norm: 3.0258 loss: 2.2513 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.2513 2023/04/13 21:14:06 - mmengine - INFO - Epoch(train) [7][ 280/1879] lr: 2.0000e-02 eta: 18:09:28 time: 0.3534 data_time: 0.0132 memory: 6717 grad_norm: 3.1111 loss: 2.3418 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.3418 2023/04/13 21:14:13 - mmengine - INFO - Epoch(train) [7][ 300/1879] lr: 2.0000e-02 eta: 18:09:24 time: 0.3819 data_time: 0.0161 memory: 6717 grad_norm: 3.0577 loss: 2.2134 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.2134 2023/04/13 21:14:20 - mmengine - INFO - Epoch(train) [7][ 320/1879] lr: 2.0000e-02 eta: 18:09:06 time: 0.3364 data_time: 0.0120 memory: 6717 grad_norm: 3.0458 loss: 2.1252 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.1252 2023/04/13 21:14:28 - mmengine - INFO - Epoch(train) [7][ 340/1879] lr: 2.0000e-02 eta: 18:09:09 time: 0.4055 data_time: 0.0149 memory: 6717 grad_norm: 3.1170 loss: 2.2692 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2692 2023/04/13 21:14:35 - mmengine - INFO - Epoch(train) [7][ 360/1879] lr: 2.0000e-02 eta: 18:08:55 time: 0.3493 data_time: 0.0124 memory: 6717 grad_norm: 3.0949 loss: 2.2408 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.2408 2023/04/13 21:14:43 - mmengine - INFO - Epoch(train) [7][ 380/1879] lr: 2.0000e-02 eta: 18:08:59 time: 0.4087 data_time: 0.0137 memory: 6717 grad_norm: 3.1478 loss: 2.0860 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0860 2023/04/13 21:14:49 - mmengine - INFO - Epoch(train) [7][ 400/1879] lr: 2.0000e-02 eta: 18:08:32 time: 0.3044 data_time: 0.0140 memory: 6717 grad_norm: 3.0326 loss: 2.1670 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1670 2023/04/13 21:14:58 - mmengine - INFO - Epoch(train) [7][ 420/1879] lr: 2.0000e-02 eta: 18:08:40 time: 0.4231 data_time: 0.0133 memory: 6717 grad_norm: 3.0918 loss: 2.3719 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.3719 2023/04/13 21:15:04 - mmengine - INFO - Epoch(train) [7][ 440/1879] lr: 2.0000e-02 eta: 18:08:18 time: 0.3223 data_time: 0.0135 memory: 6717 grad_norm: 3.1446 loss: 2.1825 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1825 2023/04/13 21:15:12 - mmengine - INFO - Epoch(train) [7][ 460/1879] lr: 2.0000e-02 eta: 18:08:23 time: 0.4103 data_time: 0.0137 memory: 6717 grad_norm: 3.1871 loss: 2.1166 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1166 2023/04/13 21:15:20 - mmengine - INFO - Epoch(train) [7][ 480/1879] lr: 2.0000e-02 eta: 18:08:16 time: 0.3726 data_time: 0.0132 memory: 6717 grad_norm: 3.1368 loss: 2.3774 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.3774 2023/04/13 21:15:27 - mmengine - INFO - Epoch(train) [7][ 500/1879] lr: 2.0000e-02 eta: 18:07:57 time: 0.3319 data_time: 0.0142 memory: 6717 grad_norm: 3.1365 loss: 2.1911 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.1911 2023/04/13 21:15:34 - mmengine - INFO - Epoch(train) [7][ 520/1879] lr: 2.0000e-02 eta: 18:07:54 time: 0.3863 data_time: 0.0139 memory: 6717 grad_norm: 3.0666 loss: 2.4173 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4173 2023/04/13 21:15:41 - mmengine - INFO - Epoch(train) [7][ 540/1879] lr: 2.0000e-02 eta: 18:07:40 time: 0.3468 data_time: 0.0136 memory: 6717 grad_norm: 3.0576 loss: 2.2707 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2707 2023/04/13 21:15:49 - mmengine - INFO - Epoch(train) [7][ 560/1879] lr: 2.0000e-02 eta: 18:07:35 time: 0.3807 data_time: 0.0137 memory: 6717 grad_norm: 3.1088 loss: 2.1913 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1913 2023/04/13 21:15:57 - mmengine - INFO - Epoch(train) [7][ 580/1879] lr: 2.0000e-02 eta: 18:07:36 time: 0.3970 data_time: 0.0128 memory: 6717 grad_norm: 3.1028 loss: 2.2750 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.2750 2023/04/13 21:16:04 - mmengine - INFO - Epoch(train) [7][ 600/1879] lr: 2.0000e-02 eta: 18:07:27 time: 0.3666 data_time: 0.0138 memory: 6717 grad_norm: 3.0144 loss: 2.2473 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.2473 2023/04/13 21:16:12 - mmengine - INFO - Epoch(train) [7][ 620/1879] lr: 2.0000e-02 eta: 18:07:20 time: 0.3713 data_time: 0.0144 memory: 6717 grad_norm: 3.1259 loss: 2.2202 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2202 2023/04/13 21:16:19 - mmengine - INFO - Epoch(train) [7][ 640/1879] lr: 2.0000e-02 eta: 18:07:17 time: 0.3857 data_time: 0.0127 memory: 6717 grad_norm: 3.1049 loss: 2.1188 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1188 2023/04/13 21:16:26 - mmengine - INFO - Epoch(train) [7][ 660/1879] lr: 2.0000e-02 eta: 18:07:02 time: 0.3465 data_time: 0.0154 memory: 6717 grad_norm: 3.1616 loss: 2.1817 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1817 2023/04/13 21:16:34 - mmengine - INFO - Epoch(train) [7][ 680/1879] lr: 2.0000e-02 eta: 18:07:01 time: 0.3902 data_time: 0.0131 memory: 6717 grad_norm: 3.1447 loss: 2.3262 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3262 2023/04/13 21:16:40 - mmengine - INFO - Epoch(train) [7][ 700/1879] lr: 2.0000e-02 eta: 18:06:38 time: 0.3174 data_time: 0.0142 memory: 6717 grad_norm: 2.9689 loss: 2.1730 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1730 2023/04/13 21:16:49 - mmengine - INFO - Epoch(train) [7][ 720/1879] lr: 2.0000e-02 eta: 18:06:52 time: 0.4452 data_time: 0.0143 memory: 6717 grad_norm: 3.1005 loss: 2.1863 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1863 2023/04/13 21:16:50 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 21:16:55 - mmengine - INFO - Epoch(train) [7][ 740/1879] lr: 2.0000e-02 eta: 18:06:21 time: 0.2891 data_time: 0.0149 memory: 6717 grad_norm: 3.0735 loss: 2.3712 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.3712 2023/04/13 21:17:03 - mmengine - INFO - Epoch(train) [7][ 760/1879] lr: 2.0000e-02 eta: 18:06:23 time: 0.4042 data_time: 0.0129 memory: 6717 grad_norm: 3.0946 loss: 2.1890 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1890 2023/04/13 21:17:10 - mmengine - INFO - Epoch(train) [7][ 780/1879] lr: 2.0000e-02 eta: 18:06:06 time: 0.3378 data_time: 0.0130 memory: 6717 grad_norm: 3.1452 loss: 2.2055 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2055 2023/04/13 21:17:18 - mmengine - INFO - Epoch(train) [7][ 800/1879] lr: 2.0000e-02 eta: 18:06:10 time: 0.4101 data_time: 0.0136 memory: 6717 grad_norm: 3.0874 loss: 2.3707 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3707 2023/04/13 21:17:25 - mmengine - INFO - Epoch(train) [7][ 820/1879] lr: 2.0000e-02 eta: 18:05:50 time: 0.3266 data_time: 0.0143 memory: 6717 grad_norm: 3.0393 loss: 2.3429 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3429 2023/04/13 21:17:32 - mmengine - INFO - Epoch(train) [7][ 840/1879] lr: 2.0000e-02 eta: 18:05:39 time: 0.3569 data_time: 0.0139 memory: 6717 grad_norm: 3.1369 loss: 2.2288 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.2288 2023/04/13 21:17:40 - mmengine - INFO - Epoch(train) [7][ 860/1879] lr: 2.0000e-02 eta: 18:05:42 time: 0.4082 data_time: 0.0139 memory: 6717 grad_norm: 3.1409 loss: 2.0585 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0585 2023/04/13 21:17:46 - mmengine - INFO - Epoch(train) [7][ 880/1879] lr: 2.0000e-02 eta: 18:05:22 time: 0.3246 data_time: 0.0164 memory: 6717 grad_norm: 3.0892 loss: 2.3286 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.3286 2023/04/13 21:17:54 - mmengine - INFO - Epoch(train) [7][ 900/1879] lr: 2.0000e-02 eta: 18:05:24 time: 0.4053 data_time: 0.0122 memory: 6717 grad_norm: 3.0105 loss: 2.1362 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1362 2023/04/13 21:18:01 - mmengine - INFO - Epoch(train) [7][ 920/1879] lr: 2.0000e-02 eta: 18:04:58 time: 0.3045 data_time: 0.0154 memory: 6717 grad_norm: 3.1048 loss: 2.3039 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.3039 2023/04/13 21:18:09 - mmengine - INFO - Epoch(train) [7][ 940/1879] lr: 2.0000e-02 eta: 18:05:01 time: 0.4085 data_time: 0.0130 memory: 6717 grad_norm: 3.2142 loss: 2.2750 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.2750 2023/04/13 21:18:15 - mmengine - INFO - Epoch(train) [7][ 960/1879] lr: 2.0000e-02 eta: 18:04:40 time: 0.3228 data_time: 0.0144 memory: 6717 grad_norm: 3.1296 loss: 2.2944 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2944 2023/04/13 21:18:23 - mmengine - INFO - Epoch(train) [7][ 980/1879] lr: 2.0000e-02 eta: 18:04:45 time: 0.4122 data_time: 0.0122 memory: 6717 grad_norm: 3.0795 loss: 2.3291 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.3291 2023/04/13 21:18:30 - mmengine - INFO - Epoch(train) [7][1000/1879] lr: 2.0000e-02 eta: 18:04:26 time: 0.3321 data_time: 0.0153 memory: 6717 grad_norm: 3.0537 loss: 2.0826 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0826 2023/04/13 21:18:38 - mmengine - INFO - Epoch(train) [7][1020/1879] lr: 2.0000e-02 eta: 18:04:22 time: 0.3831 data_time: 0.0117 memory: 6717 grad_norm: 2.9636 loss: 2.1805 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1805 2023/04/13 21:18:45 - mmengine - INFO - Epoch(train) [7][1040/1879] lr: 2.0000e-02 eta: 18:04:10 time: 0.3545 data_time: 0.0300 memory: 6717 grad_norm: 3.0563 loss: 2.3412 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.3412 2023/04/13 21:18:52 - mmengine - INFO - Epoch(train) [7][1060/1879] lr: 2.0000e-02 eta: 18:04:06 time: 0.3795 data_time: 0.0207 memory: 6717 grad_norm: 3.0926 loss: 2.0464 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.0464 2023/04/13 21:18:59 - mmengine - INFO - Epoch(train) [7][1080/1879] lr: 2.0000e-02 eta: 18:03:52 time: 0.3468 data_time: 0.0422 memory: 6717 grad_norm: 3.0894 loss: 2.1670 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1670 2023/04/13 21:19:07 - mmengine - INFO - Epoch(train) [7][1100/1879] lr: 2.0000e-02 eta: 18:03:49 time: 0.3880 data_time: 0.0118 memory: 6717 grad_norm: 3.2821 loss: 2.1250 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1250 2023/04/13 21:19:14 - mmengine - INFO - Epoch(train) [7][1120/1879] lr: 2.0000e-02 eta: 18:03:34 time: 0.3447 data_time: 0.0319 memory: 6717 grad_norm: 3.0887 loss: 2.1085 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.1085 2023/04/13 21:19:22 - mmengine - INFO - Epoch(train) [7][1140/1879] lr: 2.0000e-02 eta: 18:03:35 time: 0.3973 data_time: 0.0915 memory: 6717 grad_norm: 3.1211 loss: 2.0715 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0715 2023/04/13 21:19:29 - mmengine - INFO - Epoch(train) [7][1160/1879] lr: 2.0000e-02 eta: 18:03:23 time: 0.3561 data_time: 0.0867 memory: 6717 grad_norm: 3.1123 loss: 2.3954 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3954 2023/04/13 21:19:37 - mmengine - INFO - Epoch(train) [7][1180/1879] lr: 2.0000e-02 eta: 18:03:19 time: 0.3834 data_time: 0.0838 memory: 6717 grad_norm: 3.0737 loss: 2.2957 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2957 2023/04/13 21:19:45 - mmengine - INFO - Epoch(train) [7][1200/1879] lr: 2.0000e-02 eta: 18:03:20 time: 0.4005 data_time: 0.1751 memory: 6717 grad_norm: 3.0998 loss: 2.2325 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.2325 2023/04/13 21:19:52 - mmengine - INFO - Epoch(train) [7][1220/1879] lr: 2.0000e-02 eta: 18:03:04 time: 0.3381 data_time: 0.1406 memory: 6717 grad_norm: 3.0542 loss: 2.1445 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1445 2023/04/13 21:20:00 - mmengine - INFO - Epoch(train) [7][1240/1879] lr: 2.0000e-02 eta: 18:03:07 time: 0.4088 data_time: 0.2445 memory: 6717 grad_norm: 3.0168 loss: 2.3088 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3088 2023/04/13 21:20:06 - mmengine - INFO - Epoch(train) [7][1260/1879] lr: 2.0000e-02 eta: 18:02:43 time: 0.3125 data_time: 0.1643 memory: 6717 grad_norm: 3.0814 loss: 2.2718 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.2718 2023/04/13 21:20:15 - mmengine - INFO - Epoch(train) [7][1280/1879] lr: 2.0000e-02 eta: 18:02:57 time: 0.4451 data_time: 0.3024 memory: 6717 grad_norm: 3.1509 loss: 2.2224 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2224 2023/04/13 21:20:21 - mmengine - INFO - Epoch(train) [7][1300/1879] lr: 2.0000e-02 eta: 18:02:36 time: 0.3222 data_time: 0.1793 memory: 6717 grad_norm: 3.0986 loss: 2.3186 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.3186 2023/04/13 21:20:29 - mmengine - INFO - Epoch(train) [7][1320/1879] lr: 2.0000e-02 eta: 18:02:34 time: 0.3884 data_time: 0.2451 memory: 6717 grad_norm: 3.0149 loss: 2.1143 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1143 2023/04/13 21:20:36 - mmengine - INFO - Epoch(train) [7][1340/1879] lr: 2.0000e-02 eta: 18:02:18 time: 0.3419 data_time: 0.2004 memory: 6717 grad_norm: 3.0897 loss: 2.2732 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2732 2023/04/13 21:20:45 - mmengine - INFO - Epoch(train) [7][1360/1879] lr: 2.0000e-02 eta: 18:02:29 time: 0.4353 data_time: 0.2931 memory: 6717 grad_norm: 3.0322 loss: 2.3463 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.3463 2023/04/13 21:20:51 - mmengine - INFO - Epoch(train) [7][1380/1879] lr: 2.0000e-02 eta: 18:02:05 time: 0.3098 data_time: 0.1698 memory: 6717 grad_norm: 3.1340 loss: 2.2864 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2864 2023/04/13 21:20:58 - mmengine - INFO - Epoch(train) [7][1400/1879] lr: 2.0000e-02 eta: 18:01:57 time: 0.3692 data_time: 0.2277 memory: 6717 grad_norm: 3.0391 loss: 2.2065 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.2065 2023/04/13 21:21:05 - mmengine - INFO - Epoch(train) [7][1420/1879] lr: 2.0000e-02 eta: 18:01:36 time: 0.3234 data_time: 0.1841 memory: 6717 grad_norm: 3.0257 loss: 2.2215 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.2215 2023/04/13 21:21:13 - mmengine - INFO - Epoch(train) [7][1440/1879] lr: 2.0000e-02 eta: 18:01:44 time: 0.4238 data_time: 0.2770 memory: 6717 grad_norm: 3.0710 loss: 2.1167 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1167 2023/04/13 21:21:20 - mmengine - INFO - Epoch(train) [7][1460/1879] lr: 2.0000e-02 eta: 18:01:24 time: 0.3250 data_time: 0.1843 memory: 6717 grad_norm: 3.0033 loss: 2.1243 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1243 2023/04/13 21:21:27 - mmengine - INFO - Epoch(train) [7][1480/1879] lr: 2.0000e-02 eta: 18:01:19 time: 0.3790 data_time: 0.2383 memory: 6717 grad_norm: 3.0777 loss: 2.3420 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3420 2023/04/13 21:21:34 - mmengine - INFO - Epoch(train) [7][1500/1879] lr: 2.0000e-02 eta: 18:01:01 time: 0.3328 data_time: 0.1927 memory: 6717 grad_norm: 3.1251 loss: 2.3705 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.3705 2023/04/13 21:21:42 - mmengine - INFO - Epoch(train) [7][1520/1879] lr: 2.0000e-02 eta: 18:01:06 time: 0.4168 data_time: 0.2769 memory: 6717 grad_norm: 3.0462 loss: 2.1990 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1990 2023/04/13 21:21:49 - mmengine - INFO - Epoch(train) [7][1540/1879] lr: 2.0000e-02 eta: 18:00:49 time: 0.3346 data_time: 0.1955 memory: 6717 grad_norm: 3.0639 loss: 2.1001 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1001 2023/04/13 21:21:57 - mmengine - INFO - Epoch(train) [7][1560/1879] lr: 2.0000e-02 eta: 18:00:50 time: 0.3991 data_time: 0.2652 memory: 6717 grad_norm: 3.0086 loss: 2.1244 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1244 2023/04/13 21:22:03 - mmengine - INFO - Epoch(train) [7][1580/1879] lr: 2.0000e-02 eta: 18:00:19 time: 0.2842 data_time: 0.1407 memory: 6717 grad_norm: 3.1090 loss: 2.3412 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3412 2023/04/13 21:22:11 - mmengine - INFO - Epoch(train) [7][1600/1879] lr: 2.0000e-02 eta: 18:00:23 time: 0.4122 data_time: 0.2126 memory: 6717 grad_norm: 3.0916 loss: 2.2180 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.2180 2023/04/13 21:22:17 - mmengine - INFO - Epoch(train) [7][1620/1879] lr: 2.0000e-02 eta: 18:00:03 time: 0.3266 data_time: 0.0696 memory: 6717 grad_norm: 3.0853 loss: 2.1902 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1902 2023/04/13 21:22:26 - mmengine - INFO - Epoch(train) [7][1640/1879] lr: 2.0000e-02 eta: 18:00:19 time: 0.4537 data_time: 0.1456 memory: 6717 grad_norm: 3.0197 loss: 2.1314 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1314 2023/04/13 21:22:33 - mmengine - INFO - Epoch(train) [7][1660/1879] lr: 2.0000e-02 eta: 17:59:56 time: 0.3154 data_time: 0.0857 memory: 6717 grad_norm: 3.0527 loss: 2.2077 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.2077 2023/04/13 21:22:41 - mmengine - INFO - Epoch(train) [7][1680/1879] lr: 2.0000e-02 eta: 17:59:59 time: 0.4092 data_time: 0.1011 memory: 6717 grad_norm: 3.0900 loss: 2.1254 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1254 2023/04/13 21:22:47 - mmengine - INFO - Epoch(train) [7][1700/1879] lr: 2.0000e-02 eta: 17:59:37 time: 0.3138 data_time: 0.0838 memory: 6717 grad_norm: 3.1484 loss: 2.2064 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2064 2023/04/13 21:22:55 - mmengine - INFO - Epoch(train) [7][1720/1879] lr: 2.0000e-02 eta: 17:59:39 time: 0.4058 data_time: 0.0833 memory: 6717 grad_norm: 3.1031 loss: 2.2176 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.2176 2023/04/13 21:22:56 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 21:23:02 - mmengine - INFO - Epoch(train) [7][1740/1879] lr: 2.0000e-02 eta: 17:59:21 time: 0.3304 data_time: 0.1291 memory: 6717 grad_norm: 3.0616 loss: 2.0272 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0272 2023/04/13 21:23:10 - mmengine - INFO - Epoch(train) [7][1760/1879] lr: 2.0000e-02 eta: 17:59:28 time: 0.4261 data_time: 0.2423 memory: 6717 grad_norm: 3.0874 loss: 2.3301 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 2.3301 2023/04/13 21:23:17 - mmengine - INFO - Epoch(train) [7][1780/1879] lr: 2.0000e-02 eta: 17:59:06 time: 0.3138 data_time: 0.0610 memory: 6717 grad_norm: 3.0722 loss: 2.1714 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1714 2023/04/13 21:23:25 - mmengine - INFO - Epoch(train) [7][1800/1879] lr: 2.0000e-02 eta: 17:59:08 time: 0.4051 data_time: 0.0753 memory: 6717 grad_norm: 3.1391 loss: 2.1653 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 2.1653 2023/04/13 21:23:32 - mmengine - INFO - Epoch(train) [7][1820/1879] lr: 2.0000e-02 eta: 17:58:51 time: 0.3367 data_time: 0.1059 memory: 6717 grad_norm: 3.0228 loss: 2.2844 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2844 2023/04/13 21:23:39 - mmengine - INFO - Epoch(train) [7][1840/1879] lr: 2.0000e-02 eta: 17:58:47 time: 0.3838 data_time: 0.1242 memory: 6717 grad_norm: 3.0766 loss: 2.0329 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0329 2023/04/13 21:23:46 - mmengine - INFO - Epoch(train) [7][1860/1879] lr: 2.0000e-02 eta: 17:58:29 time: 0.3281 data_time: 0.1502 memory: 6717 grad_norm: 3.0101 loss: 2.1323 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1323 2023/04/13 21:23:52 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 21:23:52 - mmengine - INFO - Epoch(train) [7][1879/1879] lr: 2.0000e-02 eta: 17:58:08 time: 0.3072 data_time: 0.1789 memory: 6717 grad_norm: 3.1582 loss: 2.2042 top1_acc: 0.2857 top5_acc: 0.5714 loss_cls: 2.2042 2023/04/13 21:24:01 - mmengine - INFO - Epoch(val) [7][ 20/155] eta: 0:01:01 time: 0.4581 data_time: 0.4250 memory: 1391 2023/04/13 21:24:07 - mmengine - INFO - Epoch(val) [7][ 40/155] eta: 0:00:43 time: 0.3069 data_time: 0.2741 memory: 1391 2023/04/13 21:24:16 - mmengine - INFO - Epoch(val) [7][ 60/155] eta: 0:00:38 time: 0.4390 data_time: 0.4062 memory: 1391 2023/04/13 21:24:22 - mmengine - INFO - Epoch(val) [7][ 80/155] eta: 0:00:28 time: 0.3131 data_time: 0.2713 memory: 1391 2023/04/13 21:24:31 - mmengine - INFO - Epoch(val) [7][100/155] eta: 0:00:21 time: 0.4265 data_time: 0.3940 memory: 1391 2023/04/13 21:24:37 - mmengine - INFO - Epoch(val) [7][120/155] eta: 0:00:13 time: 0.3388 data_time: 0.3062 memory: 1391 2023/04/13 21:24:47 - mmengine - INFO - Epoch(val) [7][140/155] eta: 0:00:05 time: 0.4850 data_time: 0.4524 memory: 1391 2023/04/13 21:24:54 - mmengine - INFO - Epoch(val) [7][155/155] acc/top1: 0.5165 acc/top5: 0.7759 acc/mean1: 0.5163 data_time: 0.4234 time: 0.4547 2023/04/13 21:24:54 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_6.pth is removed 2023/04/13 21:24:55 - mmengine - INFO - The best checkpoint with 0.5165 acc/top1 at 7 epoch is saved to best_acc_top1_epoch_7.pth. 2023/04/13 21:25:05 - mmengine - INFO - Epoch(train) [8][ 20/1879] lr: 2.0000e-02 eta: 17:58:33 time: 0.4913 data_time: 0.3341 memory: 6717 grad_norm: 3.0536 loss: 2.3269 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.3269 2023/04/13 21:25:11 - mmengine - INFO - Epoch(train) [8][ 40/1879] lr: 2.0000e-02 eta: 17:58:16 time: 0.3358 data_time: 0.1981 memory: 6717 grad_norm: 3.0212 loss: 2.3447 top1_acc: 0.4375 top5_acc: 0.5000 loss_cls: 2.3447 2023/04/13 21:25:20 - mmengine - INFO - Epoch(train) [8][ 60/1879] lr: 2.0000e-02 eta: 17:58:21 time: 0.4164 data_time: 0.2116 memory: 6717 grad_norm: 3.0886 loss: 2.1791 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1791 2023/04/13 21:25:26 - mmengine - INFO - Epoch(train) [8][ 80/1879] lr: 2.0000e-02 eta: 17:58:02 time: 0.3265 data_time: 0.0758 memory: 6717 grad_norm: 3.0799 loss: 2.3240 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.3240 2023/04/13 21:25:35 - mmengine - INFO - Epoch(train) [8][ 100/1879] lr: 2.0000e-02 eta: 17:58:09 time: 0.4249 data_time: 0.0582 memory: 6717 grad_norm: 3.1148 loss: 2.1888 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.1888 2023/04/13 21:25:41 - mmengine - INFO - Epoch(train) [8][ 120/1879] lr: 2.0000e-02 eta: 17:57:48 time: 0.3170 data_time: 0.0289 memory: 6717 grad_norm: 3.0894 loss: 2.3212 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.3212 2023/04/13 21:25:49 - mmengine - INFO - Epoch(train) [8][ 140/1879] lr: 2.0000e-02 eta: 17:57:45 time: 0.3897 data_time: 0.0365 memory: 6717 grad_norm: 3.0883 loss: 2.2052 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.2052 2023/04/13 21:25:55 - mmengine - INFO - Epoch(train) [8][ 160/1879] lr: 2.0000e-02 eta: 17:57:24 time: 0.3186 data_time: 0.0125 memory: 6717 grad_norm: 3.0889 loss: 2.0180 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0180 2023/04/13 21:26:04 - mmengine - INFO - Epoch(train) [8][ 180/1879] lr: 2.0000e-02 eta: 17:57:32 time: 0.4285 data_time: 0.0153 memory: 6717 grad_norm: 3.1115 loss: 2.3276 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.3276 2023/04/13 21:26:10 - mmengine - INFO - Epoch(train) [8][ 200/1879] lr: 2.0000e-02 eta: 17:57:16 time: 0.3380 data_time: 0.0129 memory: 6717 grad_norm: 3.1098 loss: 2.1370 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1370 2023/04/13 21:26:19 - mmengine - INFO - Epoch(train) [8][ 220/1879] lr: 2.0000e-02 eta: 17:57:27 time: 0.4404 data_time: 0.0137 memory: 6717 grad_norm: 3.1594 loss: 2.0043 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.0043 2023/04/13 21:26:26 - mmengine - INFO - Epoch(train) [8][ 240/1879] lr: 2.0000e-02 eta: 17:57:07 time: 0.3229 data_time: 0.0130 memory: 6717 grad_norm: 3.1317 loss: 2.1225 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1225 2023/04/13 21:26:33 - mmengine - INFO - Epoch(train) [8][ 260/1879] lr: 2.0000e-02 eta: 17:56:54 time: 0.3491 data_time: 0.0241 memory: 6717 grad_norm: 3.0558 loss: 2.2074 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 2.2074 2023/04/13 21:26:40 - mmengine - INFO - Epoch(train) [8][ 280/1879] lr: 2.0000e-02 eta: 17:56:39 time: 0.3392 data_time: 0.0152 memory: 6717 grad_norm: 3.1328 loss: 1.9973 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9973 2023/04/13 21:26:48 - mmengine - INFO - Epoch(train) [8][ 300/1879] lr: 2.0000e-02 eta: 17:56:41 time: 0.4066 data_time: 0.0133 memory: 6717 grad_norm: 3.0536 loss: 2.2852 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2852 2023/04/13 21:26:54 - mmengine - INFO - Epoch(train) [8][ 320/1879] lr: 2.0000e-02 eta: 17:56:19 time: 0.3156 data_time: 0.0136 memory: 6717 grad_norm: 3.0900 loss: 2.2315 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.2315 2023/04/13 21:27:02 - mmengine - INFO - Epoch(train) [8][ 340/1879] lr: 2.0000e-02 eta: 17:56:26 time: 0.4241 data_time: 0.0136 memory: 6717 grad_norm: 3.1860 loss: 2.2083 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.2083 2023/04/13 21:27:09 - mmengine - INFO - Epoch(train) [8][ 360/1879] lr: 2.0000e-02 eta: 17:56:10 time: 0.3368 data_time: 0.0131 memory: 6717 grad_norm: 3.2841 loss: 2.1757 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.1757 2023/04/13 21:27:17 - mmengine - INFO - Epoch(train) [8][ 380/1879] lr: 2.0000e-02 eta: 17:56:10 time: 0.3992 data_time: 0.0154 memory: 6717 grad_norm: 3.1232 loss: 2.3061 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.3061 2023/04/13 21:27:24 - mmengine - INFO - Epoch(train) [8][ 400/1879] lr: 2.0000e-02 eta: 17:55:55 time: 0.3414 data_time: 0.0137 memory: 6717 grad_norm: 3.1848 loss: 2.2335 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.2335 2023/04/13 21:27:32 - mmengine - INFO - Epoch(train) [8][ 420/1879] lr: 2.0000e-02 eta: 17:55:54 time: 0.3936 data_time: 0.0152 memory: 6717 grad_norm: 2.9531 loss: 2.1142 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1142 2023/04/13 21:27:39 - mmengine - INFO - Epoch(train) [8][ 440/1879] lr: 2.0000e-02 eta: 17:55:43 time: 0.3572 data_time: 0.0294 memory: 6717 grad_norm: 3.0695 loss: 2.4069 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.4069 2023/04/13 21:27:47 - mmengine - INFO - Epoch(train) [8][ 460/1879] lr: 2.0000e-02 eta: 17:55:42 time: 0.3973 data_time: 0.0347 memory: 6717 grad_norm: 3.0822 loss: 2.0824 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0824 2023/04/13 21:27:54 - mmengine - INFO - Epoch(train) [8][ 480/1879] lr: 2.0000e-02 eta: 17:55:29 time: 0.3459 data_time: 0.0595 memory: 6717 grad_norm: 3.1163 loss: 2.0827 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0827 2023/04/13 21:28:01 - mmengine - INFO - Epoch(train) [8][ 500/1879] lr: 2.0000e-02 eta: 17:55:18 time: 0.3591 data_time: 0.0445 memory: 6717 grad_norm: 3.0638 loss: 2.1965 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.1965 2023/04/13 21:28:08 - mmengine - INFO - Epoch(train) [8][ 520/1879] lr: 2.0000e-02 eta: 17:55:11 time: 0.3712 data_time: 0.1048 memory: 6717 grad_norm: 3.0270 loss: 2.0905 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0905 2023/04/13 21:28:15 - mmengine - INFO - Epoch(train) [8][ 540/1879] lr: 2.0000e-02 eta: 17:54:54 time: 0.3304 data_time: 0.0290 memory: 6717 grad_norm: 2.9988 loss: 2.1711 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1711 2023/04/13 21:28:23 - mmengine - INFO - Epoch(train) [8][ 560/1879] lr: 2.0000e-02 eta: 17:54:49 time: 0.3817 data_time: 0.0179 memory: 6717 grad_norm: 2.9897 loss: 1.9718 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9718 2023/04/13 21:28:30 - mmengine - INFO - Epoch(train) [8][ 580/1879] lr: 2.0000e-02 eta: 17:54:39 time: 0.3576 data_time: 0.0523 memory: 6717 grad_norm: 3.1132 loss: 2.1520 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.1520 2023/04/13 21:28:38 - mmengine - INFO - Epoch(train) [8][ 600/1879] lr: 2.0000e-02 eta: 17:54:42 time: 0.4120 data_time: 0.0326 memory: 6717 grad_norm: 3.0162 loss: 2.0641 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0641 2023/04/13 21:28:45 - mmengine - INFO - Epoch(train) [8][ 620/1879] lr: 2.0000e-02 eta: 17:54:31 time: 0.3569 data_time: 0.0133 memory: 6717 grad_norm: 2.9874 loss: 1.9932 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9932 2023/04/13 21:28:52 - mmengine - INFO - Epoch(train) [8][ 640/1879] lr: 2.0000e-02 eta: 17:54:21 time: 0.3588 data_time: 0.0146 memory: 6717 grad_norm: 3.1114 loss: 2.2260 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2260 2023/04/13 21:29:00 - mmengine - INFO - Epoch(train) [8][ 660/1879] lr: 2.0000e-02 eta: 17:54:17 time: 0.3853 data_time: 0.0532 memory: 6717 grad_norm: 3.1191 loss: 2.1604 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1604 2023/04/13 21:29:07 - mmengine - INFO - Epoch(train) [8][ 680/1879] lr: 2.0000e-02 eta: 17:53:59 time: 0.3298 data_time: 0.0202 memory: 6717 grad_norm: 3.0533 loss: 2.0705 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0705 2023/04/13 21:29:15 - mmengine - INFO - Epoch(train) [8][ 700/1879] lr: 2.0000e-02 eta: 17:54:01 time: 0.4074 data_time: 0.0450 memory: 6717 grad_norm: 3.0977 loss: 2.1558 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.1558 2023/04/13 21:29:21 - mmengine - INFO - Epoch(train) [8][ 720/1879] lr: 2.0000e-02 eta: 17:53:42 time: 0.3228 data_time: 0.0132 memory: 6717 grad_norm: 2.9638 loss: 2.0724 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0724 2023/04/13 21:29:30 - mmengine - INFO - Epoch(train) [8][ 740/1879] lr: 2.0000e-02 eta: 17:53:48 time: 0.4217 data_time: 0.0148 memory: 6717 grad_norm: 3.0216 loss: 2.0782 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0782 2023/04/13 21:29:36 - mmengine - INFO - Epoch(train) [8][ 760/1879] lr: 2.0000e-02 eta: 17:53:31 time: 0.3321 data_time: 0.0126 memory: 6717 grad_norm: 3.0695 loss: 2.2733 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.2733 2023/04/13 21:29:44 - mmengine - INFO - Epoch(train) [8][ 780/1879] lr: 2.0000e-02 eta: 17:53:24 time: 0.3711 data_time: 0.0150 memory: 6717 grad_norm: 3.0230 loss: 2.1241 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1241 2023/04/13 21:29:51 - mmengine - INFO - Epoch(train) [8][ 800/1879] lr: 2.0000e-02 eta: 17:53:12 time: 0.3539 data_time: 0.0722 memory: 6717 grad_norm: 3.1221 loss: 2.1759 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1759 2023/04/13 21:29:58 - mmengine - INFO - Epoch(train) [8][ 820/1879] lr: 2.0000e-02 eta: 17:53:02 time: 0.3590 data_time: 0.0312 memory: 6717 grad_norm: 3.1722 loss: 1.9520 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9520 2023/04/13 21:30:05 - mmengine - INFO - Epoch(train) [8][ 840/1879] lr: 2.0000e-02 eta: 17:52:45 time: 0.3335 data_time: 0.0616 memory: 6717 grad_norm: 3.0641 loss: 1.9265 top1_acc: 0.4375 top5_acc: 0.5000 loss_cls: 1.9265 2023/04/13 21:30:07 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 21:30:13 - mmengine - INFO - Epoch(train) [8][ 860/1879] lr: 2.0000e-02 eta: 17:52:46 time: 0.4037 data_time: 0.0535 memory: 6717 grad_norm: 2.9871 loss: 2.0754 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0754 2023/04/13 21:30:20 - mmengine - INFO - Epoch(train) [8][ 880/1879] lr: 2.0000e-02 eta: 17:52:30 time: 0.3334 data_time: 0.0219 memory: 6717 grad_norm: 3.1389 loss: 2.1716 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1716 2023/04/13 21:30:27 - mmengine - INFO - Epoch(train) [8][ 900/1879] lr: 2.0000e-02 eta: 17:52:27 time: 0.3901 data_time: 0.0775 memory: 6717 grad_norm: 3.1024 loss: 2.3832 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3832 2023/04/13 21:30:34 - mmengine - INFO - Epoch(train) [8][ 920/1879] lr: 2.0000e-02 eta: 17:52:11 time: 0.3343 data_time: 0.0476 memory: 6717 grad_norm: 3.0229 loss: 2.0611 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0611 2023/04/13 21:30:42 - mmengine - INFO - Epoch(train) [8][ 940/1879] lr: 2.0000e-02 eta: 17:52:16 time: 0.4206 data_time: 0.0254 memory: 6717 grad_norm: 3.0721 loss: 2.0759 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0759 2023/04/13 21:30:49 - mmengine - INFO - Epoch(train) [8][ 960/1879] lr: 2.0000e-02 eta: 17:52:00 time: 0.3366 data_time: 0.0139 memory: 6717 grad_norm: 3.0180 loss: 2.2087 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2087 2023/04/13 21:30:57 - mmengine - INFO - Epoch(train) [8][ 980/1879] lr: 2.0000e-02 eta: 17:51:58 time: 0.3923 data_time: 0.0135 memory: 6717 grad_norm: 3.0864 loss: 2.2703 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.2703 2023/04/13 21:31:04 - mmengine - INFO - Epoch(train) [8][1000/1879] lr: 2.0000e-02 eta: 17:51:45 time: 0.3443 data_time: 0.0146 memory: 6717 grad_norm: 3.1535 loss: 2.3151 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3151 2023/04/13 21:31:12 - mmengine - INFO - Epoch(train) [8][1020/1879] lr: 2.0000e-02 eta: 17:51:45 time: 0.4004 data_time: 0.0134 memory: 6717 grad_norm: 3.0794 loss: 2.2921 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2921 2023/04/13 21:31:19 - mmengine - INFO - Epoch(train) [8][1040/1879] lr: 2.0000e-02 eta: 17:51:35 time: 0.3586 data_time: 0.0138 memory: 6717 grad_norm: 3.1345 loss: 2.2735 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.2735 2023/04/13 21:31:26 - mmengine - INFO - Epoch(train) [8][1060/1879] lr: 2.0000e-02 eta: 17:51:18 time: 0.3315 data_time: 0.0144 memory: 6717 grad_norm: 3.2677 loss: 2.1617 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1617 2023/04/13 21:31:34 - mmengine - INFO - Epoch(train) [8][1080/1879] lr: 2.0000e-02 eta: 17:51:20 time: 0.4091 data_time: 0.0145 memory: 6717 grad_norm: 3.0296 loss: 2.0748 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0748 2023/04/13 21:31:41 - mmengine - INFO - Epoch(train) [8][1100/1879] lr: 2.0000e-02 eta: 17:51:09 time: 0.3570 data_time: 0.0131 memory: 6717 grad_norm: 3.0987 loss: 2.1850 top1_acc: 0.2500 top5_acc: 0.8125 loss_cls: 2.1850 2023/04/13 21:31:49 - mmengine - INFO - Epoch(train) [8][1120/1879] lr: 2.0000e-02 eta: 17:51:06 time: 0.3868 data_time: 0.0162 memory: 6717 grad_norm: 3.0314 loss: 2.0115 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0115 2023/04/13 21:31:55 - mmengine - INFO - Epoch(train) [8][1140/1879] lr: 2.0000e-02 eta: 17:50:45 time: 0.3131 data_time: 0.0132 memory: 6717 grad_norm: 3.0662 loss: 2.3596 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.3596 2023/04/13 21:32:03 - mmengine - INFO - Epoch(train) [8][1160/1879] lr: 2.0000e-02 eta: 17:50:49 time: 0.4177 data_time: 0.0144 memory: 6717 grad_norm: 3.0601 loss: 1.9874 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.9874 2023/04/13 21:32:09 - mmengine - INFO - Epoch(train) [8][1180/1879] lr: 2.0000e-02 eta: 17:50:24 time: 0.3002 data_time: 0.0125 memory: 6717 grad_norm: 3.0522 loss: 2.2555 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2555 2023/04/13 21:32:17 - mmengine - INFO - Epoch(train) [8][1200/1879] lr: 2.0000e-02 eta: 17:50:24 time: 0.4009 data_time: 0.0154 memory: 6717 grad_norm: 3.1189 loss: 2.3001 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.3001 2023/04/13 21:32:24 - mmengine - INFO - Epoch(train) [8][1220/1879] lr: 2.0000e-02 eta: 17:50:04 time: 0.3155 data_time: 0.0124 memory: 6717 grad_norm: 3.0332 loss: 2.3239 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.3239 2023/04/13 21:32:32 - mmengine - INFO - Epoch(train) [8][1240/1879] lr: 2.0000e-02 eta: 17:50:03 time: 0.3960 data_time: 0.0150 memory: 6717 grad_norm: 3.0598 loss: 2.3495 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.3495 2023/04/13 21:32:38 - mmengine - INFO - Epoch(train) [8][1260/1879] lr: 2.0000e-02 eta: 17:49:48 time: 0.3415 data_time: 0.0145 memory: 6717 grad_norm: 3.0441 loss: 2.3499 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3499 2023/04/13 21:32:47 - mmengine - INFO - Epoch(train) [8][1280/1879] lr: 2.0000e-02 eta: 17:49:52 time: 0.4139 data_time: 0.0128 memory: 6717 grad_norm: 3.0319 loss: 2.3398 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.3398 2023/04/13 21:32:53 - mmengine - INFO - Epoch(train) [8][1300/1879] lr: 2.0000e-02 eta: 17:49:30 time: 0.3092 data_time: 0.0156 memory: 6717 grad_norm: 3.0866 loss: 2.1733 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.1733 2023/04/13 21:33:02 - mmengine - INFO - Epoch(train) [8][1320/1879] lr: 2.0000e-02 eta: 17:49:46 time: 0.4698 data_time: 0.0134 memory: 6717 grad_norm: 3.0425 loss: 2.1703 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.1703 2023/04/13 21:33:08 - mmengine - INFO - Epoch(train) [8][1340/1879] lr: 2.0000e-02 eta: 17:49:20 time: 0.2896 data_time: 0.0150 memory: 6717 grad_norm: 3.0526 loss: 2.1397 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1397 2023/04/13 21:33:16 - mmengine - INFO - Epoch(train) [8][1360/1879] lr: 2.0000e-02 eta: 17:49:22 time: 0.4121 data_time: 0.0144 memory: 6717 grad_norm: 3.0663 loss: 2.1263 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.1263 2023/04/13 21:33:23 - mmengine - INFO - Epoch(train) [8][1380/1879] lr: 2.0000e-02 eta: 17:49:04 time: 0.3261 data_time: 0.0120 memory: 6717 grad_norm: 3.0104 loss: 1.8928 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8928 2023/04/13 21:33:31 - mmengine - INFO - Epoch(train) [8][1400/1879] lr: 2.0000e-02 eta: 17:49:00 time: 0.3818 data_time: 0.0150 memory: 6717 grad_norm: 3.0555 loss: 2.1486 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1486 2023/04/13 21:33:37 - mmengine - INFO - Epoch(train) [8][1420/1879] lr: 2.0000e-02 eta: 17:48:38 time: 0.3095 data_time: 0.0135 memory: 6717 grad_norm: 3.0047 loss: 2.2140 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.2140 2023/04/13 21:33:46 - mmengine - INFO - Epoch(train) [8][1440/1879] lr: 2.0000e-02 eta: 17:48:50 time: 0.4539 data_time: 0.0155 memory: 6717 grad_norm: 3.0306 loss: 2.1892 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1892 2023/04/13 21:33:53 - mmengine - INFO - Epoch(train) [8][1460/1879] lr: 2.0000e-02 eta: 17:48:35 time: 0.3359 data_time: 0.0128 memory: 6717 grad_norm: 2.9997 loss: 2.2722 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.2722 2023/04/13 21:34:01 - mmengine - INFO - Epoch(train) [8][1480/1879] lr: 2.0000e-02 eta: 17:48:39 time: 0.4164 data_time: 0.0164 memory: 6717 grad_norm: 3.0206 loss: 2.0125 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0125 2023/04/13 21:34:07 - mmengine - INFO - Epoch(train) [8][1500/1879] lr: 2.0000e-02 eta: 17:48:16 time: 0.3064 data_time: 0.0130 memory: 6717 grad_norm: 3.0786 loss: 2.3272 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.3272 2023/04/13 21:34:14 - mmengine - INFO - Epoch(train) [8][1520/1879] lr: 2.0000e-02 eta: 17:48:08 time: 0.3651 data_time: 0.0147 memory: 6717 grad_norm: 3.0709 loss: 2.1686 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1686 2023/04/13 21:34:21 - mmengine - INFO - Epoch(train) [8][1540/1879] lr: 2.0000e-02 eta: 17:47:54 time: 0.3428 data_time: 0.0139 memory: 6717 grad_norm: 2.9238 loss: 2.1659 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.1659 2023/04/13 21:34:29 - mmengine - INFO - Epoch(train) [8][1560/1879] lr: 2.0000e-02 eta: 17:47:55 time: 0.4069 data_time: 0.0153 memory: 6717 grad_norm: 3.1203 loss: 2.3759 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.3759 2023/04/13 21:34:36 - mmengine - INFO - Epoch(train) [8][1580/1879] lr: 2.0000e-02 eta: 17:47:37 time: 0.3251 data_time: 0.0137 memory: 6717 grad_norm: 2.9660 loss: 2.3045 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 2.3045 2023/04/13 21:34:44 - mmengine - INFO - Epoch(train) [8][1600/1879] lr: 2.0000e-02 eta: 17:47:40 time: 0.4149 data_time: 0.0152 memory: 6717 grad_norm: 2.9534 loss: 2.0754 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 2.0754 2023/04/13 21:34:51 - mmengine - INFO - Epoch(train) [8][1620/1879] lr: 2.0000e-02 eta: 17:47:25 time: 0.3363 data_time: 0.0130 memory: 6717 grad_norm: 3.0707 loss: 1.9999 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9999 2023/04/13 21:34:59 - mmengine - INFO - Epoch(train) [8][1640/1879] lr: 2.0000e-02 eta: 17:47:24 time: 0.3978 data_time: 0.0144 memory: 6717 grad_norm: 3.0490 loss: 2.1157 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.1157 2023/04/13 21:35:05 - mmengine - INFO - Epoch(train) [8][1660/1879] lr: 2.0000e-02 eta: 17:47:06 time: 0.3248 data_time: 0.0135 memory: 6717 grad_norm: 3.0280 loss: 2.2680 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.2680 2023/04/13 21:35:14 - mmengine - INFO - Epoch(train) [8][1680/1879] lr: 2.0000e-02 eta: 17:47:12 time: 0.4284 data_time: 0.0152 memory: 6717 grad_norm: 3.0501 loss: 2.1918 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.1918 2023/04/13 21:35:20 - mmengine - INFO - Epoch(train) [8][1700/1879] lr: 2.0000e-02 eta: 17:46:54 time: 0.3244 data_time: 0.0134 memory: 6717 grad_norm: 2.9321 loss: 2.1310 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1310 2023/04/13 21:35:29 - mmengine - INFO - Epoch(train) [8][1720/1879] lr: 2.0000e-02 eta: 17:46:58 time: 0.4191 data_time: 0.0132 memory: 6717 grad_norm: 3.1056 loss: 2.0079 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.0079 2023/04/13 21:35:35 - mmengine - INFO - Epoch(train) [8][1740/1879] lr: 2.0000e-02 eta: 17:46:38 time: 0.3140 data_time: 0.0150 memory: 6717 grad_norm: 3.0649 loss: 2.1727 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.1727 2023/04/13 21:35:43 - mmengine - INFO - Epoch(train) [8][1760/1879] lr: 2.0000e-02 eta: 17:46:36 time: 0.3937 data_time: 0.0157 memory: 6717 grad_norm: 2.9548 loss: 2.4791 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.4791 2023/04/13 21:35:50 - mmengine - INFO - Epoch(train) [8][1780/1879] lr: 2.0000e-02 eta: 17:46:23 time: 0.3451 data_time: 0.0375 memory: 6717 grad_norm: 3.0549 loss: 2.4726 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4726 2023/04/13 21:35:58 - mmengine - INFO - Epoch(train) [8][1800/1879] lr: 2.0000e-02 eta: 17:46:20 time: 0.3895 data_time: 0.0155 memory: 6717 grad_norm: 3.0962 loss: 1.9893 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.9893 2023/04/13 21:36:04 - mmengine - INFO - Epoch(train) [8][1820/1879] lr: 2.0000e-02 eta: 17:46:05 time: 0.3379 data_time: 0.0147 memory: 6717 grad_norm: 3.0797 loss: 2.2785 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.2785 2023/04/13 21:36:12 - mmengine - INFO - Epoch(train) [8][1840/1879] lr: 2.0000e-02 eta: 17:46:00 time: 0.3798 data_time: 0.0420 memory: 6717 grad_norm: 3.0128 loss: 2.1963 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1963 2023/04/13 21:36:15 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 21:36:19 - mmengine - INFO - Epoch(train) [8][1860/1879] lr: 2.0000e-02 eta: 17:45:54 time: 0.3761 data_time: 0.1346 memory: 6717 grad_norm: 3.0866 loss: 2.0687 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.0687 2023/04/13 21:36:26 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 21:36:26 - mmengine - INFO - Epoch(train) [8][1879/1879] lr: 2.0000e-02 eta: 17:45:37 time: 0.3129 data_time: 0.1366 memory: 6717 grad_norm: 3.1817 loss: 2.1181 top1_acc: 0.4286 top5_acc: 0.7143 loss_cls: 2.1181 2023/04/13 21:36:35 - mmengine - INFO - Epoch(val) [8][ 20/155] eta: 0:01:01 time: 0.4581 data_time: 0.4246 memory: 1391 2023/04/13 21:36:41 - mmengine - INFO - Epoch(val) [8][ 40/155] eta: 0:00:45 time: 0.3259 data_time: 0.2923 memory: 1391 2023/04/13 21:36:50 - mmengine - INFO - Epoch(val) [8][ 60/155] eta: 0:00:38 time: 0.4300 data_time: 0.3969 memory: 1391 2023/04/13 21:36:56 - mmengine - INFO - Epoch(val) [8][ 80/155] eta: 0:00:28 time: 0.3146 data_time: 0.2818 memory: 1391 2023/04/13 21:37:05 - mmengine - INFO - Epoch(val) [8][100/155] eta: 0:00:21 time: 0.4538 data_time: 0.4205 memory: 1391 2023/04/13 21:37:11 - mmengine - INFO - Epoch(val) [8][120/155] eta: 0:00:13 time: 0.3090 data_time: 0.2756 memory: 1391 2023/04/13 21:37:21 - mmengine - INFO - Epoch(val) [8][140/155] eta: 0:00:05 time: 0.4870 data_time: 0.4542 memory: 1391 2023/04/13 21:37:28 - mmengine - INFO - Epoch(val) [8][155/155] acc/top1: 0.5212 acc/top5: 0.7787 acc/mean1: 0.5212 data_time: 0.4194 time: 0.4516 2023/04/13 21:37:28 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_7.pth is removed 2023/04/13 21:37:29 - mmengine - INFO - The best checkpoint with 0.5212 acc/top1 at 8 epoch is saved to best_acc_top1_epoch_8.pth. 2023/04/13 21:37:38 - mmengine - INFO - Epoch(train) [9][ 20/1879] lr: 2.0000e-02 eta: 17:45:54 time: 0.4789 data_time: 0.3359 memory: 6717 grad_norm: 3.1023 loss: 2.3433 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.3433 2023/04/13 21:37:45 - mmengine - INFO - Epoch(train) [9][ 40/1879] lr: 2.0000e-02 eta: 17:45:40 time: 0.3374 data_time: 0.1178 memory: 6717 grad_norm: 3.0508 loss: 2.1901 top1_acc: 0.1875 top5_acc: 0.6875 loss_cls: 2.1901 2023/04/13 21:37:53 - mmengine - INFO - Epoch(train) [9][ 60/1879] lr: 2.0000e-02 eta: 17:45:42 time: 0.4128 data_time: 0.1243 memory: 6717 grad_norm: 3.0125 loss: 2.0902 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.0902 2023/04/13 21:38:00 - mmengine - INFO - Epoch(train) [9][ 80/1879] lr: 2.0000e-02 eta: 17:45:26 time: 0.3312 data_time: 0.0634 memory: 6717 grad_norm: 3.0077 loss: 2.2511 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.2511 2023/04/13 21:38:09 - mmengine - INFO - Epoch(train) [9][ 100/1879] lr: 2.0000e-02 eta: 17:45:41 time: 0.4703 data_time: 0.0189 memory: 6717 grad_norm: 3.0677 loss: 2.0762 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0762 2023/04/13 21:38:16 - mmengine - INFO - Epoch(train) [9][ 120/1879] lr: 2.0000e-02 eta: 17:45:21 time: 0.3129 data_time: 0.0167 memory: 6717 grad_norm: 2.9742 loss: 2.0332 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.0332 2023/04/13 21:38:24 - mmengine - INFO - Epoch(train) [9][ 140/1879] lr: 2.0000e-02 eta: 17:45:26 time: 0.4243 data_time: 0.0563 memory: 6717 grad_norm: 3.0231 loss: 2.2144 top1_acc: 0.1250 top5_acc: 0.5625 loss_cls: 2.2144 2023/04/13 21:38:31 - mmengine - INFO - Epoch(train) [9][ 160/1879] lr: 2.0000e-02 eta: 17:45:11 time: 0.3360 data_time: 0.0556 memory: 6717 grad_norm: 3.0791 loss: 2.1301 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 2.1301 2023/04/13 21:38:38 - mmengine - INFO - Epoch(train) [9][ 180/1879] lr: 2.0000e-02 eta: 17:45:05 time: 0.3780 data_time: 0.0929 memory: 6717 grad_norm: 3.0776 loss: 2.1917 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.1917 2023/04/13 21:38:45 - mmengine - INFO - Epoch(train) [9][ 200/1879] lr: 2.0000e-02 eta: 17:44:48 time: 0.3271 data_time: 0.0397 memory: 6717 grad_norm: 2.9969 loss: 2.0324 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.0324 2023/04/13 21:38:53 - mmengine - INFO - Epoch(train) [9][ 220/1879] lr: 2.0000e-02 eta: 17:44:48 time: 0.4048 data_time: 0.0171 memory: 6717 grad_norm: 3.0474 loss: 2.1767 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1767 2023/04/13 21:38:59 - mmengine - INFO - Epoch(train) [9][ 240/1879] lr: 2.0000e-02 eta: 17:44:26 time: 0.3056 data_time: 0.0234 memory: 6717 grad_norm: 2.9965 loss: 2.0067 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0067 2023/04/13 21:39:07 - mmengine - INFO - Epoch(train) [9][ 260/1879] lr: 2.0000e-02 eta: 17:44:31 time: 0.4215 data_time: 0.0270 memory: 6717 grad_norm: 3.0053 loss: 2.1723 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1723 2023/04/13 21:39:14 - mmengine - INFO - Epoch(train) [9][ 280/1879] lr: 2.0000e-02 eta: 17:44:14 time: 0.3292 data_time: 0.0331 memory: 6717 grad_norm: 3.0646 loss: 2.0773 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.0773 2023/04/13 21:39:22 - mmengine - INFO - Epoch(train) [9][ 300/1879] lr: 2.0000e-02 eta: 17:44:17 time: 0.4147 data_time: 0.0377 memory: 6717 grad_norm: 3.1115 loss: 2.1743 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1743 2023/04/13 21:39:29 - mmengine - INFO - Epoch(train) [9][ 320/1879] lr: 2.0000e-02 eta: 17:43:57 time: 0.3166 data_time: 0.0283 memory: 6717 grad_norm: 3.0972 loss: 2.0910 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0910 2023/04/13 21:39:37 - mmengine - INFO - Epoch(train) [9][ 340/1879] lr: 2.0000e-02 eta: 17:43:57 time: 0.4008 data_time: 0.0390 memory: 6717 grad_norm: 3.1070 loss: 2.1277 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1277 2023/04/13 21:39:44 - mmengine - INFO - Epoch(train) [9][ 360/1879] lr: 2.0000e-02 eta: 17:43:45 time: 0.3512 data_time: 0.1413 memory: 6717 grad_norm: 3.0414 loss: 2.0607 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0607 2023/04/13 21:39:52 - mmengine - INFO - Epoch(train) [9][ 380/1879] lr: 2.0000e-02 eta: 17:43:47 time: 0.4129 data_time: 0.0236 memory: 6717 grad_norm: 3.0565 loss: 2.0591 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0591 2023/04/13 21:39:59 - mmengine - INFO - Epoch(train) [9][ 400/1879] lr: 2.0000e-02 eta: 17:43:34 time: 0.3426 data_time: 0.0467 memory: 6717 grad_norm: 3.0371 loss: 2.3032 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3032 2023/04/13 21:40:07 - mmengine - INFO - Epoch(train) [9][ 420/1879] lr: 2.0000e-02 eta: 17:43:30 time: 0.3864 data_time: 0.0127 memory: 6717 grad_norm: 3.0906 loss: 2.2514 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.2514 2023/04/13 21:40:13 - mmengine - INFO - Epoch(train) [9][ 440/1879] lr: 2.0000e-02 eta: 17:43:16 time: 0.3394 data_time: 0.1242 memory: 6717 grad_norm: 3.4203 loss: 2.2130 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.2130 2023/04/13 21:40:20 - mmengine - INFO - Epoch(train) [9][ 460/1879] lr: 2.0000e-02 eta: 17:43:05 time: 0.3561 data_time: 0.1333 memory: 6717 grad_norm: 3.2034 loss: 2.0679 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0679 2023/04/13 21:40:29 - mmengine - INFO - Epoch(train) [9][ 480/1879] lr: 2.0000e-02 eta: 17:43:08 time: 0.4154 data_time: 0.2287 memory: 6717 grad_norm: 3.1292 loss: 2.1307 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1307 2023/04/13 21:40:36 - mmengine - INFO - Epoch(train) [9][ 500/1879] lr: 2.0000e-02 eta: 17:42:57 time: 0.3534 data_time: 0.2135 memory: 6717 grad_norm: 2.9801 loss: 2.1427 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 2.1427 2023/04/13 21:40:45 - mmengine - INFO - Epoch(train) [9][ 520/1879] lr: 2.0000e-02 eta: 17:43:04 time: 0.4365 data_time: 0.2976 memory: 6717 grad_norm: 3.0407 loss: 2.1143 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.1143 2023/04/13 21:40:51 - mmengine - INFO - Epoch(train) [9][ 540/1879] lr: 2.0000e-02 eta: 17:42:43 time: 0.3071 data_time: 0.1685 memory: 6717 grad_norm: 3.0068 loss: 2.3438 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.3438 2023/04/13 21:40:59 - mmengine - INFO - Epoch(train) [9][ 560/1879] lr: 2.0000e-02 eta: 17:42:41 time: 0.3937 data_time: 0.2544 memory: 6717 grad_norm: 2.9287 loss: 2.0691 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.0691 2023/04/13 21:41:05 - mmengine - INFO - Epoch(train) [9][ 580/1879] lr: 2.0000e-02 eta: 17:42:22 time: 0.3185 data_time: 0.1768 memory: 6717 grad_norm: 3.0539 loss: 2.2110 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2110 2023/04/13 21:41:13 - mmengine - INFO - Epoch(train) [9][ 600/1879] lr: 2.0000e-02 eta: 17:42:27 time: 0.4264 data_time: 0.2863 memory: 6717 grad_norm: 2.9829 loss: 2.0694 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0694 2023/04/13 21:41:20 - mmengine - INFO - Epoch(train) [9][ 620/1879] lr: 2.0000e-02 eta: 17:42:07 time: 0.3146 data_time: 0.1743 memory: 6717 grad_norm: 3.0624 loss: 2.0084 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0084 2023/04/13 21:41:28 - mmengine - INFO - Epoch(train) [9][ 640/1879] lr: 2.0000e-02 eta: 17:42:05 time: 0.3907 data_time: 0.2556 memory: 6717 grad_norm: 3.0137 loss: 2.1754 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1754 2023/04/13 21:41:34 - mmengine - INFO - Epoch(train) [9][ 660/1879] lr: 2.0000e-02 eta: 17:41:45 time: 0.3161 data_time: 0.1771 memory: 6717 grad_norm: 3.0865 loss: 2.2503 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2503 2023/04/13 21:41:42 - mmengine - INFO - Epoch(train) [9][ 680/1879] lr: 2.0000e-02 eta: 17:41:43 time: 0.3929 data_time: 0.2415 memory: 6717 grad_norm: 3.1199 loss: 2.1537 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1537 2023/04/13 21:41:48 - mmengine - INFO - Epoch(train) [9][ 700/1879] lr: 2.0000e-02 eta: 17:41:28 time: 0.3335 data_time: 0.1436 memory: 6717 grad_norm: 2.9985 loss: 2.0505 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0505 2023/04/13 21:41:57 - mmengine - INFO - Epoch(train) [9][ 720/1879] lr: 2.0000e-02 eta: 17:41:29 time: 0.4090 data_time: 0.2559 memory: 6717 grad_norm: 2.9890 loss: 2.2228 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.2228 2023/04/13 21:42:04 - mmengine - INFO - Epoch(train) [9][ 740/1879] lr: 2.0000e-02 eta: 17:41:17 time: 0.3488 data_time: 0.1237 memory: 6717 grad_norm: 2.9997 loss: 2.1036 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1036 2023/04/13 21:42:11 - mmengine - INFO - Epoch(train) [9][ 760/1879] lr: 2.0000e-02 eta: 17:41:12 time: 0.3842 data_time: 0.1300 memory: 6717 grad_norm: 2.9899 loss: 2.3842 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3842 2023/04/13 21:42:18 - mmengine - INFO - Epoch(train) [9][ 780/1879] lr: 2.0000e-02 eta: 17:41:03 time: 0.3601 data_time: 0.0959 memory: 6717 grad_norm: 2.9113 loss: 2.1717 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1717 2023/04/13 21:42:26 - mmengine - INFO - Epoch(train) [9][ 800/1879] lr: 2.0000e-02 eta: 17:40:54 time: 0.3640 data_time: 0.0536 memory: 6717 grad_norm: 2.9925 loss: 2.1295 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1295 2023/04/13 21:42:34 - mmengine - INFO - Epoch(train) [9][ 820/1879] lr: 2.0000e-02 eta: 17:40:55 time: 0.4067 data_time: 0.0134 memory: 6717 grad_norm: 3.0162 loss: 2.1216 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 2.1216 2023/04/13 21:42:40 - mmengine - INFO - Epoch(train) [9][ 840/1879] lr: 2.0000e-02 eta: 17:40:37 time: 0.3203 data_time: 0.0138 memory: 6717 grad_norm: 3.0619 loss: 2.3075 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.3075 2023/04/13 21:42:49 - mmengine - INFO - Epoch(train) [9][ 860/1879] lr: 2.0000e-02 eta: 17:40:40 time: 0.4181 data_time: 0.0143 memory: 6717 grad_norm: 2.9439 loss: 2.1370 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1370 2023/04/13 21:42:56 - mmengine - INFO - Epoch(train) [9][ 880/1879] lr: 2.0000e-02 eta: 17:40:26 time: 0.3422 data_time: 0.0378 memory: 6717 grad_norm: 2.9723 loss: 2.1255 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1255 2023/04/13 21:43:03 - mmengine - INFO - Epoch(train) [9][ 900/1879] lr: 2.0000e-02 eta: 17:40:20 time: 0.3754 data_time: 0.0906 memory: 6717 grad_norm: 3.0231 loss: 2.0551 top1_acc: 0.1875 top5_acc: 0.6875 loss_cls: 2.0551 2023/04/13 21:43:10 - mmengine - INFO - Epoch(train) [9][ 920/1879] lr: 2.0000e-02 eta: 17:40:06 time: 0.3383 data_time: 0.0894 memory: 6717 grad_norm: 3.0582 loss: 2.1213 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.1213 2023/04/13 21:43:18 - mmengine - INFO - Epoch(train) [9][ 940/1879] lr: 2.0000e-02 eta: 17:40:06 time: 0.4042 data_time: 0.0469 memory: 6717 grad_norm: 3.0871 loss: 2.2837 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.2837 2023/04/13 21:43:24 - mmengine - INFO - Epoch(train) [9][ 960/1879] lr: 2.0000e-02 eta: 17:39:47 time: 0.3174 data_time: 0.0415 memory: 6717 grad_norm: 2.9765 loss: 2.1813 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.1813 2023/04/13 21:43:27 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 21:43:33 - mmengine - INFO - Epoch(train) [9][ 980/1879] lr: 2.0000e-02 eta: 17:39:56 time: 0.4449 data_time: 0.0176 memory: 6717 grad_norm: 3.0253 loss: 1.9478 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.9478 2023/04/13 21:43:39 - mmengine - INFO - Epoch(train) [9][1000/1879] lr: 2.0000e-02 eta: 17:39:35 time: 0.3094 data_time: 0.0156 memory: 6717 grad_norm: 2.9241 loss: 2.1669 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1669 2023/04/13 21:43:47 - mmengine - INFO - Epoch(train) [9][1020/1879] lr: 2.0000e-02 eta: 17:39:32 time: 0.3879 data_time: 0.0145 memory: 6717 grad_norm: 3.0195 loss: 2.3340 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3340 2023/04/13 21:43:54 - mmengine - INFO - Epoch(train) [9][1040/1879] lr: 2.0000e-02 eta: 17:39:18 time: 0.3398 data_time: 0.0138 memory: 6717 grad_norm: 3.0254 loss: 2.1040 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1040 2023/04/13 21:44:02 - mmengine - INFO - Epoch(train) [9][1060/1879] lr: 2.0000e-02 eta: 17:39:22 time: 0.4224 data_time: 0.0148 memory: 6717 grad_norm: 2.9688 loss: 2.0435 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0435 2023/04/13 21:44:09 - mmengine - INFO - Epoch(train) [9][1080/1879] lr: 2.0000e-02 eta: 17:39:03 time: 0.3163 data_time: 0.0140 memory: 6717 grad_norm: 3.0278 loss: 2.2460 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2460 2023/04/13 21:44:16 - mmengine - INFO - Epoch(train) [9][1100/1879] lr: 2.0000e-02 eta: 17:38:58 time: 0.3806 data_time: 0.0247 memory: 6717 grad_norm: 3.0285 loss: 2.2857 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2857 2023/04/13 21:44:24 - mmengine - INFO - Epoch(train) [9][1120/1879] lr: 2.0000e-02 eta: 17:38:52 time: 0.3765 data_time: 0.0177 memory: 6717 grad_norm: 3.0846 loss: 2.3413 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.3413 2023/04/13 21:44:30 - mmengine - INFO - Epoch(train) [9][1140/1879] lr: 2.0000e-02 eta: 17:38:37 time: 0.3344 data_time: 0.0138 memory: 6717 grad_norm: 2.9842 loss: 2.0422 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0422 2023/04/13 21:44:39 - mmengine - INFO - Epoch(train) [9][1160/1879] lr: 2.0000e-02 eta: 17:38:38 time: 0.4105 data_time: 0.0152 memory: 6717 grad_norm: 3.0085 loss: 2.0166 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0166 2023/04/13 21:44:45 - mmengine - INFO - Epoch(train) [9][1180/1879] lr: 2.0000e-02 eta: 17:38:21 time: 0.3243 data_time: 0.0132 memory: 6717 grad_norm: 3.4007 loss: 2.1369 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1369 2023/04/13 21:44:52 - mmengine - INFO - Epoch(train) [9][1200/1879] lr: 2.0000e-02 eta: 17:38:12 time: 0.3642 data_time: 0.0156 memory: 6717 grad_norm: 3.1125 loss: 2.0885 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 2.0885 2023/04/13 21:45:00 - mmengine - INFO - Epoch(train) [9][1220/1879] lr: 2.0000e-02 eta: 17:38:01 time: 0.3526 data_time: 0.0235 memory: 6717 grad_norm: 2.9747 loss: 2.1737 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1737 2023/04/13 21:45:07 - mmengine - INFO - Epoch(train) [9][1240/1879] lr: 2.0000e-02 eta: 17:37:55 time: 0.3725 data_time: 0.0794 memory: 6717 grad_norm: 3.0185 loss: 2.2477 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2477 2023/04/13 21:45:14 - mmengine - INFO - Epoch(train) [9][1260/1879] lr: 2.0000e-02 eta: 17:37:46 time: 0.3651 data_time: 0.0562 memory: 6717 grad_norm: 3.0581 loss: 2.0153 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0153 2023/04/13 21:45:22 - mmengine - INFO - Epoch(train) [9][1280/1879] lr: 2.0000e-02 eta: 17:37:41 time: 0.3822 data_time: 0.1409 memory: 6717 grad_norm: 3.0030 loss: 2.2326 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2326 2023/04/13 21:45:29 - mmengine - INFO - Epoch(train) [9][1300/1879] lr: 2.0000e-02 eta: 17:37:29 time: 0.3473 data_time: 0.1005 memory: 6717 grad_norm: 2.9888 loss: 2.0340 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.0340 2023/04/13 21:45:37 - mmengine - INFO - Epoch(train) [9][1320/1879] lr: 2.0000e-02 eta: 17:37:26 time: 0.3888 data_time: 0.1309 memory: 6717 grad_norm: 2.9510 loss: 2.2073 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 2.2073 2023/04/13 21:45:44 - mmengine - INFO - Epoch(train) [9][1340/1879] lr: 2.0000e-02 eta: 17:37:15 time: 0.3527 data_time: 0.0876 memory: 6717 grad_norm: 2.9877 loss: 1.9874 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9874 2023/04/13 21:45:52 - mmengine - INFO - Epoch(train) [9][1360/1879] lr: 2.0000e-02 eta: 17:37:16 time: 0.4093 data_time: 0.1853 memory: 6717 grad_norm: 3.1130 loss: 2.0510 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 2.0510 2023/04/13 21:45:59 - mmengine - INFO - Epoch(train) [9][1380/1879] lr: 2.0000e-02 eta: 17:37:01 time: 0.3327 data_time: 0.1244 memory: 6717 grad_norm: 2.9395 loss: 2.2967 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.2967 2023/04/13 21:46:07 - mmengine - INFO - Epoch(train) [9][1400/1879] lr: 2.0000e-02 eta: 17:37:01 time: 0.4072 data_time: 0.1935 memory: 6717 grad_norm: 3.0516 loss: 2.2466 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2466 2023/04/13 21:46:13 - mmengine - INFO - Epoch(train) [9][1420/1879] lr: 2.0000e-02 eta: 17:36:42 time: 0.3127 data_time: 0.0691 memory: 6717 grad_norm: 3.0223 loss: 2.2057 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.2057 2023/04/13 21:46:21 - mmengine - INFO - Epoch(train) [9][1440/1879] lr: 2.0000e-02 eta: 17:36:41 time: 0.4036 data_time: 0.0169 memory: 6717 grad_norm: 3.0771 loss: 2.3360 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.3360 2023/04/13 21:46:28 - mmengine - INFO - Epoch(train) [9][1460/1879] lr: 2.0000e-02 eta: 17:36:34 time: 0.3722 data_time: 0.0130 memory: 6717 grad_norm: 2.9539 loss: 2.1062 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 2.1062 2023/04/13 21:46:36 - mmengine - INFO - Epoch(train) [9][1480/1879] lr: 2.0000e-02 eta: 17:36:29 time: 0.3814 data_time: 0.0159 memory: 6717 grad_norm: 3.0135 loss: 2.0472 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0472 2023/04/13 21:46:44 - mmengine - INFO - Epoch(train) [9][1500/1879] lr: 2.0000e-02 eta: 17:36:22 time: 0.3710 data_time: 0.0124 memory: 6717 grad_norm: 2.9319 loss: 2.3357 top1_acc: 0.2500 top5_acc: 0.3125 loss_cls: 2.3357 2023/04/13 21:46:51 - mmengine - INFO - Epoch(train) [9][1520/1879] lr: 2.0000e-02 eta: 17:36:11 time: 0.3498 data_time: 0.0155 memory: 6717 grad_norm: 2.9292 loss: 2.1172 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.1172 2023/04/13 21:46:58 - mmengine - INFO - Epoch(train) [9][1540/1879] lr: 2.0000e-02 eta: 17:36:09 time: 0.3951 data_time: 0.0122 memory: 6717 grad_norm: 2.9976 loss: 2.4617 top1_acc: 0.2500 top5_acc: 0.4375 loss_cls: 2.4617 2023/04/13 21:47:06 - mmengine - INFO - Epoch(train) [9][1560/1879] lr: 2.0000e-02 eta: 17:35:58 time: 0.3565 data_time: 0.0146 memory: 6717 grad_norm: 3.0362 loss: 2.0185 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.0185 2023/04/13 21:47:13 - mmengine - INFO - Epoch(train) [9][1580/1879] lr: 2.0000e-02 eta: 17:35:53 time: 0.3802 data_time: 0.0130 memory: 6717 grad_norm: 3.0203 loss: 2.1606 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.1606 2023/04/13 21:47:21 - mmengine - INFO - Epoch(train) [9][1600/1879] lr: 2.0000e-02 eta: 17:35:46 time: 0.3713 data_time: 0.0141 memory: 6717 grad_norm: 2.9998 loss: 2.1936 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.1936 2023/04/13 21:47:27 - mmengine - INFO - Epoch(train) [9][1620/1879] lr: 2.0000e-02 eta: 17:35:29 time: 0.3209 data_time: 0.0141 memory: 6717 grad_norm: 3.0138 loss: 2.0703 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.0703 2023/04/13 21:47:39 - mmengine - INFO - Epoch(train) [9][1640/1879] lr: 2.0000e-02 eta: 17:36:08 time: 0.5994 data_time: 0.0433 memory: 6717 grad_norm: 2.9406 loss: 2.3299 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3299 2023/04/13 21:47:46 - mmengine - INFO - Epoch(train) [9][1660/1879] lr: 2.0000e-02 eta: 17:35:53 time: 0.3336 data_time: 0.1031 memory: 6717 grad_norm: 3.0690 loss: 2.1889 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1889 2023/04/13 21:47:53 - mmengine - INFO - Epoch(train) [9][1680/1879] lr: 2.0000e-02 eta: 17:35:46 time: 0.3700 data_time: 0.0195 memory: 6717 grad_norm: 3.0625 loss: 2.0815 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0815 2023/04/13 21:48:00 - mmengine - INFO - Epoch(train) [9][1700/1879] lr: 2.0000e-02 eta: 17:35:32 time: 0.3398 data_time: 0.0170 memory: 6717 grad_norm: 3.0118 loss: 2.0997 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0997 2023/04/13 21:48:08 - mmengine - INFO - Epoch(train) [9][1720/1879] lr: 2.0000e-02 eta: 17:35:30 time: 0.3933 data_time: 0.0180 memory: 6717 grad_norm: 2.9044 loss: 2.0196 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.0196 2023/04/13 21:48:14 - mmengine - INFO - Epoch(train) [9][1740/1879] lr: 2.0000e-02 eta: 17:35:15 time: 0.3323 data_time: 0.0152 memory: 6717 grad_norm: 3.0081 loss: 1.9679 top1_acc: 0.2500 top5_acc: 0.8125 loss_cls: 1.9679 2023/04/13 21:48:23 - mmengine - INFO - Epoch(train) [9][1760/1879] lr: 2.0000e-02 eta: 17:35:17 time: 0.4156 data_time: 0.0139 memory: 6717 grad_norm: 3.0091 loss: 1.9878 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.9878 2023/04/13 21:48:29 - mmengine - INFO - Epoch(train) [9][1780/1879] lr: 2.0000e-02 eta: 17:35:02 time: 0.3339 data_time: 0.0156 memory: 6717 grad_norm: 2.9854 loss: 2.1268 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 2.1268 2023/04/13 21:48:38 - mmengine - INFO - Epoch(train) [9][1800/1879] lr: 2.0000e-02 eta: 17:35:06 time: 0.4290 data_time: 0.0128 memory: 6717 grad_norm: 2.9365 loss: 2.3034 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3034 2023/04/13 21:48:45 - mmengine - INFO - Epoch(train) [9][1820/1879] lr: 2.0000e-02 eta: 17:34:51 time: 0.3309 data_time: 0.0147 memory: 6717 grad_norm: 2.9966 loss: 2.3085 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3085 2023/04/13 21:48:53 - mmengine - INFO - Epoch(train) [9][1840/1879] lr: 2.0000e-02 eta: 17:34:53 time: 0.4155 data_time: 0.0129 memory: 6717 grad_norm: 3.0416 loss: 2.2903 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2903 2023/04/13 21:48:58 - mmengine - INFO - Epoch(train) [9][1860/1879] lr: 2.0000e-02 eta: 17:34:27 time: 0.2769 data_time: 0.0150 memory: 6717 grad_norm: 2.9789 loss: 2.1771 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.1771 2023/04/13 21:49:04 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 21:49:04 - mmengine - INFO - Epoch(train) [9][1879/1879] lr: 2.0000e-02 eta: 17:34:10 time: 0.3105 data_time: 0.0118 memory: 6717 grad_norm: 3.0132 loss: 2.1741 top1_acc: 0.4286 top5_acc: 0.7143 loss_cls: 2.1741 2023/04/13 21:49:04 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/04/13 21:49:14 - mmengine - INFO - Epoch(val) [9][ 20/155] eta: 0:01:01 time: 0.4570 data_time: 0.4242 memory: 1391 2023/04/13 21:49:20 - mmengine - INFO - Epoch(val) [9][ 40/155] eta: 0:00:43 time: 0.2961 data_time: 0.2634 memory: 1391 2023/04/13 21:49:29 - mmengine - INFO - Epoch(val) [9][ 60/155] eta: 0:00:38 time: 0.4519 data_time: 0.4186 memory: 1391 2023/04/13 21:49:35 - mmengine - INFO - Epoch(val) [9][ 80/155] eta: 0:00:28 time: 0.3183 data_time: 0.2851 memory: 1391 2023/04/13 21:49:44 - mmengine - INFO - Epoch(val) [9][100/155] eta: 0:00:21 time: 0.4525 data_time: 0.4197 memory: 1391 2023/04/13 21:49:50 - mmengine - INFO - Epoch(val) [9][120/155] eta: 0:00:13 time: 0.2962 data_time: 0.2631 memory: 1391 2023/04/13 21:50:00 - mmengine - INFO - Epoch(val) [9][140/155] eta: 0:00:05 time: 0.4864 data_time: 0.4538 memory: 1391 2023/04/13 21:50:07 - mmengine - INFO - Epoch(val) [9][155/155] acc/top1: 0.5384 acc/top5: 0.7911 acc/mean1: 0.5381 data_time: 0.4201 time: 0.4519 2023/04/13 21:50:07 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_8.pth is removed 2023/04/13 21:50:08 - mmengine - INFO - The best checkpoint with 0.5384 acc/top1 at 9 epoch is saved to best_acc_top1_epoch_9.pth. 2023/04/13 21:50:17 - mmengine - INFO - Epoch(train) [10][ 20/1879] lr: 2.0000e-02 eta: 17:34:25 time: 0.4833 data_time: 0.3105 memory: 6717 grad_norm: 3.0299 loss: 2.2375 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.2375 2023/04/13 21:50:24 - mmengine - INFO - Epoch(train) [10][ 40/1879] lr: 2.0000e-02 eta: 17:34:07 time: 0.3162 data_time: 0.1511 memory: 6717 grad_norm: 2.9996 loss: 1.9094 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.9094 2023/04/13 21:50:32 - mmengine - INFO - Epoch(train) [10][ 60/1879] lr: 2.0000e-02 eta: 17:34:10 time: 0.4230 data_time: 0.2783 memory: 6717 grad_norm: 3.0867 loss: 1.9988 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 1.9988 2023/04/13 21:50:39 - mmengine - INFO - Epoch(train) [10][ 80/1879] lr: 2.0000e-02 eta: 17:33:55 time: 0.3278 data_time: 0.1938 memory: 6717 grad_norm: 3.0473 loss: 1.9215 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9215 2023/04/13 21:50:44 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 21:50:47 - mmengine - INFO - Epoch(train) [10][ 100/1879] lr: 2.0000e-02 eta: 17:33:55 time: 0.4104 data_time: 0.2641 memory: 6717 grad_norm: 3.0310 loss: 2.0793 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0793 2023/04/13 21:50:53 - mmengine - INFO - Epoch(train) [10][ 120/1879] lr: 2.0000e-02 eta: 17:33:39 time: 0.3277 data_time: 0.1628 memory: 6717 grad_norm: 3.0335 loss: 2.2233 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2233 2023/04/13 21:51:02 - mmengine - INFO - Epoch(train) [10][ 140/1879] lr: 2.0000e-02 eta: 17:33:45 time: 0.4339 data_time: 0.2719 memory: 6717 grad_norm: 3.0395 loss: 2.2975 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2975 2023/04/13 21:51:08 - mmengine - INFO - Epoch(train) [10][ 160/1879] lr: 2.0000e-02 eta: 17:33:25 time: 0.3087 data_time: 0.1733 memory: 6717 grad_norm: 3.0594 loss: 2.0787 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0787 2023/04/13 21:51:17 - mmengine - INFO - Epoch(train) [10][ 180/1879] lr: 2.0000e-02 eta: 17:33:31 time: 0.4392 data_time: 0.2139 memory: 6717 grad_norm: 3.0305 loss: 2.1908 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.1908 2023/04/13 21:51:23 - mmengine - INFO - Epoch(train) [10][ 200/1879] lr: 2.0000e-02 eta: 17:33:14 time: 0.3219 data_time: 0.0405 memory: 6717 grad_norm: 3.0971 loss: 2.0384 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0384 2023/04/13 21:51:32 - mmengine - INFO - Epoch(train) [10][ 220/1879] lr: 2.0000e-02 eta: 17:33:18 time: 0.4265 data_time: 0.0334 memory: 6717 grad_norm: 2.9560 loss: 2.1413 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1413 2023/04/13 21:51:38 - mmengine - INFO - Epoch(train) [10][ 240/1879] lr: 2.0000e-02 eta: 17:33:00 time: 0.3130 data_time: 0.0127 memory: 6717 grad_norm: 2.9805 loss: 2.3829 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.3829 2023/04/13 21:51:47 - mmengine - INFO - Epoch(train) [10][ 260/1879] lr: 2.0000e-02 eta: 17:33:06 time: 0.4398 data_time: 0.0218 memory: 6717 grad_norm: 2.9559 loss: 2.0467 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.0467 2023/04/13 21:51:54 - mmengine - INFO - Epoch(train) [10][ 280/1879] lr: 2.0000e-02 eta: 17:32:50 time: 0.3278 data_time: 0.0125 memory: 6717 grad_norm: 3.0235 loss: 2.0660 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.0660 2023/04/13 21:52:01 - mmengine - INFO - Epoch(train) [10][ 300/1879] lr: 2.0000e-02 eta: 17:32:46 time: 0.3855 data_time: 0.0135 memory: 6717 grad_norm: 2.9958 loss: 1.9011 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.9011 2023/04/13 21:52:08 - mmengine - INFO - Epoch(train) [10][ 320/1879] lr: 2.0000e-02 eta: 17:32:27 time: 0.3130 data_time: 0.0146 memory: 6717 grad_norm: 3.0618 loss: 2.1559 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 2.1559 2023/04/13 21:52:16 - mmengine - INFO - Epoch(train) [10][ 340/1879] lr: 2.0000e-02 eta: 17:32:28 time: 0.4128 data_time: 0.0147 memory: 6717 grad_norm: 3.0085 loss: 2.0126 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 2.0126 2023/04/13 21:52:22 - mmengine - INFO - Epoch(train) [10][ 360/1879] lr: 2.0000e-02 eta: 17:32:08 time: 0.3034 data_time: 0.0167 memory: 6717 grad_norm: 2.9294 loss: 1.9008 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9008 2023/04/13 21:52:31 - mmengine - INFO - Epoch(train) [10][ 380/1879] lr: 2.0000e-02 eta: 17:32:13 time: 0.4326 data_time: 0.0149 memory: 6717 grad_norm: 3.0137 loss: 2.0158 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.0158 2023/04/13 21:52:37 - mmengine - INFO - Epoch(train) [10][ 400/1879] lr: 2.0000e-02 eta: 17:31:54 time: 0.3149 data_time: 0.0127 memory: 6717 grad_norm: 2.9997 loss: 2.0961 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 2.0961 2023/04/13 21:52:46 - mmengine - INFO - Epoch(train) [10][ 420/1879] lr: 2.0000e-02 eta: 17:32:01 time: 0.4392 data_time: 0.0155 memory: 6717 grad_norm: 3.0352 loss: 2.0786 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.0786 2023/04/13 21:52:52 - mmengine - INFO - Epoch(train) [10][ 440/1879] lr: 2.0000e-02 eta: 17:31:46 time: 0.3317 data_time: 0.0130 memory: 6717 grad_norm: 3.0468 loss: 2.0367 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0367 2023/04/13 21:53:00 - mmengine - INFO - Epoch(train) [10][ 460/1879] lr: 2.0000e-02 eta: 17:31:44 time: 0.4002 data_time: 0.0140 memory: 6717 grad_norm: 3.0371 loss: 2.0323 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0323 2023/04/13 21:53:07 - mmengine - INFO - Epoch(train) [10][ 480/1879] lr: 2.0000e-02 eta: 17:31:26 time: 0.3143 data_time: 0.0155 memory: 6717 grad_norm: 2.9670 loss: 1.9451 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9451 2023/04/13 21:53:15 - mmengine - INFO - Epoch(train) [10][ 500/1879] lr: 2.0000e-02 eta: 17:31:27 time: 0.4124 data_time: 0.0139 memory: 6717 grad_norm: 3.0044 loss: 2.2313 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.2313 2023/04/13 21:53:22 - mmengine - INFO - Epoch(train) [10][ 520/1879] lr: 2.0000e-02 eta: 17:31:14 time: 0.3417 data_time: 0.0134 memory: 6717 grad_norm: 2.9418 loss: 2.1282 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1282 2023/04/13 21:53:30 - mmengine - INFO - Epoch(train) [10][ 540/1879] lr: 2.0000e-02 eta: 17:31:17 time: 0.4223 data_time: 0.0132 memory: 6717 grad_norm: 3.7891 loss: 2.3210 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.3210 2023/04/13 21:53:37 - mmengine - INFO - Epoch(train) [10][ 560/1879] lr: 2.0000e-02 eta: 17:31:03 time: 0.3368 data_time: 0.0156 memory: 6717 grad_norm: 3.1188 loss: 2.3250 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.3250 2023/04/13 21:53:45 - mmengine - INFO - Epoch(train) [10][ 580/1879] lr: 2.0000e-02 eta: 17:31:02 time: 0.4049 data_time: 0.0144 memory: 6717 grad_norm: 3.0994 loss: 2.3459 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.3459 2023/04/13 21:53:51 - mmengine - INFO - Epoch(train) [10][ 600/1879] lr: 2.0000e-02 eta: 17:30:46 time: 0.3267 data_time: 0.0130 memory: 6717 grad_norm: 2.9977 loss: 2.2300 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.2300 2023/04/13 21:54:00 - mmengine - INFO - Epoch(train) [10][ 620/1879] lr: 2.0000e-02 eta: 17:30:47 time: 0.4118 data_time: 0.0141 memory: 6717 grad_norm: 2.9957 loss: 2.2317 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2317 2023/04/13 21:54:06 - mmengine - INFO - Epoch(train) [10][ 640/1879] lr: 2.0000e-02 eta: 17:30:31 time: 0.3261 data_time: 0.0141 memory: 6717 grad_norm: 2.9754 loss: 1.8666 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8666 2023/04/13 21:54:14 - mmengine - INFO - Epoch(train) [10][ 660/1879] lr: 2.0000e-02 eta: 17:30:26 time: 0.3836 data_time: 0.0134 memory: 6717 grad_norm: 2.9721 loss: 2.1966 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.1966 2023/04/13 21:54:20 - mmengine - INFO - Epoch(train) [10][ 680/1879] lr: 2.0000e-02 eta: 17:30:07 time: 0.3106 data_time: 0.0162 memory: 6717 grad_norm: 3.1043 loss: 1.8576 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8576 2023/04/13 21:54:29 - mmengine - INFO - Epoch(train) [10][ 700/1879] lr: 2.0000e-02 eta: 17:30:12 time: 0.4337 data_time: 0.0407 memory: 6717 grad_norm: 2.9826 loss: 2.0372 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0372 2023/04/13 21:54:35 - mmengine - INFO - Epoch(train) [10][ 720/1879] lr: 2.0000e-02 eta: 17:29:55 time: 0.3197 data_time: 0.0137 memory: 6717 grad_norm: 2.9643 loss: 2.0871 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0871 2023/04/13 21:54:44 - mmengine - INFO - Epoch(train) [10][ 740/1879] lr: 2.0000e-02 eta: 17:29:58 time: 0.4235 data_time: 0.0134 memory: 6717 grad_norm: 3.0733 loss: 2.1487 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1487 2023/04/13 21:54:51 - mmengine - INFO - Epoch(train) [10][ 760/1879] lr: 2.0000e-02 eta: 17:29:48 time: 0.3563 data_time: 0.0147 memory: 6717 grad_norm: 2.9985 loss: 2.1128 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1128 2023/04/13 21:55:00 - mmengine - INFO - Epoch(train) [10][ 780/1879] lr: 2.0000e-02 eta: 17:29:56 time: 0.4512 data_time: 0.0130 memory: 6717 grad_norm: 2.9380 loss: 2.2657 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.2657 2023/04/13 21:55:06 - mmengine - INFO - Epoch(train) [10][ 800/1879] lr: 2.0000e-02 eta: 17:29:35 time: 0.2993 data_time: 0.0132 memory: 6717 grad_norm: 3.0881 loss: 1.8473 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.8473 2023/04/13 21:55:14 - mmengine - INFO - Epoch(train) [10][ 820/1879] lr: 2.0000e-02 eta: 17:29:39 time: 0.4286 data_time: 0.0143 memory: 6717 grad_norm: 2.9857 loss: 2.0512 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0512 2023/04/13 21:55:21 - mmengine - INFO - Epoch(train) [10][ 840/1879] lr: 2.0000e-02 eta: 17:29:26 time: 0.3377 data_time: 0.0132 memory: 6717 grad_norm: 3.0683 loss: 1.9114 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9114 2023/04/13 21:55:29 - mmengine - INFO - Epoch(train) [10][ 860/1879] lr: 2.0000e-02 eta: 17:29:22 time: 0.3916 data_time: 0.0132 memory: 6717 grad_norm: 3.0408 loss: 2.0817 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0817 2023/04/13 21:55:35 - mmengine - INFO - Epoch(train) [10][ 880/1879] lr: 2.0000e-02 eta: 17:29:00 time: 0.2928 data_time: 0.0146 memory: 6717 grad_norm: 2.9558 loss: 2.1407 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1407 2023/04/13 21:55:43 - mmengine - INFO - Epoch(train) [10][ 900/1879] lr: 2.0000e-02 eta: 17:29:04 time: 0.4302 data_time: 0.0140 memory: 6717 grad_norm: 3.0236 loss: 2.1578 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.1578 2023/04/13 21:55:49 - mmengine - INFO - Epoch(train) [10][ 920/1879] lr: 2.0000e-02 eta: 17:28:44 time: 0.3030 data_time: 0.0141 memory: 6717 grad_norm: 3.0581 loss: 2.2989 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.2989 2023/04/13 21:55:57 - mmengine - INFO - Epoch(train) [10][ 940/1879] lr: 2.0000e-02 eta: 17:28:42 time: 0.4007 data_time: 0.0135 memory: 6717 grad_norm: 2.9300 loss: 2.1508 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.1508 2023/04/13 21:56:05 - mmengine - INFO - Epoch(train) [10][ 960/1879] lr: 2.0000e-02 eta: 17:28:36 time: 0.3775 data_time: 0.0127 memory: 6717 grad_norm: 2.8962 loss: 1.8629 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.8629 2023/04/13 21:56:13 - mmengine - INFO - Epoch(train) [10][ 980/1879] lr: 2.0000e-02 eta: 17:28:37 time: 0.4137 data_time: 0.0128 memory: 6717 grad_norm: 3.0170 loss: 1.9825 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.9825 2023/04/13 21:56:19 - mmengine - INFO - Epoch(train) [10][1000/1879] lr: 2.0000e-02 eta: 17:28:16 time: 0.2962 data_time: 0.0149 memory: 6717 grad_norm: 3.0038 loss: 2.0536 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 2.0536 2023/04/13 21:56:28 - mmengine - INFO - Epoch(train) [10][1020/1879] lr: 2.0000e-02 eta: 17:28:21 time: 0.4349 data_time: 0.0123 memory: 6717 grad_norm: 3.0012 loss: 2.2240 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.2240 2023/04/13 21:56:35 - mmengine - INFO - Epoch(train) [10][1040/1879] lr: 2.0000e-02 eta: 17:28:07 time: 0.3366 data_time: 0.0147 memory: 6717 grad_norm: 3.0177 loss: 2.0806 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.0806 2023/04/13 21:56:44 - mmengine - INFO - Epoch(train) [10][1060/1879] lr: 2.0000e-02 eta: 17:28:17 time: 0.4637 data_time: 0.0131 memory: 6717 grad_norm: 3.0404 loss: 1.8718 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8718 2023/04/13 21:56:50 - mmengine - INFO - Epoch(train) [10][1080/1879] lr: 2.0000e-02 eta: 17:28:02 time: 0.3261 data_time: 0.0142 memory: 6717 grad_norm: 2.9689 loss: 2.0762 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.0762 2023/04/13 21:56:53 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 21:56:58 - mmengine - INFO - Epoch(train) [10][1100/1879] lr: 2.0000e-02 eta: 17:27:57 time: 0.3872 data_time: 0.0145 memory: 6717 grad_norm: 3.0113 loss: 2.2042 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2042 2023/04/13 21:57:05 - mmengine - INFO - Epoch(train) [10][1120/1879] lr: 2.0000e-02 eta: 17:27:46 time: 0.3509 data_time: 0.0149 memory: 6717 grad_norm: 2.9850 loss: 2.1367 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.1367 2023/04/13 21:57:12 - mmengine - INFO - Epoch(train) [10][1140/1879] lr: 2.0000e-02 eta: 17:27:34 time: 0.3420 data_time: 0.0123 memory: 6717 grad_norm: 3.0244 loss: 2.1331 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1331 2023/04/13 21:57:20 - mmengine - INFO - Epoch(train) [10][1160/1879] lr: 2.0000e-02 eta: 17:27:32 time: 0.3982 data_time: 0.0146 memory: 6717 grad_norm: 2.9748 loss: 1.8660 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8660 2023/04/13 21:57:27 - mmengine - INFO - Epoch(train) [10][1180/1879] lr: 2.0000e-02 eta: 17:27:23 time: 0.3618 data_time: 0.0138 memory: 6717 grad_norm: 3.0175 loss: 2.0766 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.0766 2023/04/13 21:57:34 - mmengine - INFO - Epoch(train) [10][1200/1879] lr: 2.0000e-02 eta: 17:27:06 time: 0.3221 data_time: 0.0192 memory: 6717 grad_norm: 3.0161 loss: 2.0644 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 2.0644 2023/04/13 21:57:42 - mmengine - INFO - Epoch(train) [10][1220/1879] lr: 2.0000e-02 eta: 17:27:05 time: 0.4041 data_time: 0.0123 memory: 6717 grad_norm: 2.9872 loss: 2.3550 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.3550 2023/04/13 21:57:48 - mmengine - INFO - Epoch(train) [10][1240/1879] lr: 2.0000e-02 eta: 17:26:51 time: 0.3328 data_time: 0.0140 memory: 6717 grad_norm: 2.9774 loss: 2.1571 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1571 2023/04/13 21:57:56 - mmengine - INFO - Epoch(train) [10][1260/1879] lr: 2.0000e-02 eta: 17:26:44 time: 0.3737 data_time: 0.0139 memory: 6717 grad_norm: 2.9760 loss: 2.0587 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 2.0587 2023/04/13 21:58:03 - mmengine - INFO - Epoch(train) [10][1280/1879] lr: 2.0000e-02 eta: 17:26:37 time: 0.3697 data_time: 0.0136 memory: 6717 grad_norm: 2.9446 loss: 2.1436 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 2.1436 2023/04/13 21:58:10 - mmengine - INFO - Epoch(train) [10][1300/1879] lr: 2.0000e-02 eta: 17:26:24 time: 0.3434 data_time: 0.0141 memory: 6717 grad_norm: 2.9843 loss: 1.9198 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9198 2023/04/13 21:58:18 - mmengine - INFO - Epoch(train) [10][1320/1879] lr: 2.0000e-02 eta: 17:26:25 time: 0.4130 data_time: 0.0141 memory: 6717 grad_norm: 2.9821 loss: 2.0759 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0759 2023/04/13 21:58:25 - mmengine - INFO - Epoch(train) [10][1340/1879] lr: 2.0000e-02 eta: 17:26:09 time: 0.3239 data_time: 0.0138 memory: 6717 grad_norm: 2.8509 loss: 2.0804 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0804 2023/04/13 21:58:33 - mmengine - INFO - Epoch(train) [10][1360/1879] lr: 2.0000e-02 eta: 17:26:09 time: 0.4112 data_time: 0.0147 memory: 6717 grad_norm: 2.8947 loss: 2.1698 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1698 2023/04/13 21:58:40 - mmengine - INFO - Epoch(train) [10][1380/1879] lr: 2.0000e-02 eta: 17:25:55 time: 0.3314 data_time: 0.0124 memory: 6717 grad_norm: 3.0035 loss: 2.1457 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 2.1457 2023/04/13 21:58:48 - mmengine - INFO - Epoch(train) [10][1400/1879] lr: 2.0000e-02 eta: 17:25:59 time: 0.4321 data_time: 0.0169 memory: 6717 grad_norm: 2.9625 loss: 2.2686 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.2686 2023/04/13 21:58:54 - mmengine - INFO - Epoch(train) [10][1420/1879] lr: 2.0000e-02 eta: 17:25:37 time: 0.2942 data_time: 0.0127 memory: 6717 grad_norm: 2.9604 loss: 2.0844 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0844 2023/04/13 21:59:03 - mmengine - INFO - Epoch(train) [10][1440/1879] lr: 2.0000e-02 eta: 17:25:41 time: 0.4298 data_time: 0.0155 memory: 6717 grad_norm: 2.9028 loss: 1.9728 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9728 2023/04/13 21:59:09 - mmengine - INFO - Epoch(train) [10][1460/1879] lr: 2.0000e-02 eta: 17:25:22 time: 0.3071 data_time: 0.0133 memory: 6717 grad_norm: 2.9438 loss: 2.1782 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.1782 2023/04/13 21:59:17 - mmengine - INFO - Epoch(train) [10][1480/1879] lr: 2.0000e-02 eta: 17:25:23 time: 0.4177 data_time: 0.0158 memory: 6717 grad_norm: 3.0077 loss: 2.1135 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.1135 2023/04/13 21:59:24 - mmengine - INFO - Epoch(train) [10][1500/1879] lr: 2.0000e-02 eta: 17:25:06 time: 0.3137 data_time: 0.0127 memory: 6717 grad_norm: 3.0799 loss: 2.0166 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0166 2023/04/13 21:59:32 - mmengine - INFO - Epoch(train) [10][1520/1879] lr: 2.0000e-02 eta: 17:25:10 time: 0.4353 data_time: 0.0159 memory: 6717 grad_norm: 2.9192 loss: 2.0791 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.0791 2023/04/13 21:59:38 - mmengine - INFO - Epoch(train) [10][1540/1879] lr: 2.0000e-02 eta: 17:24:51 time: 0.3051 data_time: 0.0129 memory: 6717 grad_norm: 2.9956 loss: 1.9043 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9043 2023/04/13 21:59:47 - mmengine - INFO - Epoch(train) [10][1560/1879] lr: 2.0000e-02 eta: 17:24:52 time: 0.4156 data_time: 0.0158 memory: 6717 grad_norm: 2.9858 loss: 2.1558 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1558 2023/04/13 21:59:53 - mmengine - INFO - Epoch(train) [10][1580/1879] lr: 2.0000e-02 eta: 17:24:32 time: 0.3003 data_time: 0.0124 memory: 6717 grad_norm: 2.8358 loss: 1.8846 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8846 2023/04/13 22:00:01 - mmengine - INFO - Epoch(train) [10][1600/1879] lr: 2.0000e-02 eta: 17:24:34 time: 0.4221 data_time: 0.0150 memory: 6717 grad_norm: 2.9764 loss: 2.3451 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3451 2023/04/13 22:00:08 - mmengine - INFO - Epoch(train) [10][1620/1879] lr: 2.0000e-02 eta: 17:24:21 time: 0.3376 data_time: 0.0126 memory: 6717 grad_norm: 3.0576 loss: 2.2588 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2588 2023/04/13 22:00:16 - mmengine - INFO - Epoch(train) [10][1640/1879] lr: 2.0000e-02 eta: 17:24:22 time: 0.4172 data_time: 0.0164 memory: 6717 grad_norm: 2.9505 loss: 1.9771 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9771 2023/04/13 22:00:23 - mmengine - INFO - Epoch(train) [10][1660/1879] lr: 2.0000e-02 eta: 17:24:05 time: 0.3194 data_time: 0.0119 memory: 6717 grad_norm: 2.9349 loss: 2.1274 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.1274 2023/04/13 22:00:31 - mmengine - INFO - Epoch(train) [10][1680/1879] lr: 2.0000e-02 eta: 17:24:10 time: 0.4355 data_time: 0.0141 memory: 6717 grad_norm: 2.9258 loss: 2.0515 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.0515 2023/04/13 22:00:38 - mmengine - INFO - Epoch(train) [10][1700/1879] lr: 2.0000e-02 eta: 17:23:56 time: 0.3355 data_time: 0.0150 memory: 6717 grad_norm: 2.9984 loss: 1.9883 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9883 2023/04/13 22:00:46 - mmengine - INFO - Epoch(train) [10][1720/1879] lr: 2.0000e-02 eta: 17:23:50 time: 0.3769 data_time: 0.0132 memory: 6717 grad_norm: 2.9322 loss: 2.0980 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0980 2023/04/13 22:00:53 - mmengine - INFO - Epoch(train) [10][1740/1879] lr: 2.0000e-02 eta: 17:23:41 time: 0.3633 data_time: 0.0145 memory: 6717 grad_norm: 2.9522 loss: 1.9817 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9817 2023/04/13 22:01:01 - mmengine - INFO - Epoch(train) [10][1760/1879] lr: 2.0000e-02 eta: 17:23:42 time: 0.4127 data_time: 0.0134 memory: 6717 grad_norm: 2.9375 loss: 2.0326 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0326 2023/04/13 22:01:07 - mmengine - INFO - Epoch(train) [10][1780/1879] lr: 2.0000e-02 eta: 17:23:22 time: 0.3038 data_time: 0.0143 memory: 6717 grad_norm: 3.0228 loss: 2.1041 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1041 2023/04/13 22:01:15 - mmengine - INFO - Epoch(train) [10][1800/1879] lr: 2.0000e-02 eta: 17:23:15 time: 0.3716 data_time: 0.0154 memory: 6717 grad_norm: 2.9866 loss: 1.9757 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9757 2023/04/13 22:01:22 - mmengine - INFO - Epoch(train) [10][1820/1879] lr: 2.0000e-02 eta: 17:23:06 time: 0.3585 data_time: 0.0128 memory: 6717 grad_norm: 2.9647 loss: 1.8169 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8169 2023/04/13 22:01:29 - mmengine - INFO - Epoch(train) [10][1840/1879] lr: 2.0000e-02 eta: 17:22:58 time: 0.3703 data_time: 0.0146 memory: 6717 grad_norm: 3.0036 loss: 1.8357 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8357 2023/04/13 22:01:38 - mmengine - INFO - Epoch(train) [10][1860/1879] lr: 2.0000e-02 eta: 17:23:02 time: 0.4314 data_time: 0.0141 memory: 6717 grad_norm: 3.0616 loss: 2.0192 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0192 2023/04/13 22:01:44 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 22:01:44 - mmengine - INFO - Epoch(train) [10][1879/1879] lr: 2.0000e-02 eta: 17:22:42 time: 0.2872 data_time: 0.0118 memory: 6717 grad_norm: 3.1226 loss: 1.9268 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.9268 2023/04/13 22:01:58 - mmengine - INFO - Epoch(val) [10][ 20/155] eta: 0:01:35 time: 0.7042 data_time: 0.6708 memory: 1391 2023/04/13 22:02:04 - mmengine - INFO - Epoch(val) [10][ 40/155] eta: 0:00:58 time: 0.3153 data_time: 0.2826 memory: 1391 2023/04/13 22:02:12 - mmengine - INFO - Epoch(val) [10][ 60/155] eta: 0:00:45 time: 0.4082 data_time: 0.3756 memory: 1391 2023/04/13 22:02:18 - mmengine - INFO - Epoch(val) [10][ 80/155] eta: 0:00:32 time: 0.3079 data_time: 0.2750 memory: 1391 2023/04/13 22:02:26 - mmengine - INFO - Epoch(val) [10][100/155] eta: 0:00:23 time: 0.3991 data_time: 0.3662 memory: 1391 2023/04/13 22:02:34 - mmengine - INFO - Epoch(val) [10][120/155] eta: 0:00:14 time: 0.3632 data_time: 0.3309 memory: 1391 2023/04/13 22:02:42 - mmengine - INFO - Epoch(val) [10][140/155] eta: 0:00:06 time: 0.4086 data_time: 0.3756 memory: 1391 2023/04/13 22:02:49 - mmengine - INFO - Epoch(val) [10][155/155] acc/top1: 0.5405 acc/top5: 0.7914 acc/mean1: 0.5403 data_time: 0.3665 time: 0.3982 2023/04/13 22:02:49 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_9.pth is removed 2023/04/13 22:02:50 - mmengine - INFO - The best checkpoint with 0.5405 acc/top1 at 10 epoch is saved to best_acc_top1_epoch_10.pth. 2023/04/13 22:02:59 - mmengine - INFO - Epoch(train) [11][ 20/1879] lr: 2.0000e-02 eta: 17:22:53 time: 0.4700 data_time: 0.3336 memory: 6717 grad_norm: 2.9337 loss: 2.0053 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0053 2023/04/13 22:03:06 - mmengine - INFO - Epoch(train) [11][ 40/1879] lr: 2.0000e-02 eta: 17:22:38 time: 0.3300 data_time: 0.1975 memory: 6717 grad_norm: 3.0098 loss: 2.1372 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.1372 2023/04/13 22:03:14 - mmengine - INFO - Epoch(train) [11][ 60/1879] lr: 2.0000e-02 eta: 17:22:41 time: 0.4276 data_time: 0.2941 memory: 6717 grad_norm: 4.3624 loss: 2.1552 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.1552 2023/04/13 22:03:21 - mmengine - INFO - Epoch(train) [11][ 80/1879] lr: 2.0000e-02 eta: 17:22:27 time: 0.3359 data_time: 0.2049 memory: 6717 grad_norm: 3.1413 loss: 2.1199 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.1199 2023/04/13 22:03:29 - mmengine - INFO - Epoch(train) [11][ 100/1879] lr: 2.0000e-02 eta: 17:22:24 time: 0.3947 data_time: 0.2344 memory: 6717 grad_norm: 3.0555 loss: 2.1951 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 2.1951 2023/04/13 22:03:35 - mmengine - INFO - Epoch(train) [11][ 120/1879] lr: 2.0000e-02 eta: 17:22:06 time: 0.3107 data_time: 0.1305 memory: 6717 grad_norm: 3.0659 loss: 2.1294 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.1294 2023/04/13 22:03:43 - mmengine - INFO - Epoch(train) [11][ 140/1879] lr: 2.0000e-02 eta: 17:22:06 time: 0.4079 data_time: 0.2098 memory: 6717 grad_norm: 2.9705 loss: 2.0036 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0036 2023/04/13 22:03:50 - mmengine - INFO - Epoch(train) [11][ 160/1879] lr: 2.0000e-02 eta: 17:21:50 time: 0.3212 data_time: 0.1325 memory: 6717 grad_norm: 3.0150 loss: 1.9158 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9158 2023/04/13 22:03:58 - mmengine - INFO - Epoch(train) [11][ 180/1879] lr: 2.0000e-02 eta: 17:21:48 time: 0.4008 data_time: 0.1902 memory: 6717 grad_norm: 2.9971 loss: 1.9142 top1_acc: 0.1875 top5_acc: 0.6250 loss_cls: 1.9142 2023/04/13 22:04:05 - mmengine - INFO - Epoch(train) [11][ 200/1879] lr: 2.0000e-02 eta: 17:21:39 time: 0.3627 data_time: 0.1412 memory: 6717 grad_norm: 3.0755 loss: 2.1972 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1972 2023/04/13 22:04:09 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 22:04:13 - mmengine - INFO - Epoch(train) [11][ 220/1879] lr: 2.0000e-02 eta: 17:21:37 time: 0.4009 data_time: 0.1537 memory: 6717 grad_norm: 2.9293 loss: 2.1250 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.1250 2023/04/13 22:04:20 - mmengine - INFO - Epoch(train) [11][ 240/1879] lr: 2.0000e-02 eta: 17:21:29 time: 0.3652 data_time: 0.1333 memory: 6717 grad_norm: 2.9856 loss: 1.9018 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9018 2023/04/13 22:04:28 - mmengine - INFO - Epoch(train) [11][ 260/1879] lr: 2.0000e-02 eta: 17:21:23 time: 0.3773 data_time: 0.0748 memory: 6717 grad_norm: 2.9505 loss: 2.2887 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.2887 2023/04/13 22:04:36 - mmengine - INFO - Epoch(train) [11][ 280/1879] lr: 2.0000e-02 eta: 17:21:19 time: 0.3917 data_time: 0.0120 memory: 6717 grad_norm: 2.9365 loss: 1.7564 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7564 2023/04/13 22:04:42 - mmengine - INFO - Epoch(train) [11][ 300/1879] lr: 2.0000e-02 eta: 17:21:04 time: 0.3256 data_time: 0.0959 memory: 6717 grad_norm: 2.9707 loss: 2.2177 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 2.2177 2023/04/13 22:04:50 - mmengine - INFO - Epoch(train) [11][ 320/1879] lr: 2.0000e-02 eta: 17:21:01 time: 0.3951 data_time: 0.0297 memory: 6717 grad_norm: 2.9119 loss: 2.1088 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1088 2023/04/13 22:04:58 - mmengine - INFO - Epoch(train) [11][ 340/1879] lr: 2.0000e-02 eta: 17:20:53 time: 0.3666 data_time: 0.0150 memory: 6717 grad_norm: 2.9932 loss: 2.1167 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.1167 2023/04/13 22:05:04 - mmengine - INFO - Epoch(train) [11][ 360/1879] lr: 2.0000e-02 eta: 17:20:39 time: 0.3328 data_time: 0.0246 memory: 6717 grad_norm: 2.9562 loss: 1.9375 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.9375 2023/04/13 22:05:12 - mmengine - INFO - Epoch(train) [11][ 380/1879] lr: 2.0000e-02 eta: 17:20:37 time: 0.4007 data_time: 0.0683 memory: 6717 grad_norm: 2.9770 loss: 2.3970 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.3970 2023/04/13 22:05:19 - mmengine - INFO - Epoch(train) [11][ 400/1879] lr: 2.0000e-02 eta: 17:20:21 time: 0.3196 data_time: 0.0719 memory: 6717 grad_norm: 3.0042 loss: 1.9885 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.9885 2023/04/13 22:05:26 - mmengine - INFO - Epoch(train) [11][ 420/1879] lr: 2.0000e-02 eta: 17:20:14 time: 0.3752 data_time: 0.1328 memory: 6717 grad_norm: 2.9145 loss: 2.0858 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.0858 2023/04/13 22:05:33 - mmengine - INFO - Epoch(train) [11][ 440/1879] lr: 2.0000e-02 eta: 17:20:02 time: 0.3438 data_time: 0.1101 memory: 6717 grad_norm: 2.9786 loss: 2.0723 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.0723 2023/04/13 22:05:41 - mmengine - INFO - Epoch(train) [11][ 460/1879] lr: 2.0000e-02 eta: 17:19:56 time: 0.3760 data_time: 0.1145 memory: 6717 grad_norm: 2.9899 loss: 2.2600 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.2600 2023/04/13 22:05:48 - mmengine - INFO - Epoch(train) [11][ 480/1879] lr: 2.0000e-02 eta: 17:19:49 time: 0.3747 data_time: 0.0600 memory: 6717 grad_norm: 2.9914 loss: 1.8876 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8876 2023/04/13 22:05:55 - mmengine - INFO - Epoch(train) [11][ 500/1879] lr: 2.0000e-02 eta: 17:19:43 time: 0.3740 data_time: 0.1986 memory: 6717 grad_norm: 2.9461 loss: 2.1913 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.1913 2023/04/13 22:06:02 - mmengine - INFO - Epoch(train) [11][ 520/1879] lr: 2.0000e-02 eta: 17:19:31 time: 0.3463 data_time: 0.1918 memory: 6717 grad_norm: 2.9781 loss: 1.8992 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8992 2023/04/13 22:06:10 - mmengine - INFO - Epoch(train) [11][ 540/1879] lr: 2.0000e-02 eta: 17:19:29 time: 0.4003 data_time: 0.2540 memory: 6717 grad_norm: 2.9479 loss: 2.2448 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2448 2023/04/13 22:06:18 - mmengine - INFO - Epoch(train) [11][ 560/1879] lr: 2.0000e-02 eta: 17:19:20 time: 0.3622 data_time: 0.1282 memory: 6717 grad_norm: 2.9098 loss: 1.9948 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9948 2023/04/13 22:06:24 - mmengine - INFO - Epoch(train) [11][ 580/1879] lr: 2.0000e-02 eta: 17:19:06 time: 0.3324 data_time: 0.0381 memory: 6717 grad_norm: 2.9539 loss: 1.8523 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8523 2023/04/13 22:06:32 - mmengine - INFO - Epoch(train) [11][ 600/1879] lr: 2.0000e-02 eta: 17:19:04 time: 0.4011 data_time: 0.0414 memory: 6717 grad_norm: 3.0605 loss: 2.1593 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.1593 2023/04/13 22:06:39 - mmengine - INFO - Epoch(train) [11][ 620/1879] lr: 2.0000e-02 eta: 17:18:54 time: 0.3538 data_time: 0.1357 memory: 6717 grad_norm: 2.9461 loss: 2.3542 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.3542 2023/04/13 22:06:46 - mmengine - INFO - Epoch(train) [11][ 640/1879] lr: 2.0000e-02 eta: 17:18:43 time: 0.3468 data_time: 0.1241 memory: 6717 grad_norm: 2.9722 loss: 1.9156 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 1.9156 2023/04/13 22:06:55 - mmengine - INFO - Epoch(train) [11][ 660/1879] lr: 2.0000e-02 eta: 17:18:42 time: 0.4105 data_time: 0.2405 memory: 6717 grad_norm: 2.9282 loss: 2.1766 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.1766 2023/04/13 22:07:01 - mmengine - INFO - Epoch(train) [11][ 680/1879] lr: 2.0000e-02 eta: 17:18:30 time: 0.3402 data_time: 0.1987 memory: 6717 grad_norm: 2.9472 loss: 2.0045 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0045 2023/04/13 22:07:09 - mmengine - INFO - Epoch(train) [11][ 700/1879] lr: 2.0000e-02 eta: 17:18:28 time: 0.4014 data_time: 0.2603 memory: 6717 grad_norm: 2.9811 loss: 2.3009 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3009 2023/04/13 22:07:16 - mmengine - INFO - Epoch(train) [11][ 720/1879] lr: 2.0000e-02 eta: 17:18:16 time: 0.3444 data_time: 0.2051 memory: 6717 grad_norm: 3.0564 loss: 2.1502 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 2.1502 2023/04/13 22:07:25 - mmengine - INFO - Epoch(train) [11][ 740/1879] lr: 2.0000e-02 eta: 17:18:16 time: 0.4151 data_time: 0.2628 memory: 6717 grad_norm: 3.0361 loss: 2.0869 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0869 2023/04/13 22:07:31 - mmengine - INFO - Epoch(train) [11][ 760/1879] lr: 2.0000e-02 eta: 17:17:58 time: 0.3069 data_time: 0.1660 memory: 6717 grad_norm: 2.9597 loss: 1.9534 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9534 2023/04/13 22:07:39 - mmengine - INFO - Epoch(train) [11][ 780/1879] lr: 2.0000e-02 eta: 17:18:00 time: 0.4227 data_time: 0.2600 memory: 6717 grad_norm: 2.9299 loss: 2.1558 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 2.1558 2023/04/13 22:07:46 - mmengine - INFO - Epoch(train) [11][ 800/1879] lr: 2.0000e-02 eta: 17:17:45 time: 0.3291 data_time: 0.1731 memory: 6717 grad_norm: 2.9048 loss: 2.1116 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1116 2023/04/13 22:07:54 - mmengine - INFO - Epoch(train) [11][ 820/1879] lr: 2.0000e-02 eta: 17:17:42 time: 0.3936 data_time: 0.1981 memory: 6717 grad_norm: 3.0268 loss: 1.9853 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9853 2023/04/13 22:08:00 - mmengine - INFO - Epoch(train) [11][ 840/1879] lr: 2.0000e-02 eta: 17:17:29 time: 0.3364 data_time: 0.1376 memory: 6717 grad_norm: 2.9880 loss: 2.0058 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0058 2023/04/13 22:08:08 - mmengine - INFO - Epoch(train) [11][ 860/1879] lr: 2.0000e-02 eta: 17:17:27 time: 0.4042 data_time: 0.2109 memory: 6717 grad_norm: 2.9738 loss: 2.1620 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1620 2023/04/13 22:08:15 - mmengine - INFO - Epoch(train) [11][ 880/1879] lr: 2.0000e-02 eta: 17:17:16 time: 0.3483 data_time: 0.1181 memory: 6717 grad_norm: 2.9288 loss: 2.2069 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 2.2069 2023/04/13 22:08:22 - mmengine - INFO - Epoch(train) [11][ 900/1879] lr: 2.0000e-02 eta: 17:17:05 time: 0.3449 data_time: 0.0870 memory: 6717 grad_norm: 2.9552 loss: 2.0501 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.0501 2023/04/13 22:08:31 - mmengine - INFO - Epoch(train) [11][ 920/1879] lr: 2.0000e-02 eta: 17:17:04 time: 0.4118 data_time: 0.0122 memory: 6717 grad_norm: 2.9530 loss: 1.9570 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9570 2023/04/13 22:08:38 - mmengine - INFO - Epoch(train) [11][ 940/1879] lr: 2.0000e-02 eta: 17:16:55 time: 0.3614 data_time: 0.0172 memory: 6717 grad_norm: 2.9667 loss: 1.8640 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8640 2023/04/13 22:08:46 - mmengine - INFO - Epoch(train) [11][ 960/1879] lr: 2.0000e-02 eta: 17:16:54 time: 0.4033 data_time: 0.0120 memory: 6717 grad_norm: 2.9822 loss: 1.9624 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.9624 2023/04/13 22:08:53 - mmengine - INFO - Epoch(train) [11][ 980/1879] lr: 2.0000e-02 eta: 17:16:41 time: 0.3401 data_time: 0.0153 memory: 6717 grad_norm: 2.9386 loss: 1.9176 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9176 2023/04/13 22:09:01 - mmengine - INFO - Epoch(train) [11][1000/1879] lr: 2.0000e-02 eta: 17:16:41 time: 0.4090 data_time: 0.0118 memory: 6717 grad_norm: 3.0222 loss: 2.2257 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.2257 2023/04/13 22:09:09 - mmengine - INFO - Epoch(train) [11][1020/1879] lr: 2.0000e-02 eta: 17:16:40 time: 0.4128 data_time: 0.0144 memory: 6717 grad_norm: 3.0089 loss: 2.0364 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.0364 2023/04/13 22:09:16 - mmengine - INFO - Epoch(train) [11][1040/1879] lr: 2.0000e-02 eta: 17:16:25 time: 0.3233 data_time: 0.0121 memory: 6717 grad_norm: 2.8547 loss: 1.9287 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.9287 2023/04/13 22:09:24 - mmengine - INFO - Epoch(train) [11][1060/1879] lr: 2.0000e-02 eta: 17:16:30 time: 0.4411 data_time: 0.0141 memory: 6717 grad_norm: 2.9972 loss: 2.0209 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0209 2023/04/13 22:09:31 - mmengine - INFO - Epoch(train) [11][1080/1879] lr: 2.0000e-02 eta: 17:16:12 time: 0.3079 data_time: 0.0141 memory: 6717 grad_norm: 3.0140 loss: 1.8948 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8948 2023/04/13 22:09:39 - mmengine - INFO - Epoch(train) [11][1100/1879] lr: 2.0000e-02 eta: 17:16:14 time: 0.4265 data_time: 0.0144 memory: 6717 grad_norm: 2.9580 loss: 2.0033 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0033 2023/04/13 22:09:46 - mmengine - INFO - Epoch(train) [11][1120/1879] lr: 2.0000e-02 eta: 17:15:58 time: 0.3223 data_time: 0.0134 memory: 6717 grad_norm: 2.9853 loss: 2.0457 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0457 2023/04/13 22:09:54 - mmengine - INFO - Epoch(train) [11][1140/1879] lr: 2.0000e-02 eta: 17:16:03 time: 0.4433 data_time: 0.0143 memory: 6717 grad_norm: 2.9211 loss: 1.8540 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8540 2023/04/13 22:10:00 - mmengine - INFO - Epoch(train) [11][1160/1879] lr: 2.0000e-02 eta: 17:15:45 time: 0.3050 data_time: 0.0144 memory: 6717 grad_norm: 2.9840 loss: 2.0686 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.0686 2023/04/13 22:10:09 - mmengine - INFO - Epoch(train) [11][1180/1879] lr: 2.0000e-02 eta: 17:15:44 time: 0.4080 data_time: 0.0130 memory: 6717 grad_norm: 2.8909 loss: 2.1175 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1175 2023/04/13 22:10:15 - mmengine - INFO - Epoch(train) [11][1200/1879] lr: 2.0000e-02 eta: 17:15:30 time: 0.3288 data_time: 0.0132 memory: 6717 grad_norm: 2.9193 loss: 2.1112 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.1112 2023/04/13 22:10:19 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 22:10:24 - mmengine - INFO - Epoch(train) [11][1220/1879] lr: 2.0000e-02 eta: 17:15:32 time: 0.4261 data_time: 0.0133 memory: 6717 grad_norm: 2.9832 loss: 1.8637 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.8637 2023/04/13 22:10:30 - mmengine - INFO - Epoch(train) [11][1240/1879] lr: 2.0000e-02 eta: 17:15:18 time: 0.3340 data_time: 0.0140 memory: 6717 grad_norm: 2.9370 loss: 1.9921 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9921 2023/04/13 22:10:39 - mmengine - INFO - Epoch(train) [11][1260/1879] lr: 2.0000e-02 eta: 17:15:23 time: 0.4402 data_time: 0.0152 memory: 6717 grad_norm: 2.9696 loss: 2.0606 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0606 2023/04/13 22:10:46 - mmengine - INFO - Epoch(train) [11][1280/1879] lr: 2.0000e-02 eta: 17:15:11 time: 0.3432 data_time: 0.0132 memory: 6717 grad_norm: 2.9490 loss: 1.9756 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9756 2023/04/13 22:10:55 - mmengine - INFO - Epoch(train) [11][1300/1879] lr: 2.0000e-02 eta: 17:15:19 time: 0.4615 data_time: 0.0134 memory: 6717 grad_norm: 2.9506 loss: 2.0840 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.0840 2023/04/13 22:11:02 - mmengine - INFO - Epoch(train) [11][1320/1879] lr: 2.0000e-02 eta: 17:15:05 time: 0.3310 data_time: 0.0148 memory: 6717 grad_norm: 2.9605 loss: 2.1036 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1036 2023/04/13 22:11:10 - mmengine - INFO - Epoch(train) [11][1340/1879] lr: 2.0000e-02 eta: 17:15:05 time: 0.4146 data_time: 0.0142 memory: 6717 grad_norm: 2.9896 loss: 2.0858 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0858 2023/04/13 22:11:16 - mmengine - INFO - Epoch(train) [11][1360/1879] lr: 2.0000e-02 eta: 17:14:47 time: 0.3116 data_time: 0.0136 memory: 6717 grad_norm: 2.9990 loss: 1.9399 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9399 2023/04/13 22:11:25 - mmengine - INFO - Epoch(train) [11][1380/1879] lr: 2.0000e-02 eta: 17:14:46 time: 0.4068 data_time: 0.0139 memory: 6717 grad_norm: 2.9338 loss: 2.0902 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0902 2023/04/13 22:11:31 - mmengine - INFO - Epoch(train) [11][1400/1879] lr: 2.0000e-02 eta: 17:14:33 time: 0.3329 data_time: 0.0138 memory: 6717 grad_norm: 2.9166 loss: 2.2701 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.2701 2023/04/13 22:11:40 - mmengine - INFO - Epoch(train) [11][1420/1879] lr: 2.0000e-02 eta: 17:14:34 time: 0.4229 data_time: 0.0141 memory: 6717 grad_norm: 2.9329 loss: 2.1295 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.1295 2023/04/13 22:11:46 - mmengine - INFO - Epoch(train) [11][1440/1879] lr: 2.0000e-02 eta: 17:14:13 time: 0.2905 data_time: 0.0164 memory: 6717 grad_norm: 2.8501 loss: 2.4476 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.4476 2023/04/13 22:11:53 - mmengine - INFO - Epoch(train) [11][1460/1879] lr: 2.0000e-02 eta: 17:14:10 time: 0.3925 data_time: 0.0132 memory: 6717 grad_norm: 2.9119 loss: 1.6505 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6505 2023/04/13 22:12:00 - mmengine - INFO - Epoch(train) [11][1480/1879] lr: 2.0000e-02 eta: 17:13:56 time: 0.3303 data_time: 0.0157 memory: 6717 grad_norm: 2.8748 loss: 2.2943 top1_acc: 0.1875 top5_acc: 0.7500 loss_cls: 2.2943 2023/04/13 22:12:08 - mmengine - INFO - Epoch(train) [11][1500/1879] lr: 2.0000e-02 eta: 17:13:51 time: 0.3845 data_time: 0.0139 memory: 6717 grad_norm: 2.8899 loss: 2.0181 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0181 2023/04/13 22:12:15 - mmengine - INFO - Epoch(train) [11][1520/1879] lr: 2.0000e-02 eta: 17:13:40 time: 0.3494 data_time: 0.0149 memory: 6717 grad_norm: 2.9612 loss: 2.0986 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0986 2023/04/13 22:12:23 - mmengine - INFO - Epoch(train) [11][1540/1879] lr: 2.0000e-02 eta: 17:13:40 time: 0.4187 data_time: 0.0124 memory: 6717 grad_norm: 2.9440 loss: 2.0148 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0148 2023/04/13 22:12:29 - mmengine - INFO - Epoch(train) [11][1560/1879] lr: 2.0000e-02 eta: 17:13:19 time: 0.2859 data_time: 0.0164 memory: 6717 grad_norm: 2.9490 loss: 1.9074 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9074 2023/04/13 22:12:36 - mmengine - INFO - Epoch(train) [11][1580/1879] lr: 2.0000e-02 eta: 17:13:12 time: 0.3726 data_time: 0.0220 memory: 6717 grad_norm: 2.9541 loss: 2.0084 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0084 2023/04/13 22:12:44 - mmengine - INFO - Epoch(train) [11][1600/1879] lr: 2.0000e-02 eta: 17:13:05 time: 0.3738 data_time: 0.1060 memory: 6717 grad_norm: 2.9925 loss: 1.9690 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.9690 2023/04/13 22:12:51 - mmengine - INFO - Epoch(train) [11][1620/1879] lr: 2.0000e-02 eta: 17:12:55 time: 0.3546 data_time: 0.0615 memory: 6717 grad_norm: 2.9216 loss: 1.8999 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8999 2023/04/13 22:12:58 - mmengine - INFO - Epoch(train) [11][1640/1879] lr: 2.0000e-02 eta: 17:12:49 time: 0.3778 data_time: 0.0553 memory: 6717 grad_norm: 2.9590 loss: 2.3038 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3038 2023/04/13 22:13:05 - mmengine - INFO - Epoch(train) [11][1660/1879] lr: 2.0000e-02 eta: 17:12:38 time: 0.3481 data_time: 0.0336 memory: 6717 grad_norm: 3.0196 loss: 2.1207 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.1207 2023/04/13 22:13:13 - mmengine - INFO - Epoch(train) [11][1680/1879] lr: 2.0000e-02 eta: 17:12:35 time: 0.3969 data_time: 0.0142 memory: 6717 grad_norm: 2.9564 loss: 2.2443 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.2443 2023/04/13 22:13:20 - mmengine - INFO - Epoch(train) [11][1700/1879] lr: 2.0000e-02 eta: 17:12:22 time: 0.3353 data_time: 0.0130 memory: 6717 grad_norm: 2.9139 loss: 2.0421 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 2.0421 2023/04/13 22:13:28 - mmengine - INFO - Epoch(train) [11][1720/1879] lr: 2.0000e-02 eta: 17:12:22 time: 0.4115 data_time: 0.0144 memory: 6717 grad_norm: 2.8977 loss: 2.0405 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0405 2023/04/13 22:13:34 - mmengine - INFO - Epoch(train) [11][1740/1879] lr: 2.0000e-02 eta: 17:12:05 time: 0.3119 data_time: 0.0145 memory: 6717 grad_norm: 2.9665 loss: 2.1592 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1592 2023/04/13 22:13:43 - mmengine - INFO - Epoch(train) [11][1760/1879] lr: 2.0000e-02 eta: 17:12:11 time: 0.4511 data_time: 0.0159 memory: 6717 grad_norm: 2.9631 loss: 1.8806 top1_acc: 0.1875 top5_acc: 0.5625 loss_cls: 1.8806 2023/04/13 22:13:50 - mmengine - INFO - Epoch(train) [11][1780/1879] lr: 2.0000e-02 eta: 17:11:55 time: 0.3178 data_time: 0.0123 memory: 6717 grad_norm: 2.9035 loss: 2.1413 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.1413 2023/04/13 22:13:58 - mmengine - INFO - Epoch(train) [11][1800/1879] lr: 2.0000e-02 eta: 17:11:52 time: 0.3974 data_time: 0.0137 memory: 6717 grad_norm: 2.8769 loss: 1.9380 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9380 2023/04/13 22:14:04 - mmengine - INFO - Epoch(train) [11][1820/1879] lr: 2.0000e-02 eta: 17:11:37 time: 0.3229 data_time: 0.0152 memory: 6717 grad_norm: 2.9862 loss: 1.7937 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7937 2023/04/13 22:14:13 - mmengine - INFO - Epoch(train) [11][1840/1879] lr: 2.0000e-02 eta: 17:11:40 time: 0.4390 data_time: 0.0151 memory: 6717 grad_norm: 2.9689 loss: 1.9895 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9895 2023/04/13 22:14:19 - mmengine - INFO - Epoch(train) [11][1860/1879] lr: 2.0000e-02 eta: 17:11:24 time: 0.3134 data_time: 0.0139 memory: 6717 grad_norm: 2.9185 loss: 2.2263 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.2263 2023/04/13 22:14:27 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 22:14:27 - mmengine - INFO - Epoch(train) [11][1879/1879] lr: 2.0000e-02 eta: 17:11:21 time: 0.3852 data_time: 0.0126 memory: 6717 grad_norm: 2.9395 loss: 1.9869 top1_acc: 0.4286 top5_acc: 0.7143 loss_cls: 1.9869 2023/04/13 22:14:36 - mmengine - INFO - Epoch(val) [11][ 20/155] eta: 0:01:02 time: 0.4612 data_time: 0.4288 memory: 1391 2023/04/13 22:14:42 - mmengine - INFO - Epoch(val) [11][ 40/155] eta: 0:00:44 time: 0.3196 data_time: 0.2860 memory: 1391 2023/04/13 22:14:51 - mmengine - INFO - Epoch(val) [11][ 60/155] eta: 0:00:38 time: 0.4333 data_time: 0.4004 memory: 1391 2023/04/13 22:14:57 - mmengine - INFO - Epoch(val) [11][ 80/155] eta: 0:00:28 time: 0.3164 data_time: 0.2830 memory: 1391 2023/04/13 22:15:07 - mmengine - INFO - Epoch(val) [11][100/155] eta: 0:00:21 time: 0.4567 data_time: 0.4236 memory: 1391 2023/04/13 22:15:13 - mmengine - INFO - Epoch(val) [11][120/155] eta: 0:00:13 time: 0.2992 data_time: 0.2664 memory: 1391 2023/04/13 22:15:21 - mmengine - INFO - Epoch(val) [11][140/155] eta: 0:00:05 time: 0.4441 data_time: 0.4108 memory: 1391 2023/04/13 22:15:29 - mmengine - INFO - Epoch(val) [11][155/155] acc/top1: 0.5506 acc/top5: 0.7994 acc/mean1: 0.5505 data_time: 0.3598 time: 0.3924 2023/04/13 22:15:29 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_10.pth is removed 2023/04/13 22:15:29 - mmengine - INFO - The best checkpoint with 0.5506 acc/top1 at 11 epoch is saved to best_acc_top1_epoch_11.pth. 2023/04/13 22:15:39 - mmengine - INFO - Epoch(train) [12][ 20/1879] lr: 2.0000e-02 eta: 17:11:33 time: 0.4880 data_time: 0.2805 memory: 6717 grad_norm: 2.9197 loss: 2.2172 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.2172 2023/04/13 22:15:45 - mmengine - INFO - Epoch(train) [12][ 40/1879] lr: 2.0000e-02 eta: 17:11:18 time: 0.3256 data_time: 0.1001 memory: 6717 grad_norm: 2.9729 loss: 2.1209 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.1209 2023/04/13 22:15:54 - mmengine - INFO - Epoch(train) [12][ 60/1879] lr: 2.0000e-02 eta: 17:11:23 time: 0.4440 data_time: 0.0749 memory: 6717 grad_norm: 2.9723 loss: 2.0044 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.0044 2023/04/13 22:16:01 - mmengine - INFO - Epoch(train) [12][ 80/1879] lr: 2.0000e-02 eta: 17:11:09 time: 0.3317 data_time: 0.0133 memory: 6717 grad_norm: 2.9985 loss: 1.9561 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9561 2023/04/13 22:16:09 - mmengine - INFO - Epoch(train) [12][ 100/1879] lr: 2.0000e-02 eta: 17:11:12 time: 0.4374 data_time: 0.0153 memory: 6717 grad_norm: 2.8911 loss: 1.9765 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9765 2023/04/13 22:16:16 - mmengine - INFO - Epoch(train) [12][ 120/1879] lr: 2.0000e-02 eta: 17:11:03 time: 0.3544 data_time: 0.0132 memory: 6717 grad_norm: 2.8844 loss: 1.8903 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.8903 2023/04/13 22:16:25 - mmengine - INFO - Epoch(train) [12][ 140/1879] lr: 2.0000e-02 eta: 17:11:00 time: 0.4012 data_time: 0.0150 memory: 6717 grad_norm: 2.9711 loss: 1.9511 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9511 2023/04/13 22:16:31 - mmengine - INFO - Epoch(train) [12][ 160/1879] lr: 2.0000e-02 eta: 17:10:43 time: 0.3099 data_time: 0.0144 memory: 6717 grad_norm: 3.0148 loss: 1.8377 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8377 2023/04/13 22:16:39 - mmengine - INFO - Epoch(train) [12][ 180/1879] lr: 2.0000e-02 eta: 17:10:43 time: 0.4177 data_time: 0.0133 memory: 6717 grad_norm: 2.9532 loss: 2.0242 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.0242 2023/04/13 22:16:46 - mmengine - INFO - Epoch(train) [12][ 200/1879] lr: 2.0000e-02 eta: 17:10:32 time: 0.3445 data_time: 0.0170 memory: 6717 grad_norm: 3.0628 loss: 1.9721 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.9721 2023/04/13 22:16:54 - mmengine - INFO - Epoch(train) [12][ 220/1879] lr: 2.0000e-02 eta: 17:10:31 time: 0.4146 data_time: 0.0135 memory: 6717 grad_norm: 2.9281 loss: 1.8560 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8560 2023/04/13 22:17:01 - mmengine - INFO - Epoch(train) [12][ 240/1879] lr: 2.0000e-02 eta: 17:10:17 time: 0.3283 data_time: 0.0142 memory: 6717 grad_norm: 2.9387 loss: 2.1165 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1165 2023/04/13 22:17:09 - mmengine - INFO - Epoch(train) [12][ 260/1879] lr: 2.0000e-02 eta: 17:10:18 time: 0.4231 data_time: 0.0130 memory: 6717 grad_norm: 2.9661 loss: 2.0705 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0705 2023/04/13 22:17:15 - mmengine - INFO - Epoch(train) [12][ 280/1879] lr: 2.0000e-02 eta: 17:10:00 time: 0.3034 data_time: 0.0135 memory: 6717 grad_norm: 3.0014 loss: 2.0623 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.0623 2023/04/13 22:17:23 - mmengine - INFO - Epoch(train) [12][ 300/1879] lr: 2.0000e-02 eta: 17:09:54 time: 0.3756 data_time: 0.0149 memory: 6717 grad_norm: 2.8995 loss: 1.6515 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.6515 2023/04/13 22:17:29 - mmengine - INFO - Epoch(train) [12][ 320/1879] lr: 2.0000e-02 eta: 17:09:36 time: 0.3078 data_time: 0.0133 memory: 6717 grad_norm: 3.0015 loss: 2.2509 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.2509 2023/04/13 22:17:34 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 22:17:38 - mmengine - INFO - Epoch(train) [12][ 340/1879] lr: 2.0000e-02 eta: 17:09:39 time: 0.4336 data_time: 0.0142 memory: 6717 grad_norm: 3.0219 loss: 1.9356 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.9356 2023/04/13 22:17:47 - mmengine - INFO - Epoch(train) [12][ 360/1879] lr: 2.0000e-02 eta: 17:09:44 time: 0.4489 data_time: 0.0139 memory: 6717 grad_norm: 3.0055 loss: 1.9864 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9864 2023/04/13 22:17:53 - mmengine - INFO - Epoch(train) [12][ 380/1879] lr: 2.0000e-02 eta: 17:09:27 time: 0.3113 data_time: 0.0133 memory: 6717 grad_norm: 2.9102 loss: 1.9869 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.9869 2023/04/13 22:18:01 - mmengine - INFO - Epoch(train) [12][ 400/1879] lr: 2.0000e-02 eta: 17:09:28 time: 0.4197 data_time: 0.0149 memory: 6717 grad_norm: 2.9132 loss: 2.0925 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.0925 2023/04/13 22:18:07 - mmengine - INFO - Epoch(train) [12][ 420/1879] lr: 2.0000e-02 eta: 17:09:09 time: 0.2997 data_time: 0.0146 memory: 6717 grad_norm: 3.0104 loss: 1.9093 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9093 2023/04/13 22:18:16 - mmengine - INFO - Epoch(train) [12][ 440/1879] lr: 2.0000e-02 eta: 17:09:11 time: 0.4249 data_time: 0.0142 memory: 6717 grad_norm: 2.8488 loss: 1.9803 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.9803 2023/04/13 22:18:22 - mmengine - INFO - Epoch(train) [12][ 460/1879] lr: 2.0000e-02 eta: 17:08:56 time: 0.3278 data_time: 0.0159 memory: 6717 grad_norm: 2.8958 loss: 2.0966 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0966 2023/04/13 22:18:30 - mmengine - INFO - Epoch(train) [12][ 480/1879] lr: 2.0000e-02 eta: 17:08:54 time: 0.3995 data_time: 0.0129 memory: 6717 grad_norm: 2.9189 loss: 1.9670 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9670 2023/04/13 22:18:37 - mmengine - INFO - Epoch(train) [12][ 500/1879] lr: 2.0000e-02 eta: 17:08:38 time: 0.3156 data_time: 0.0147 memory: 6717 grad_norm: 2.9526 loss: 2.0114 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0114 2023/04/13 22:18:44 - mmengine - INFO - Epoch(train) [12][ 520/1879] lr: 2.0000e-02 eta: 17:08:33 time: 0.3877 data_time: 0.0134 memory: 6717 grad_norm: 2.9224 loss: 1.8811 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.8811 2023/04/13 22:18:51 - mmengine - INFO - Epoch(train) [12][ 540/1879] lr: 2.0000e-02 eta: 17:08:17 time: 0.3121 data_time: 0.0146 memory: 6717 grad_norm: 3.0487 loss: 2.0805 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0805 2023/04/13 22:18:58 - mmengine - INFO - Epoch(train) [12][ 560/1879] lr: 2.0000e-02 eta: 17:08:12 time: 0.3887 data_time: 0.0136 memory: 6717 grad_norm: 2.9477 loss: 1.9256 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9256 2023/04/13 22:19:06 - mmengine - INFO - Epoch(train) [12][ 580/1879] lr: 2.0000e-02 eta: 17:08:06 time: 0.3788 data_time: 0.0156 memory: 6717 grad_norm: 3.0295 loss: 1.9460 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.9460 2023/04/13 22:19:14 - mmengine - INFO - Epoch(train) [12][ 600/1879] lr: 2.0000e-02 eta: 17:08:02 time: 0.3943 data_time: 0.0124 memory: 6717 grad_norm: 3.0920 loss: 1.9757 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9757 2023/04/13 22:19:21 - mmengine - INFO - Epoch(train) [12][ 620/1879] lr: 2.0000e-02 eta: 17:07:51 time: 0.3441 data_time: 0.0151 memory: 6717 grad_norm: 2.9253 loss: 1.9400 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.9400 2023/04/13 22:19:28 - mmengine - INFO - Epoch(train) [12][ 640/1879] lr: 2.0000e-02 eta: 17:07:40 time: 0.3501 data_time: 0.0130 memory: 6717 grad_norm: 2.8760 loss: 2.0758 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0758 2023/04/13 22:19:36 - mmengine - INFO - Epoch(train) [12][ 660/1879] lr: 2.0000e-02 eta: 17:07:36 time: 0.3875 data_time: 0.0421 memory: 6717 grad_norm: 3.0374 loss: 1.7779 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7779 2023/04/13 22:19:42 - mmengine - INFO - Epoch(train) [12][ 680/1879] lr: 2.0000e-02 eta: 17:07:25 time: 0.3485 data_time: 0.0356 memory: 6717 grad_norm: 2.9887 loss: 1.8775 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8775 2023/04/13 22:19:51 - mmengine - INFO - Epoch(train) [12][ 700/1879] lr: 2.0000e-02 eta: 17:07:26 time: 0.4257 data_time: 0.0452 memory: 6717 grad_norm: 2.9423 loss: 1.9892 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9892 2023/04/13 22:19:58 - mmengine - INFO - Epoch(train) [12][ 720/1879] lr: 2.0000e-02 eta: 17:07:13 time: 0.3343 data_time: 0.0126 memory: 6717 grad_norm: 2.9401 loss: 1.7833 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7833 2023/04/13 22:20:06 - mmengine - INFO - Epoch(train) [12][ 740/1879] lr: 2.0000e-02 eta: 17:07:10 time: 0.3960 data_time: 0.0162 memory: 6717 grad_norm: 3.0974 loss: 1.9426 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.9426 2023/04/13 22:20:12 - mmengine - INFO - Epoch(train) [12][ 760/1879] lr: 2.0000e-02 eta: 17:06:58 time: 0.3398 data_time: 0.0306 memory: 6717 grad_norm: 3.0014 loss: 1.9197 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9197 2023/04/13 22:20:21 - mmengine - INFO - Epoch(train) [12][ 780/1879] lr: 2.0000e-02 eta: 17:06:59 time: 0.4236 data_time: 0.0568 memory: 6717 grad_norm: 2.9901 loss: 1.9290 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9290 2023/04/13 22:20:27 - mmengine - INFO - Epoch(train) [12][ 800/1879] lr: 2.0000e-02 eta: 17:06:40 time: 0.2984 data_time: 0.0226 memory: 6717 grad_norm: 2.8952 loss: 1.9072 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9072 2023/04/13 22:20:35 - mmengine - INFO - Epoch(train) [12][ 820/1879] lr: 2.0000e-02 eta: 17:06:40 time: 0.4168 data_time: 0.0235 memory: 6717 grad_norm: 2.9694 loss: 2.0892 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0892 2023/04/13 22:20:41 - mmengine - INFO - Epoch(train) [12][ 840/1879] lr: 2.0000e-02 eta: 17:06:24 time: 0.3141 data_time: 0.0376 memory: 6717 grad_norm: 2.9695 loss: 1.9872 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.9872 2023/04/13 22:20:49 - mmengine - INFO - Epoch(train) [12][ 860/1879] lr: 2.0000e-02 eta: 17:06:20 time: 0.3940 data_time: 0.0154 memory: 6717 grad_norm: 2.9450 loss: 2.0608 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0608 2023/04/13 22:20:56 - mmengine - INFO - Epoch(train) [12][ 880/1879] lr: 2.0000e-02 eta: 17:06:07 time: 0.3353 data_time: 0.0126 memory: 6717 grad_norm: 2.9916 loss: 2.0079 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0079 2023/04/13 22:21:04 - mmengine - INFO - Epoch(train) [12][ 900/1879] lr: 2.0000e-02 eta: 17:06:05 time: 0.4049 data_time: 0.0158 memory: 6717 grad_norm: 2.8790 loss: 2.0935 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0935 2023/04/13 22:21:11 - mmengine - INFO - Epoch(train) [12][ 920/1879] lr: 2.0000e-02 eta: 17:05:50 time: 0.3223 data_time: 0.0128 memory: 6717 grad_norm: 2.9264 loss: 1.8511 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8511 2023/04/13 22:21:19 - mmengine - INFO - Epoch(train) [12][ 940/1879] lr: 2.0000e-02 eta: 17:05:50 time: 0.4123 data_time: 0.0152 memory: 6717 grad_norm: 2.9405 loss: 1.9852 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9852 2023/04/13 22:21:26 - mmengine - INFO - Epoch(train) [12][ 960/1879] lr: 2.0000e-02 eta: 17:05:38 time: 0.3405 data_time: 0.0133 memory: 6717 grad_norm: 2.9076 loss: 2.0069 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.0069 2023/04/13 22:21:34 - mmengine - INFO - Epoch(train) [12][ 980/1879] lr: 2.0000e-02 eta: 17:05:35 time: 0.4017 data_time: 0.0144 memory: 6717 grad_norm: 2.9443 loss: 2.0869 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.0869 2023/04/13 22:21:41 - mmengine - INFO - Epoch(train) [12][1000/1879] lr: 2.0000e-02 eta: 17:05:24 time: 0.3481 data_time: 0.0133 memory: 6717 grad_norm: 2.9693 loss: 1.8939 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8939 2023/04/13 22:21:49 - mmengine - INFO - Epoch(train) [12][1020/1879] lr: 2.0000e-02 eta: 17:05:21 time: 0.3962 data_time: 0.0131 memory: 6717 grad_norm: 2.9737 loss: 2.0780 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0780 2023/04/13 22:21:55 - mmengine - INFO - Epoch(train) [12][1040/1879] lr: 2.0000e-02 eta: 17:05:06 time: 0.3248 data_time: 0.0138 memory: 6717 grad_norm: 2.9864 loss: 1.8408 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8408 2023/04/13 22:22:03 - mmengine - INFO - Epoch(train) [12][1060/1879] lr: 2.0000e-02 eta: 17:05:02 time: 0.3880 data_time: 0.0140 memory: 6717 grad_norm: 2.9266 loss: 2.0861 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.0861 2023/04/13 22:22:09 - mmengine - INFO - Epoch(train) [12][1080/1879] lr: 2.0000e-02 eta: 17:04:47 time: 0.3230 data_time: 0.0146 memory: 6717 grad_norm: 2.9320 loss: 1.7891 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.7891 2023/04/13 22:22:17 - mmengine - INFO - Epoch(train) [12][1100/1879] lr: 2.0000e-02 eta: 17:04:40 time: 0.3717 data_time: 0.0199 memory: 6717 grad_norm: 3.0220 loss: 1.9729 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9729 2023/04/13 22:22:24 - mmengine - INFO - Epoch(train) [12][1120/1879] lr: 2.0000e-02 eta: 17:04:28 time: 0.3427 data_time: 0.0128 memory: 6717 grad_norm: 3.0186 loss: 2.0896 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0896 2023/04/13 22:22:33 - mmengine - INFO - Epoch(train) [12][1140/1879] lr: 2.0000e-02 eta: 17:04:33 time: 0.4484 data_time: 0.0155 memory: 6717 grad_norm: 2.9340 loss: 2.1700 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1700 2023/04/13 22:22:39 - mmengine - INFO - Epoch(train) [12][1160/1879] lr: 2.0000e-02 eta: 17:04:19 time: 0.3234 data_time: 0.0127 memory: 6717 grad_norm: 2.9474 loss: 2.0942 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0942 2023/04/13 22:22:48 - mmengine - INFO - Epoch(train) [12][1180/1879] lr: 2.0000e-02 eta: 17:04:20 time: 0.4284 data_time: 0.0158 memory: 6717 grad_norm: 2.9208 loss: 2.0095 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.0095 2023/04/13 22:22:54 - mmengine - INFO - Epoch(train) [12][1200/1879] lr: 2.0000e-02 eta: 17:04:02 time: 0.2992 data_time: 0.0128 memory: 6717 grad_norm: 2.9671 loss: 2.1384 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1384 2023/04/13 22:23:02 - mmengine - INFO - Epoch(train) [12][1220/1879] lr: 2.0000e-02 eta: 17:04:00 time: 0.4039 data_time: 0.0524 memory: 6717 grad_norm: 2.9883 loss: 1.9318 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9318 2023/04/13 22:23:08 - mmengine - INFO - Epoch(train) [12][1240/1879] lr: 2.0000e-02 eta: 17:03:46 time: 0.3271 data_time: 0.0123 memory: 6717 grad_norm: 2.8505 loss: 2.0381 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0381 2023/04/13 22:23:16 - mmengine - INFO - Epoch(train) [12][1260/1879] lr: 2.0000e-02 eta: 17:03:40 time: 0.3785 data_time: 0.0567 memory: 6717 grad_norm: 2.9125 loss: 1.9556 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9556 2023/04/13 22:23:23 - mmengine - INFO - Epoch(train) [12][1280/1879] lr: 2.0000e-02 eta: 17:03:30 time: 0.3565 data_time: 0.0129 memory: 6717 grad_norm: 2.9374 loss: 1.9525 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9525 2023/04/13 22:23:30 - mmengine - INFO - Epoch(train) [12][1300/1879] lr: 2.0000e-02 eta: 17:03:22 time: 0.3682 data_time: 0.0156 memory: 6717 grad_norm: 2.9072 loss: 2.1070 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1070 2023/04/13 22:23:38 - mmengine - INFO - Epoch(train) [12][1320/1879] lr: 2.0000e-02 eta: 17:03:15 time: 0.3672 data_time: 0.0123 memory: 6717 grad_norm: 2.9100 loss: 2.0707 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0707 2023/04/13 22:23:44 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 22:23:47 - mmengine - INFO - Epoch(train) [12][1340/1879] lr: 2.0000e-02 eta: 17:03:19 time: 0.4464 data_time: 0.0143 memory: 6717 grad_norm: 2.9239 loss: 2.2142 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.2142 2023/04/13 22:23:53 - mmengine - INFO - Epoch(train) [12][1360/1879] lr: 2.0000e-02 eta: 17:03:03 time: 0.3159 data_time: 0.0134 memory: 6717 grad_norm: 2.9477 loss: 2.0139 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0139 2023/04/13 22:24:00 - mmengine - INFO - Epoch(train) [12][1380/1879] lr: 2.0000e-02 eta: 17:02:56 time: 0.3691 data_time: 0.0157 memory: 6717 grad_norm: 2.9146 loss: 1.9721 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9721 2023/04/13 22:24:07 - mmengine - INFO - Epoch(train) [12][1400/1879] lr: 2.0000e-02 eta: 17:02:45 time: 0.3497 data_time: 0.0121 memory: 6717 grad_norm: 2.9468 loss: 2.0180 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.0180 2023/04/13 22:24:14 - mmengine - INFO - Epoch(train) [12][1420/1879] lr: 2.0000e-02 eta: 17:02:33 time: 0.3359 data_time: 0.0156 memory: 6717 grad_norm: 2.9164 loss: 1.8622 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8622 2023/04/13 22:24:22 - mmengine - INFO - Epoch(train) [12][1440/1879] lr: 2.0000e-02 eta: 17:02:30 time: 0.4015 data_time: 0.0124 memory: 6717 grad_norm: 2.9114 loss: 1.9650 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9650 2023/04/13 22:24:28 - mmengine - INFO - Epoch(train) [12][1460/1879] lr: 2.0000e-02 eta: 17:02:15 time: 0.3197 data_time: 0.0143 memory: 6717 grad_norm: 2.8917 loss: 2.1813 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.1813 2023/04/13 22:24:36 - mmengine - INFO - Epoch(train) [12][1480/1879] lr: 2.0000e-02 eta: 17:02:08 time: 0.3732 data_time: 0.0142 memory: 6717 grad_norm: 2.9952 loss: 1.9148 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9148 2023/04/13 22:24:43 - mmengine - INFO - Epoch(train) [12][1500/1879] lr: 2.0000e-02 eta: 17:02:00 time: 0.3655 data_time: 0.0152 memory: 6717 grad_norm: 2.8137 loss: 1.9378 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.9378 2023/04/13 22:24:51 - mmengine - INFO - Epoch(train) [12][1520/1879] lr: 2.0000e-02 eta: 17:01:52 time: 0.3694 data_time: 0.0130 memory: 6717 grad_norm: 3.8324 loss: 2.1761 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.1761 2023/04/13 22:24:58 - mmengine - INFO - Epoch(train) [12][1540/1879] lr: 2.0000e-02 eta: 17:01:43 time: 0.3589 data_time: 0.0145 memory: 6717 grad_norm: 3.0755 loss: 2.0929 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0929 2023/04/13 22:25:06 - mmengine - INFO - Epoch(train) [12][1560/1879] lr: 2.0000e-02 eta: 17:01:40 time: 0.4004 data_time: 0.0142 memory: 6717 grad_norm: 3.0070 loss: 2.0350 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.0350 2023/04/13 22:25:13 - mmengine - INFO - Epoch(train) [12][1580/1879] lr: 2.0000e-02 eta: 17:01:32 time: 0.3655 data_time: 0.0232 memory: 6717 grad_norm: 2.9852 loss: 2.1171 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.1171 2023/04/13 22:25:21 - mmengine - INFO - Epoch(train) [12][1600/1879] lr: 2.0000e-02 eta: 17:01:31 time: 0.4090 data_time: 0.0134 memory: 6717 grad_norm: 2.9984 loss: 2.0826 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0826 2023/04/13 22:25:28 - mmengine - INFO - Epoch(train) [12][1620/1879] lr: 2.0000e-02 eta: 17:01:18 time: 0.3346 data_time: 0.0142 memory: 6717 grad_norm: 2.9170 loss: 2.1900 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1900 2023/04/13 22:25:36 - mmengine - INFO - Epoch(train) [12][1640/1879] lr: 2.0000e-02 eta: 17:01:18 time: 0.4164 data_time: 0.0147 memory: 6717 grad_norm: 2.9568 loss: 1.9876 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.9876 2023/04/13 22:25:43 - mmengine - INFO - Epoch(train) [12][1660/1879] lr: 2.0000e-02 eta: 17:01:04 time: 0.3272 data_time: 0.0137 memory: 6717 grad_norm: 2.9206 loss: 1.9573 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9573 2023/04/13 22:25:51 - mmengine - INFO - Epoch(train) [12][1680/1879] lr: 2.0000e-02 eta: 17:01:02 time: 0.4092 data_time: 0.0139 memory: 6717 grad_norm: 2.9046 loss: 2.0151 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0151 2023/04/13 22:25:58 - mmengine - INFO - Epoch(train) [12][1700/1879] lr: 2.0000e-02 eta: 17:00:51 time: 0.3425 data_time: 0.0124 memory: 6717 grad_norm: 2.9304 loss: 1.9810 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9810 2023/04/13 22:26:06 - mmengine - INFO - Epoch(train) [12][1720/1879] lr: 2.0000e-02 eta: 17:00:49 time: 0.4102 data_time: 0.0145 memory: 6717 grad_norm: 2.8553 loss: 2.0827 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0827 2023/04/13 22:26:13 - mmengine - INFO - Epoch(train) [12][1740/1879] lr: 2.0000e-02 eta: 17:00:35 time: 0.3255 data_time: 0.0134 memory: 6717 grad_norm: 2.9036 loss: 1.9427 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9427 2023/04/13 22:26:20 - mmengine - INFO - Epoch(train) [12][1760/1879] lr: 2.0000e-02 eta: 17:00:30 time: 0.3868 data_time: 0.0144 memory: 6717 grad_norm: 2.9248 loss: 1.8590 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8590 2023/04/13 22:26:27 - mmengine - INFO - Epoch(train) [12][1780/1879] lr: 2.0000e-02 eta: 17:00:17 time: 0.3307 data_time: 0.0139 memory: 6717 grad_norm: 3.0096 loss: 1.9895 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9895 2023/04/13 22:26:36 - mmengine - INFO - Epoch(train) [12][1800/1879] lr: 2.0000e-02 eta: 17:00:19 time: 0.4322 data_time: 0.0149 memory: 6717 grad_norm: 2.9276 loss: 2.2011 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 2.2011 2023/04/13 22:26:42 - mmengine - INFO - Epoch(train) [12][1820/1879] lr: 2.0000e-02 eta: 17:00:05 time: 0.3273 data_time: 0.0122 memory: 6717 grad_norm: 2.9545 loss: 1.8401 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.8401 2023/04/13 22:26:50 - mmengine - INFO - Epoch(train) [12][1840/1879] lr: 2.0000e-02 eta: 17:00:05 time: 0.4194 data_time: 0.0138 memory: 6717 grad_norm: 2.9269 loss: 2.0236 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0236 2023/04/13 22:26:57 - mmengine - INFO - Epoch(train) [12][1860/1879] lr: 2.0000e-02 eta: 16:59:49 time: 0.3092 data_time: 0.0144 memory: 6717 grad_norm: 2.9456 loss: 2.1246 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1246 2023/04/13 22:27:04 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 22:27:04 - mmengine - INFO - Epoch(train) [12][1879/1879] lr: 2.0000e-02 eta: 16:59:47 time: 0.3913 data_time: 0.0113 memory: 6717 grad_norm: 2.9596 loss: 2.0442 top1_acc: 0.4286 top5_acc: 0.8571 loss_cls: 2.0442 2023/04/13 22:27:04 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/04/13 22:27:14 - mmengine - INFO - Epoch(val) [12][ 20/155] eta: 0:01:01 time: 0.4563 data_time: 0.4234 memory: 1391 2023/04/13 22:27:20 - mmengine - INFO - Epoch(val) [12][ 40/155] eta: 0:00:44 time: 0.3180 data_time: 0.2859 memory: 1391 2023/04/13 22:27:29 - mmengine - INFO - Epoch(val) [12][ 60/155] eta: 0:00:37 time: 0.4252 data_time: 0.3916 memory: 1391 2023/04/13 22:27:35 - mmengine - INFO - Epoch(val) [12][ 80/155] eta: 0:00:28 time: 0.3168 data_time: 0.2840 memory: 1391 2023/04/13 22:27:44 - mmengine - INFO - Epoch(val) [12][100/155] eta: 0:00:21 time: 0.4239 data_time: 0.3911 memory: 1391 2023/04/13 22:27:50 - mmengine - INFO - Epoch(val) [12][120/155] eta: 0:00:13 time: 0.3383 data_time: 0.3048 memory: 1391 2023/04/13 22:28:00 - mmengine - INFO - Epoch(val) [12][140/155] eta: 0:00:05 time: 0.4855 data_time: 0.4527 memory: 1391 2023/04/13 22:28:07 - mmengine - INFO - Epoch(val) [12][155/155] acc/top1: 0.5366 acc/top5: 0.7879 acc/mean1: 0.5366 data_time: 0.4181 time: 0.4503 2023/04/13 22:28:17 - mmengine - INFO - Epoch(train) [13][ 20/1879] lr: 2.0000e-02 eta: 16:59:55 time: 0.4804 data_time: 0.2614 memory: 6717 grad_norm: 2.9466 loss: 1.8030 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8030 2023/04/13 22:28:25 - mmengine - INFO - Epoch(train) [13][ 40/1879] lr: 2.0000e-02 eta: 16:59:53 time: 0.4016 data_time: 0.0480 memory: 6717 grad_norm: 2.8845 loss: 1.9650 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9650 2023/04/13 22:28:32 - mmengine - INFO - Epoch(train) [13][ 60/1879] lr: 2.0000e-02 eta: 16:59:40 time: 0.3375 data_time: 0.0154 memory: 6717 grad_norm: 2.9785 loss: 1.9493 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9493 2023/04/13 22:28:39 - mmengine - INFO - Epoch(train) [13][ 80/1879] lr: 2.0000e-02 eta: 16:59:34 time: 0.3787 data_time: 0.0706 memory: 6717 grad_norm: 2.9325 loss: 1.9816 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9816 2023/04/13 22:28:47 - mmengine - INFO - Epoch(train) [13][ 100/1879] lr: 2.0000e-02 eta: 16:59:30 time: 0.3914 data_time: 0.0155 memory: 6717 grad_norm: 2.9546 loss: 1.9112 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.9112 2023/04/13 22:28:54 - mmengine - INFO - Epoch(train) [13][ 120/1879] lr: 2.0000e-02 eta: 16:59:19 time: 0.3438 data_time: 0.0141 memory: 6717 grad_norm: 2.9706 loss: 1.9632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9632 2023/04/13 22:29:02 - mmengine - INFO - Epoch(train) [13][ 140/1879] lr: 2.0000e-02 eta: 16:59:18 time: 0.4138 data_time: 0.0147 memory: 6717 grad_norm: 2.9224 loss: 2.1760 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1760 2023/04/13 22:29:09 - mmengine - INFO - Epoch(train) [13][ 160/1879] lr: 2.0000e-02 eta: 16:59:09 time: 0.3590 data_time: 0.0139 memory: 6717 grad_norm: 2.8926 loss: 1.8657 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8657 2023/04/13 22:29:16 - mmengine - INFO - Epoch(train) [13][ 180/1879] lr: 2.0000e-02 eta: 16:58:59 time: 0.3517 data_time: 0.0131 memory: 6717 grad_norm: 2.9221 loss: 1.9730 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.9730 2023/04/13 22:29:24 - mmengine - INFO - Epoch(train) [13][ 200/1879] lr: 2.0000e-02 eta: 16:58:56 time: 0.4023 data_time: 0.0134 memory: 6717 grad_norm: 2.8932 loss: 1.9695 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9695 2023/04/13 22:29:32 - mmengine - INFO - Epoch(train) [13][ 220/1879] lr: 2.0000e-02 eta: 16:58:46 time: 0.3569 data_time: 0.0133 memory: 6717 grad_norm: 2.8529 loss: 2.1001 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1001 2023/04/13 22:29:39 - mmengine - INFO - Epoch(train) [13][ 240/1879] lr: 2.0000e-02 eta: 16:58:40 time: 0.3747 data_time: 0.0147 memory: 6717 grad_norm: 2.9625 loss: 1.9759 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9759 2023/04/13 22:29:48 - mmengine - INFO - Epoch(train) [13][ 260/1879] lr: 2.0000e-02 eta: 16:58:40 time: 0.4254 data_time: 0.0133 memory: 6717 grad_norm: 2.9746 loss: 2.0458 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0458 2023/04/13 22:29:54 - mmengine - INFO - Epoch(train) [13][ 280/1879] lr: 2.0000e-02 eta: 16:58:27 time: 0.3260 data_time: 0.0131 memory: 6717 grad_norm: 3.0127 loss: 2.1161 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.1161 2023/04/13 22:30:01 - mmengine - INFO - Epoch(train) [13][ 300/1879] lr: 2.0000e-02 eta: 16:58:17 time: 0.3559 data_time: 0.0140 memory: 6717 grad_norm: 2.8837 loss: 1.9962 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9962 2023/04/13 22:30:09 - mmengine - INFO - Epoch(train) [13][ 320/1879] lr: 2.0000e-02 eta: 16:58:12 time: 0.3872 data_time: 0.0137 memory: 6717 grad_norm: 2.9116 loss: 2.0177 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0177 2023/04/13 22:30:16 - mmengine - INFO - Epoch(train) [13][ 340/1879] lr: 2.0000e-02 eta: 16:58:02 time: 0.3506 data_time: 0.0138 memory: 6717 grad_norm: 2.9698 loss: 2.1022 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.1022 2023/04/13 22:30:23 - mmengine - INFO - Epoch(train) [13][ 360/1879] lr: 2.0000e-02 eta: 16:57:53 time: 0.3608 data_time: 0.0139 memory: 6717 grad_norm: 2.8259 loss: 1.9606 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9606 2023/04/13 22:30:32 - mmengine - INFO - Epoch(train) [13][ 380/1879] lr: 2.0000e-02 eta: 16:57:56 time: 0.4395 data_time: 0.0146 memory: 6717 grad_norm: 2.8965 loss: 2.0062 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0062 2023/04/13 22:30:38 - mmengine - INFO - Epoch(train) [13][ 400/1879] lr: 2.0000e-02 eta: 16:57:38 time: 0.2984 data_time: 0.0157 memory: 6717 grad_norm: 2.9848 loss: 1.8375 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8375 2023/04/13 22:30:45 - mmengine - INFO - Epoch(train) [13][ 420/1879] lr: 2.0000e-02 eta: 16:57:32 time: 0.3768 data_time: 0.0136 memory: 6717 grad_norm: 2.9654 loss: 1.9896 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9896 2023/04/13 22:30:53 - mmengine - INFO - Epoch(train) [13][ 440/1879] lr: 2.0000e-02 eta: 16:57:24 time: 0.3723 data_time: 0.0153 memory: 6717 grad_norm: 2.9719 loss: 1.9883 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9883 2023/04/13 22:30:58 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 22:31:01 - mmengine - INFO - Epoch(train) [13][ 460/1879] lr: 2.0000e-02 eta: 16:57:20 time: 0.3940 data_time: 0.0139 memory: 6717 grad_norm: 2.9318 loss: 1.9808 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9808 2023/04/13 22:31:08 - mmengine - INFO - Epoch(train) [13][ 480/1879] lr: 2.0000e-02 eta: 16:57:08 time: 0.3374 data_time: 0.0141 memory: 6717 grad_norm: 2.9214 loss: 1.9869 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9869 2023/04/13 22:31:16 - mmengine - INFO - Epoch(train) [13][ 500/1879] lr: 2.0000e-02 eta: 16:57:05 time: 0.4013 data_time: 0.0129 memory: 6717 grad_norm: 2.9298 loss: 1.8428 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8428 2023/04/13 22:31:22 - mmengine - INFO - Epoch(train) [13][ 520/1879] lr: 2.0000e-02 eta: 16:56:52 time: 0.3311 data_time: 0.0143 memory: 6717 grad_norm: 2.9435 loss: 2.0546 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0546 2023/04/13 22:31:30 - mmengine - INFO - Epoch(train) [13][ 540/1879] lr: 2.0000e-02 eta: 16:56:51 time: 0.4134 data_time: 0.0132 memory: 6717 grad_norm: 2.9407 loss: 1.9472 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9472 2023/04/13 22:31:37 - mmengine - INFO - Epoch(train) [13][ 560/1879] lr: 2.0000e-02 eta: 16:56:38 time: 0.3298 data_time: 0.0154 memory: 6717 grad_norm: 2.9196 loss: 2.0708 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0708 2023/04/13 22:31:45 - mmengine - INFO - Epoch(train) [13][ 580/1879] lr: 2.0000e-02 eta: 16:56:35 time: 0.3982 data_time: 0.0148 memory: 6717 grad_norm: 2.9580 loss: 2.1244 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1244 2023/04/13 22:31:52 - mmengine - INFO - Epoch(train) [13][ 600/1879] lr: 2.0000e-02 eta: 16:56:24 time: 0.3466 data_time: 0.0134 memory: 6717 grad_norm: 2.9459 loss: 1.8339 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8339 2023/04/13 22:32:00 - mmengine - INFO - Epoch(train) [13][ 620/1879] lr: 2.0000e-02 eta: 16:56:24 time: 0.4247 data_time: 0.0156 memory: 6717 grad_norm: 2.9460 loss: 1.9762 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9762 2023/04/13 22:32:07 - mmengine - INFO - Epoch(train) [13][ 640/1879] lr: 2.0000e-02 eta: 16:56:10 time: 0.3199 data_time: 0.0122 memory: 6717 grad_norm: 2.8745 loss: 1.9805 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.9805 2023/04/13 22:32:14 - mmengine - INFO - Epoch(train) [13][ 660/1879] lr: 2.0000e-02 eta: 16:56:02 time: 0.3707 data_time: 0.0152 memory: 6717 grad_norm: 2.9836 loss: 2.0900 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0900 2023/04/13 22:32:21 - mmengine - INFO - Epoch(train) [13][ 680/1879] lr: 2.0000e-02 eta: 16:55:48 time: 0.3225 data_time: 0.0133 memory: 6717 grad_norm: 2.9070 loss: 2.0378 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0378 2023/04/13 22:32:29 - mmengine - INFO - Epoch(train) [13][ 700/1879] lr: 2.0000e-02 eta: 16:55:44 time: 0.3908 data_time: 0.0163 memory: 6717 grad_norm: 2.9906 loss: 1.9451 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9451 2023/04/13 22:32:36 - mmengine - INFO - Epoch(train) [13][ 720/1879] lr: 2.0000e-02 eta: 16:55:34 time: 0.3570 data_time: 0.0122 memory: 6717 grad_norm: 2.9500 loss: 1.7290 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7290 2023/04/13 22:32:43 - mmengine - INFO - Epoch(train) [13][ 740/1879] lr: 2.0000e-02 eta: 16:55:26 time: 0.3630 data_time: 0.0150 memory: 6717 grad_norm: 2.9495 loss: 2.0724 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0724 2023/04/13 22:32:50 - mmengine - INFO - Epoch(train) [13][ 760/1879] lr: 2.0000e-02 eta: 16:55:19 time: 0.3698 data_time: 0.0124 memory: 6717 grad_norm: 2.8759 loss: 2.0865 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.0865 2023/04/13 22:32:59 - mmengine - INFO - Epoch(train) [13][ 780/1879] lr: 2.0000e-02 eta: 16:55:22 time: 0.4491 data_time: 0.0133 memory: 6717 grad_norm: 2.9115 loss: 2.0782 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 2.0782 2023/04/13 22:33:05 - mmengine - INFO - Epoch(train) [13][ 800/1879] lr: 2.0000e-02 eta: 16:55:04 time: 0.2945 data_time: 0.0134 memory: 6717 grad_norm: 2.9601 loss: 1.8213 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8213 2023/04/13 22:33:14 - mmengine - INFO - Epoch(train) [13][ 820/1879] lr: 2.0000e-02 eta: 16:55:05 time: 0.4275 data_time: 0.0142 memory: 6717 grad_norm: 2.9418 loss: 2.1515 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1515 2023/04/13 22:33:20 - mmengine - INFO - Epoch(train) [13][ 840/1879] lr: 2.0000e-02 eta: 16:54:49 time: 0.3111 data_time: 0.0139 memory: 6717 grad_norm: 2.9402 loss: 2.0920 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.0920 2023/04/13 22:33:28 - mmengine - INFO - Epoch(train) [13][ 860/1879] lr: 2.0000e-02 eta: 16:54:43 time: 0.3762 data_time: 0.0144 memory: 6717 grad_norm: 2.9403 loss: 1.9984 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9984 2023/04/13 22:33:34 - mmengine - INFO - Epoch(train) [13][ 880/1879] lr: 2.0000e-02 eta: 16:54:28 time: 0.3200 data_time: 0.0136 memory: 6717 grad_norm: 2.9377 loss: 1.8611 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8611 2023/04/13 22:33:42 - mmengine - INFO - Epoch(train) [13][ 900/1879] lr: 2.0000e-02 eta: 16:54:26 time: 0.4056 data_time: 0.0145 memory: 6717 grad_norm: 2.9895 loss: 1.9631 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9631 2023/04/13 22:33:48 - mmengine - INFO - Epoch(train) [13][ 920/1879] lr: 2.0000e-02 eta: 16:54:11 time: 0.3178 data_time: 0.0138 memory: 6717 grad_norm: 2.9085 loss: 1.9720 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9720 2023/04/13 22:33:57 - mmengine - INFO - Epoch(train) [13][ 940/1879] lr: 2.0000e-02 eta: 16:54:14 time: 0.4411 data_time: 0.0161 memory: 6717 grad_norm: 2.9098 loss: 1.8360 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8360 2023/04/13 22:34:04 - mmengine - INFO - Epoch(train) [13][ 960/1879] lr: 2.0000e-02 eta: 16:53:59 time: 0.3212 data_time: 0.0131 memory: 6717 grad_norm: 2.9311 loss: 2.1164 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 2.1164 2023/04/13 22:34:11 - mmengine - INFO - Epoch(train) [13][ 980/1879] lr: 2.0000e-02 eta: 16:53:54 time: 0.3820 data_time: 0.0167 memory: 6717 grad_norm: 2.9240 loss: 1.8340 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8340 2023/04/13 22:34:18 - mmengine - INFO - Epoch(train) [13][1000/1879] lr: 2.0000e-02 eta: 16:53:43 time: 0.3448 data_time: 0.0123 memory: 6717 grad_norm: 2.9059 loss: 1.9468 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9468 2023/04/13 22:34:26 - mmengine - INFO - Epoch(train) [13][1020/1879] lr: 2.0000e-02 eta: 16:53:36 time: 0.3733 data_time: 0.0157 memory: 6717 grad_norm: 2.8787 loss: 1.9128 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9128 2023/04/13 22:34:34 - mmengine - INFO - Epoch(train) [13][1040/1879] lr: 2.0000e-02 eta: 16:53:35 time: 0.4209 data_time: 0.0126 memory: 6717 grad_norm: 2.8408 loss: 1.9555 top1_acc: 0.1250 top5_acc: 0.5625 loss_cls: 1.9555 2023/04/13 22:34:41 - mmengine - INFO - Epoch(train) [13][1060/1879] lr: 2.0000e-02 eta: 16:53:22 time: 0.3266 data_time: 0.0346 memory: 6717 grad_norm: 2.9702 loss: 2.0965 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0965 2023/04/13 22:34:49 - mmengine - INFO - Epoch(train) [13][1080/1879] lr: 2.0000e-02 eta: 16:53:20 time: 0.4094 data_time: 0.0119 memory: 6717 grad_norm: 2.8759 loss: 2.0475 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0475 2023/04/13 22:34:55 - mmengine - INFO - Epoch(train) [13][1100/1879] lr: 2.0000e-02 eta: 16:53:06 time: 0.3227 data_time: 0.0151 memory: 6717 grad_norm: 2.9373 loss: 2.0713 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 2.0713 2023/04/13 22:35:03 - mmengine - INFO - Epoch(train) [13][1120/1879] lr: 2.0000e-02 eta: 16:53:03 time: 0.4006 data_time: 0.0122 memory: 6717 grad_norm: 2.8275 loss: 1.8473 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8473 2023/04/13 22:35:10 - mmengine - INFO - Epoch(train) [13][1140/1879] lr: 2.0000e-02 eta: 16:52:51 time: 0.3392 data_time: 0.0135 memory: 6717 grad_norm: 2.8979 loss: 1.9589 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9589 2023/04/13 22:35:18 - mmengine - INFO - Epoch(train) [13][1160/1879] lr: 2.0000e-02 eta: 16:52:48 time: 0.3984 data_time: 0.0133 memory: 6717 grad_norm: 2.9192 loss: 2.0847 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0847 2023/04/13 22:35:25 - mmengine - INFO - Epoch(train) [13][1180/1879] lr: 2.0000e-02 eta: 16:52:38 time: 0.3537 data_time: 0.0144 memory: 6717 grad_norm: 2.8388 loss: 1.9629 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9629 2023/04/13 22:35:32 - mmengine - INFO - Epoch(train) [13][1200/1879] lr: 2.0000e-02 eta: 16:52:29 time: 0.3566 data_time: 0.0667 memory: 6717 grad_norm: 2.9598 loss: 2.1822 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 2.1822 2023/04/13 22:35:40 - mmengine - INFO - Epoch(train) [13][1220/1879] lr: 2.0000e-02 eta: 16:52:24 time: 0.3889 data_time: 0.0415 memory: 6717 grad_norm: 2.9289 loss: 1.9154 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9154 2023/04/13 22:35:48 - mmengine - INFO - Epoch(train) [13][1240/1879] lr: 2.0000e-02 eta: 16:52:17 time: 0.3759 data_time: 0.0182 memory: 6717 grad_norm: 2.9016 loss: 1.9124 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9124 2023/04/13 22:35:55 - mmengine - INFO - Epoch(train) [13][1260/1879] lr: 2.0000e-02 eta: 16:52:07 time: 0.3520 data_time: 0.0334 memory: 6717 grad_norm: 2.9489 loss: 2.0771 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0771 2023/04/13 22:36:01 - mmengine - INFO - Epoch(train) [13][1280/1879] lr: 2.0000e-02 eta: 16:51:56 time: 0.3425 data_time: 0.0298 memory: 6717 grad_norm: 2.9280 loss: 1.8645 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8645 2023/04/13 22:36:10 - mmengine - INFO - Epoch(train) [13][1300/1879] lr: 2.0000e-02 eta: 16:51:57 time: 0.4287 data_time: 0.0229 memory: 6717 grad_norm: 2.9001 loss: 2.0802 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0802 2023/04/13 22:36:16 - mmengine - INFO - Epoch(train) [13][1320/1879] lr: 2.0000e-02 eta: 16:51:43 time: 0.3247 data_time: 0.0129 memory: 6717 grad_norm: 2.9844 loss: 2.0755 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0755 2023/04/13 22:36:25 - mmengine - INFO - Epoch(train) [13][1340/1879] lr: 2.0000e-02 eta: 16:51:46 time: 0.4418 data_time: 0.0121 memory: 6717 grad_norm: 2.9434 loss: 2.0644 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0644 2023/04/13 22:36:32 - mmengine - INFO - Epoch(train) [13][1360/1879] lr: 2.0000e-02 eta: 16:51:34 time: 0.3365 data_time: 0.0380 memory: 6717 grad_norm: 3.0523 loss: 1.8348 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8348 2023/04/13 22:36:40 - mmengine - INFO - Epoch(train) [13][1380/1879] lr: 2.0000e-02 eta: 16:51:31 time: 0.4050 data_time: 0.0562 memory: 6717 grad_norm: 2.8689 loss: 1.9978 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 1.9978 2023/04/13 22:36:47 - mmengine - INFO - Epoch(train) [13][1400/1879] lr: 2.0000e-02 eta: 16:51:18 time: 0.3294 data_time: 0.0152 memory: 6717 grad_norm: 2.8887 loss: 2.0796 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0796 2023/04/13 22:36:55 - mmengine - INFO - Epoch(train) [13][1420/1879] lr: 2.0000e-02 eta: 16:51:14 time: 0.3911 data_time: 0.0117 memory: 6717 grad_norm: 2.8961 loss: 2.0223 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0223 2023/04/13 22:37:01 - mmengine - INFO - Epoch(train) [13][1440/1879] lr: 2.0000e-02 eta: 16:51:01 time: 0.3290 data_time: 0.0381 memory: 6717 grad_norm: 2.9067 loss: 1.9611 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.9611 2023/04/13 22:37:06 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 22:37:09 - mmengine - INFO - Epoch(train) [13][1460/1879] lr: 2.0000e-02 eta: 16:50:53 time: 0.3713 data_time: 0.1045 memory: 6717 grad_norm: 2.9467 loss: 2.1037 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1037 2023/04/13 22:37:16 - mmengine - INFO - Epoch(train) [13][1480/1879] lr: 2.0000e-02 eta: 16:50:46 time: 0.3699 data_time: 0.0704 memory: 6717 grad_norm: 2.9084 loss: 1.9108 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 1.9108 2023/04/13 22:37:24 - mmengine - INFO - Epoch(train) [13][1500/1879] lr: 2.0000e-02 eta: 16:50:43 time: 0.4053 data_time: 0.0484 memory: 6717 grad_norm: 2.9799 loss: 2.0539 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0539 2023/04/13 22:37:31 - mmengine - INFO - Epoch(train) [13][1520/1879] lr: 2.0000e-02 eta: 16:50:31 time: 0.3331 data_time: 0.0224 memory: 6717 grad_norm: 2.9472 loss: 2.1373 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.1373 2023/04/13 22:37:39 - mmengine - INFO - Epoch(train) [13][1540/1879] lr: 2.0000e-02 eta: 16:50:31 time: 0.4273 data_time: 0.0122 memory: 6717 grad_norm: 2.9264 loss: 2.0609 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.0609 2023/04/13 22:37:46 - mmengine - INFO - Epoch(train) [13][1560/1879] lr: 2.0000e-02 eta: 16:50:21 time: 0.3507 data_time: 0.0139 memory: 6717 grad_norm: 2.8574 loss: 1.9689 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9689 2023/04/13 22:37:55 - mmengine - INFO - Epoch(train) [13][1580/1879] lr: 2.0000e-02 eta: 16:50:25 time: 0.4541 data_time: 0.0138 memory: 6717 grad_norm: 3.0146 loss: 1.9410 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9410 2023/04/13 22:38:02 - mmengine - INFO - Epoch(train) [13][1600/1879] lr: 2.0000e-02 eta: 16:50:11 time: 0.3181 data_time: 0.0124 memory: 6717 grad_norm: 2.9809 loss: 2.1408 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.1408 2023/04/13 22:38:10 - mmengine - INFO - Epoch(train) [13][1620/1879] lr: 2.0000e-02 eta: 16:50:09 time: 0.4099 data_time: 0.0144 memory: 6717 grad_norm: 2.8755 loss: 1.9092 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.9092 2023/04/13 22:38:18 - mmengine - INFO - Epoch(train) [13][1640/1879] lr: 2.0000e-02 eta: 16:50:08 time: 0.4200 data_time: 0.0132 memory: 6717 grad_norm: 2.9397 loss: 1.9771 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9771 2023/04/13 22:38:25 - mmengine - INFO - Epoch(train) [13][1660/1879] lr: 2.0000e-02 eta: 16:49:54 time: 0.3243 data_time: 0.0147 memory: 6717 grad_norm: 2.8633 loss: 1.8857 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8857 2023/04/13 22:38:33 - mmengine - INFO - Epoch(train) [13][1680/1879] lr: 2.0000e-02 eta: 16:49:55 time: 0.4294 data_time: 0.0146 memory: 6717 grad_norm: 2.8861 loss: 2.1918 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.1918 2023/04/13 22:38:40 - mmengine - INFO - Epoch(train) [13][1700/1879] lr: 2.0000e-02 eta: 16:49:40 time: 0.3125 data_time: 0.0136 memory: 6717 grad_norm: 2.9757 loss: 1.9523 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9523 2023/04/13 22:38:49 - mmengine - INFO - Epoch(train) [13][1720/1879] lr: 2.0000e-02 eta: 16:49:47 time: 0.4782 data_time: 0.0140 memory: 6717 grad_norm: 2.8940 loss: 2.2646 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.2646 2023/04/13 22:38:55 - mmengine - INFO - Epoch(train) [13][1740/1879] lr: 2.0000e-02 eta: 16:49:30 time: 0.3026 data_time: 0.0122 memory: 6717 grad_norm: 2.9030 loss: 1.9546 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9546 2023/04/13 22:39:04 - mmengine - INFO - Epoch(train) [13][1760/1879] lr: 2.0000e-02 eta: 16:49:32 time: 0.4404 data_time: 0.0130 memory: 6717 grad_norm: 2.9022 loss: 2.0700 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.0700 2023/04/13 22:39:11 - mmengine - INFO - Epoch(train) [13][1780/1879] lr: 2.0000e-02 eta: 16:49:20 time: 0.3310 data_time: 0.0133 memory: 6717 grad_norm: 2.8778 loss: 2.1253 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.1253 2023/04/13 22:39:18 - mmengine - INFO - Epoch(train) [13][1800/1879] lr: 2.0000e-02 eta: 16:49:11 time: 0.3563 data_time: 0.0136 memory: 6717 grad_norm: 2.8708 loss: 2.0858 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0858 2023/04/13 22:39:24 - mmengine - INFO - Epoch(train) [13][1820/1879] lr: 2.0000e-02 eta: 16:48:57 time: 0.3224 data_time: 0.0152 memory: 6717 grad_norm: 2.9148 loss: 2.1564 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.1564 2023/04/13 22:39:33 - mmengine - INFO - Epoch(train) [13][1840/1879] lr: 2.0000e-02 eta: 16:48:58 time: 0.4340 data_time: 0.0129 memory: 6717 grad_norm: 2.9205 loss: 2.0980 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0980 2023/04/13 22:39:40 - mmengine - INFO - Epoch(train) [13][1860/1879] lr: 2.0000e-02 eta: 16:48:47 time: 0.3472 data_time: 0.0154 memory: 6717 grad_norm: 2.8607 loss: 1.9697 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9697 2023/04/13 22:39:46 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 22:39:46 - mmengine - INFO - Epoch(train) [13][1879/1879] lr: 2.0000e-02 eta: 16:48:33 time: 0.3098 data_time: 0.0110 memory: 6717 grad_norm: 2.9461 loss: 1.9939 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.9939 2023/04/13 22:39:55 - mmengine - INFO - Epoch(val) [13][ 20/155] eta: 0:00:58 time: 0.4337 data_time: 0.4005 memory: 1391 2023/04/13 22:40:01 - mmengine - INFO - Epoch(val) [13][ 40/155] eta: 0:00:44 time: 0.3373 data_time: 0.3037 memory: 1391 2023/04/13 22:40:09 - mmengine - INFO - Epoch(val) [13][ 60/155] eta: 0:00:36 time: 0.3750 data_time: 0.3407 memory: 1391 2023/04/13 22:40:16 - mmengine - INFO - Epoch(val) [13][ 80/155] eta: 0:00:28 time: 0.3686 data_time: 0.3351 memory: 1391 2023/04/13 22:40:25 - mmengine - INFO - Epoch(val) [13][100/155] eta: 0:00:21 time: 0.4262 data_time: 0.3929 memory: 1391 2023/04/13 22:40:31 - mmengine - INFO - Epoch(val) [13][120/155] eta: 0:00:13 time: 0.3182 data_time: 0.2845 memory: 1391 2023/04/13 22:40:38 - mmengine - INFO - Epoch(val) [13][140/155] eta: 0:00:05 time: 0.3699 data_time: 0.3363 memory: 1391 2023/04/13 22:40:47 - mmengine - INFO - Epoch(val) [13][155/155] acc/top1: 0.5556 acc/top5: 0.8052 acc/mean1: 0.5555 data_time: 0.3293 time: 0.3623 2023/04/13 22:40:47 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_11.pth is removed 2023/04/13 22:40:48 - mmengine - INFO - The best checkpoint with 0.5556 acc/top1 at 13 epoch is saved to best_acc_top1_epoch_13.pth. 2023/04/13 22:40:57 - mmengine - INFO - Epoch(train) [14][ 20/1879] lr: 2.0000e-02 eta: 16:48:39 time: 0.4691 data_time: 0.3001 memory: 6717 grad_norm: 2.9142 loss: 2.0002 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0002 2023/04/13 22:41:04 - mmengine - INFO - Epoch(train) [14][ 40/1879] lr: 2.0000e-02 eta: 16:48:28 time: 0.3450 data_time: 0.1753 memory: 6717 grad_norm: 2.8815 loss: 2.2057 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.2057 2023/04/13 22:41:12 - mmengine - INFO - Epoch(train) [14][ 60/1879] lr: 2.0000e-02 eta: 16:48:24 time: 0.3976 data_time: 0.1966 memory: 6717 grad_norm: 2.8808 loss: 1.9988 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9988 2023/04/13 22:41:19 - mmengine - INFO - Epoch(train) [14][ 80/1879] lr: 2.0000e-02 eta: 16:48:15 time: 0.3609 data_time: 0.1931 memory: 6717 grad_norm: 2.9833 loss: 1.9155 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.9155 2023/04/13 22:41:27 - mmengine - INFO - Epoch(train) [14][ 100/1879] lr: 2.0000e-02 eta: 16:48:08 time: 0.3736 data_time: 0.0799 memory: 6717 grad_norm: 2.8897 loss: 1.7350 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7350 2023/04/13 22:41:33 - mmengine - INFO - Epoch(train) [14][ 120/1879] lr: 2.0000e-02 eta: 16:47:57 time: 0.3366 data_time: 0.0326 memory: 6717 grad_norm: 2.8773 loss: 1.6261 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6261 2023/04/13 22:41:42 - mmengine - INFO - Epoch(train) [14][ 140/1879] lr: 2.0000e-02 eta: 16:47:55 time: 0.4157 data_time: 0.0189 memory: 6717 grad_norm: 2.9208 loss: 1.9409 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.9409 2023/04/13 22:41:48 - mmengine - INFO - Epoch(train) [14][ 160/1879] lr: 2.0000e-02 eta: 16:47:41 time: 0.3159 data_time: 0.0140 memory: 6717 grad_norm: 2.9160 loss: 2.0279 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0279 2023/04/13 22:41:56 - mmengine - INFO - Epoch(train) [14][ 180/1879] lr: 2.0000e-02 eta: 16:47:40 time: 0.4196 data_time: 0.0154 memory: 6717 grad_norm: 2.8157 loss: 1.7680 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.7680 2023/04/13 22:42:03 - mmengine - INFO - Epoch(train) [14][ 200/1879] lr: 2.0000e-02 eta: 16:47:25 time: 0.3166 data_time: 0.0127 memory: 6717 grad_norm: 2.9962 loss: 1.9265 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.9265 2023/04/13 22:42:12 - mmengine - INFO - Epoch(train) [14][ 220/1879] lr: 2.0000e-02 eta: 16:47:29 time: 0.4553 data_time: 0.0142 memory: 6717 grad_norm: 2.8538 loss: 1.9786 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9786 2023/04/13 22:42:18 - mmengine - INFO - Epoch(train) [14][ 240/1879] lr: 2.0000e-02 eta: 16:47:15 time: 0.3193 data_time: 0.0119 memory: 6717 grad_norm: 2.9364 loss: 2.0670 top1_acc: 0.4375 top5_acc: 0.5000 loss_cls: 2.0670 2023/04/13 22:42:26 - mmengine - INFO - Epoch(train) [14][ 260/1879] lr: 2.0000e-02 eta: 16:47:12 time: 0.4061 data_time: 0.0135 memory: 6717 grad_norm: 2.9390 loss: 1.9543 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9543 2023/04/13 22:42:33 - mmengine - INFO - Epoch(train) [14][ 280/1879] lr: 2.0000e-02 eta: 16:46:59 time: 0.3254 data_time: 0.0135 memory: 6717 grad_norm: 3.0023 loss: 1.8666 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8666 2023/04/13 22:42:41 - mmengine - INFO - Epoch(train) [14][ 300/1879] lr: 2.0000e-02 eta: 16:47:00 time: 0.4314 data_time: 0.0138 memory: 6717 grad_norm: 2.8354 loss: 1.8702 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8702 2023/04/13 22:42:47 - mmengine - INFO - Epoch(train) [14][ 320/1879] lr: 2.0000e-02 eta: 16:46:42 time: 0.2963 data_time: 0.0136 memory: 6717 grad_norm: 2.9144 loss: 2.0879 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0879 2023/04/13 22:42:56 - mmengine - INFO - Epoch(train) [14][ 340/1879] lr: 2.0000e-02 eta: 16:46:41 time: 0.4155 data_time: 0.0145 memory: 6717 grad_norm: 2.9223 loss: 1.8938 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8938 2023/04/13 22:43:02 - mmengine - INFO - Epoch(train) [14][ 360/1879] lr: 2.0000e-02 eta: 16:46:26 time: 0.3149 data_time: 0.0128 memory: 6717 grad_norm: 2.9583 loss: 1.8579 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8579 2023/04/13 22:43:11 - mmengine - INFO - Epoch(train) [14][ 380/1879] lr: 2.0000e-02 eta: 16:46:27 time: 0.4333 data_time: 0.0150 memory: 6717 grad_norm: 2.9188 loss: 1.8117 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8117 2023/04/13 22:43:17 - mmengine - INFO - Epoch(train) [14][ 400/1879] lr: 2.0000e-02 eta: 16:46:14 time: 0.3236 data_time: 0.0148 memory: 6717 grad_norm: 2.9416 loss: 1.9790 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9790 2023/04/13 22:43:25 - mmengine - INFO - Epoch(train) [14][ 420/1879] lr: 2.0000e-02 eta: 16:46:10 time: 0.3957 data_time: 0.0137 memory: 6717 grad_norm: 2.9431 loss: 1.8848 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8848 2023/04/13 22:43:31 - mmengine - INFO - Epoch(train) [14][ 440/1879] lr: 2.0000e-02 eta: 16:45:56 time: 0.3215 data_time: 0.0132 memory: 6717 grad_norm: 2.9993 loss: 1.9125 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9125 2023/04/13 22:43:40 - mmengine - INFO - Epoch(train) [14][ 460/1879] lr: 2.0000e-02 eta: 16:45:57 time: 0.4366 data_time: 0.0146 memory: 6717 grad_norm: 2.8757 loss: 2.1299 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1299 2023/04/13 22:43:47 - mmengine - INFO - Epoch(train) [14][ 480/1879] lr: 2.0000e-02 eta: 16:45:47 time: 0.3531 data_time: 0.0127 memory: 6717 grad_norm: 2.9422 loss: 1.8847 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.8847 2023/04/13 22:43:56 - mmengine - INFO - Epoch(train) [14][ 500/1879] lr: 2.0000e-02 eta: 16:45:46 time: 0.4132 data_time: 0.0138 memory: 6717 grad_norm: 2.8803 loss: 2.0586 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0586 2023/04/13 22:44:02 - mmengine - INFO - Epoch(train) [14][ 520/1879] lr: 2.0000e-02 eta: 16:45:29 time: 0.2978 data_time: 0.0127 memory: 6717 grad_norm: 3.0041 loss: 2.1537 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.1537 2023/04/13 22:44:10 - mmengine - INFO - Epoch(train) [14][ 540/1879] lr: 2.0000e-02 eta: 16:45:29 time: 0.4251 data_time: 0.0150 memory: 6717 grad_norm: 3.1066 loss: 2.0116 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.0116 2023/04/13 22:44:17 - mmengine - INFO - Epoch(train) [14][ 560/1879] lr: 2.0000e-02 eta: 16:45:16 time: 0.3330 data_time: 0.0124 memory: 6717 grad_norm: 2.9657 loss: 2.0160 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0160 2023/04/13 22:44:22 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 22:44:25 - mmengine - INFO - Epoch(train) [14][ 580/1879] lr: 2.0000e-02 eta: 16:45:16 time: 0.4216 data_time: 0.0133 memory: 6717 grad_norm: 2.8612 loss: 1.9296 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9296 2023/04/13 22:44:32 - mmengine - INFO - Epoch(train) [14][ 600/1879] lr: 2.0000e-02 eta: 16:45:02 time: 0.3219 data_time: 0.0134 memory: 6717 grad_norm: 2.9779 loss: 1.9256 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.9256 2023/04/13 22:44:39 - mmengine - INFO - Epoch(train) [14][ 620/1879] lr: 2.0000e-02 eta: 16:44:57 time: 0.3935 data_time: 0.0129 memory: 6717 grad_norm: 2.8810 loss: 2.0629 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0629 2023/04/13 22:44:47 - mmengine - INFO - Epoch(train) [14][ 640/1879] lr: 2.0000e-02 eta: 16:44:50 time: 0.3661 data_time: 0.0139 memory: 6717 grad_norm: 2.9969 loss: 1.9428 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.9428 2023/04/13 22:44:54 - mmengine - INFO - Epoch(train) [14][ 660/1879] lr: 2.0000e-02 eta: 16:44:41 time: 0.3580 data_time: 0.0167 memory: 6717 grad_norm: 2.9473 loss: 1.9434 top1_acc: 0.2500 top5_acc: 0.8125 loss_cls: 1.9434 2023/04/13 22:45:02 - mmengine - INFO - Epoch(train) [14][ 680/1879] lr: 2.0000e-02 eta: 16:44:35 time: 0.3873 data_time: 0.0137 memory: 6717 grad_norm: 2.7913 loss: 1.8761 top1_acc: 0.3125 top5_acc: 0.5000 loss_cls: 1.8761 2023/04/13 22:45:09 - mmengine - INFO - Epoch(train) [14][ 700/1879] lr: 2.0000e-02 eta: 16:44:27 time: 0.3592 data_time: 0.0137 memory: 6717 grad_norm: 2.8868 loss: 2.1195 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.1195 2023/04/13 22:45:17 - mmengine - INFO - Epoch(train) [14][ 720/1879] lr: 2.0000e-02 eta: 16:44:27 time: 0.4302 data_time: 0.0133 memory: 6717 grad_norm: 2.9730 loss: 1.7833 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7833 2023/04/13 22:45:24 - mmengine - INFO - Epoch(train) [14][ 740/1879] lr: 2.0000e-02 eta: 16:44:12 time: 0.3117 data_time: 0.0140 memory: 6717 grad_norm: 2.9177 loss: 1.7923 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7923 2023/04/13 22:45:31 - mmengine - INFO - Epoch(train) [14][ 760/1879] lr: 2.0000e-02 eta: 16:44:05 time: 0.3734 data_time: 0.0148 memory: 6717 grad_norm: 2.8814 loss: 1.7890 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7890 2023/04/13 22:45:38 - mmengine - INFO - Epoch(train) [14][ 780/1879] lr: 2.0000e-02 eta: 16:43:54 time: 0.3406 data_time: 0.0151 memory: 6717 grad_norm: 2.8363 loss: 1.7473 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7473 2023/04/13 22:45:47 - mmengine - INFO - Epoch(train) [14][ 800/1879] lr: 2.0000e-02 eta: 16:43:56 time: 0.4434 data_time: 0.0129 memory: 6717 grad_norm: 2.9374 loss: 1.9007 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9007 2023/04/13 22:45:53 - mmengine - INFO - Epoch(train) [14][ 820/1879] lr: 2.0000e-02 eta: 16:43:42 time: 0.3209 data_time: 0.0144 memory: 6717 grad_norm: 2.9760 loss: 1.9976 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9976 2023/04/13 22:46:02 - mmengine - INFO - Epoch(train) [14][ 840/1879] lr: 2.0000e-02 eta: 16:43:41 time: 0.4194 data_time: 0.0131 memory: 6717 grad_norm: 2.8592 loss: 1.9174 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.9174 2023/04/13 22:46:07 - mmengine - INFO - Epoch(train) [14][ 860/1879] lr: 2.0000e-02 eta: 16:43:23 time: 0.2894 data_time: 0.0158 memory: 6717 grad_norm: 2.9586 loss: 1.6756 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6756 2023/04/13 22:46:16 - mmengine - INFO - Epoch(train) [14][ 880/1879] lr: 2.0000e-02 eta: 16:43:21 time: 0.4108 data_time: 0.0134 memory: 6717 grad_norm: 2.8886 loss: 1.9856 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9856 2023/04/13 22:46:22 - mmengine - INFO - Epoch(train) [14][ 900/1879] lr: 2.0000e-02 eta: 16:43:08 time: 0.3244 data_time: 0.0147 memory: 6717 grad_norm: 2.8740 loss: 2.0020 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0020 2023/04/13 22:46:30 - mmengine - INFO - Epoch(train) [14][ 920/1879] lr: 2.0000e-02 eta: 16:43:03 time: 0.3910 data_time: 0.0129 memory: 6717 grad_norm: 2.9371 loss: 1.9207 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.9207 2023/04/13 22:46:37 - mmengine - INFO - Epoch(train) [14][ 940/1879] lr: 2.0000e-02 eta: 16:42:53 time: 0.3485 data_time: 0.0144 memory: 6717 grad_norm: 2.8715 loss: 1.9554 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9554 2023/04/13 22:46:44 - mmengine - INFO - Epoch(train) [14][ 960/1879] lr: 2.0000e-02 eta: 16:42:43 time: 0.3565 data_time: 0.0146 memory: 6717 grad_norm: 2.8628 loss: 2.0711 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0711 2023/04/13 22:46:51 - mmengine - INFO - Epoch(train) [14][ 980/1879] lr: 2.0000e-02 eta: 16:42:35 time: 0.3644 data_time: 0.0136 memory: 6717 grad_norm: 2.9327 loss: 2.0359 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0359 2023/04/13 22:46:59 - mmengine - INFO - Epoch(train) [14][1000/1879] lr: 2.0000e-02 eta: 16:42:28 time: 0.3675 data_time: 0.0339 memory: 6717 grad_norm: 2.8878 loss: 2.0358 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0358 2023/04/13 22:47:06 - mmengine - INFO - Epoch(train) [14][1020/1879] lr: 2.0000e-02 eta: 16:42:19 time: 0.3609 data_time: 0.0249 memory: 6717 grad_norm: 2.9344 loss: 2.0266 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0266 2023/04/13 22:47:14 - mmengine - INFO - Epoch(train) [14][1040/1879] lr: 2.0000e-02 eta: 16:42:13 time: 0.3853 data_time: 0.0406 memory: 6717 grad_norm: 2.9192 loss: 1.8964 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8964 2023/04/13 22:47:20 - mmengine - INFO - Epoch(train) [14][1060/1879] lr: 2.0000e-02 eta: 16:42:02 time: 0.3414 data_time: 0.0776 memory: 6717 grad_norm: 2.8146 loss: 1.7996 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7996 2023/04/13 22:47:28 - mmengine - INFO - Epoch(train) [14][1080/1879] lr: 2.0000e-02 eta: 16:41:56 time: 0.3773 data_time: 0.0345 memory: 6717 grad_norm: 2.9154 loss: 2.1899 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1899 2023/04/13 22:47:36 - mmengine - INFO - Epoch(train) [14][1100/1879] lr: 2.0000e-02 eta: 16:41:50 time: 0.3846 data_time: 0.0330 memory: 6717 grad_norm: 2.9434 loss: 2.1322 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 2.1322 2023/04/13 22:47:43 - mmengine - INFO - Epoch(train) [14][1120/1879] lr: 2.0000e-02 eta: 16:41:44 time: 0.3823 data_time: 0.1753 memory: 6717 grad_norm: 2.8969 loss: 2.0846 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0846 2023/04/13 22:47:50 - mmengine - INFO - Epoch(train) [14][1140/1879] lr: 2.0000e-02 eta: 16:41:33 time: 0.3355 data_time: 0.0742 memory: 6717 grad_norm: 2.8454 loss: 1.8573 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.8573 2023/04/13 22:47:57 - mmengine - INFO - Epoch(train) [14][1160/1879] lr: 2.0000e-02 eta: 16:41:21 time: 0.3357 data_time: 0.0918 memory: 6717 grad_norm: 3.0380 loss: 2.0285 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.0285 2023/04/13 22:48:05 - mmengine - INFO - Epoch(train) [14][1180/1879] lr: 2.0000e-02 eta: 16:41:20 time: 0.4252 data_time: 0.0355 memory: 6717 grad_norm: 2.7756 loss: 1.8417 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8417 2023/04/13 22:48:12 - mmengine - INFO - Epoch(train) [14][1200/1879] lr: 2.0000e-02 eta: 16:41:07 time: 0.3250 data_time: 0.0278 memory: 6717 grad_norm: 3.0331 loss: 1.9542 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.9542 2023/04/13 22:48:20 - mmengine - INFO - Epoch(train) [14][1220/1879] lr: 2.0000e-02 eta: 16:41:07 time: 0.4249 data_time: 0.0131 memory: 6717 grad_norm: 2.8561 loss: 2.0385 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0385 2023/04/13 22:48:27 - mmengine - INFO - Epoch(train) [14][1240/1879] lr: 2.0000e-02 eta: 16:40:56 time: 0.3412 data_time: 0.0140 memory: 6717 grad_norm: 2.9313 loss: 1.9082 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9082 2023/04/13 22:48:35 - mmengine - INFO - Epoch(train) [14][1260/1879] lr: 2.0000e-02 eta: 16:40:52 time: 0.3972 data_time: 0.0135 memory: 6717 grad_norm: 2.9372 loss: 1.8435 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8435 2023/04/13 22:48:42 - mmengine - INFO - Epoch(train) [14][1280/1879] lr: 2.0000e-02 eta: 16:40:41 time: 0.3418 data_time: 0.0158 memory: 6717 grad_norm: 2.7920 loss: 1.8043 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8043 2023/04/13 22:48:50 - mmengine - INFO - Epoch(train) [14][1300/1879] lr: 2.0000e-02 eta: 16:40:38 time: 0.4069 data_time: 0.0133 memory: 6717 grad_norm: 2.9105 loss: 2.0619 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0619 2023/04/13 22:48:57 - mmengine - INFO - Epoch(train) [14][1320/1879] lr: 2.0000e-02 eta: 16:40:26 time: 0.3303 data_time: 0.0151 memory: 6717 grad_norm: 2.9560 loss: 1.8739 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.8739 2023/04/13 22:49:04 - mmengine - INFO - Epoch(train) [14][1340/1879] lr: 2.0000e-02 eta: 16:40:17 time: 0.3617 data_time: 0.0128 memory: 6717 grad_norm: 2.8648 loss: 1.9964 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9964 2023/04/13 22:49:10 - mmengine - INFO - Epoch(train) [14][1360/1879] lr: 2.0000e-02 eta: 16:40:04 time: 0.3236 data_time: 0.0159 memory: 6717 grad_norm: 2.8158 loss: 2.3161 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.3161 2023/04/13 22:49:19 - mmengine - INFO - Epoch(train) [14][1380/1879] lr: 2.0000e-02 eta: 16:40:03 time: 0.4253 data_time: 0.0140 memory: 6717 grad_norm: 2.9180 loss: 2.0928 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 2.0928 2023/04/13 22:49:25 - mmengine - INFO - Epoch(train) [14][1400/1879] lr: 2.0000e-02 eta: 16:39:50 time: 0.3188 data_time: 0.0149 memory: 6717 grad_norm: 2.8431 loss: 1.9977 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9977 2023/04/13 22:49:33 - mmengine - INFO - Epoch(train) [14][1420/1879] lr: 2.0000e-02 eta: 16:39:47 time: 0.4113 data_time: 0.0148 memory: 6717 grad_norm: 2.9055 loss: 1.8880 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8880 2023/04/13 22:49:41 - mmengine - INFO - Epoch(train) [14][1440/1879] lr: 2.0000e-02 eta: 16:39:39 time: 0.3605 data_time: 0.0146 memory: 6717 grad_norm: 2.9152 loss: 1.8496 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.8496 2023/04/13 22:49:49 - mmengine - INFO - Epoch(train) [14][1460/1879] lr: 2.0000e-02 eta: 16:39:36 time: 0.4110 data_time: 0.0132 memory: 6717 grad_norm: 2.8050 loss: 1.8853 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8853 2023/04/13 22:49:56 - mmengine - INFO - Epoch(train) [14][1480/1879] lr: 2.0000e-02 eta: 16:39:27 time: 0.3521 data_time: 0.0144 memory: 6717 grad_norm: 2.8385 loss: 1.9139 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9139 2023/04/13 22:50:04 - mmengine - INFO - Epoch(train) [14][1500/1879] lr: 2.0000e-02 eta: 16:39:21 time: 0.3861 data_time: 0.0133 memory: 6717 grad_norm: 2.9338 loss: 2.0355 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0355 2023/04/13 22:50:10 - mmengine - INFO - Epoch(train) [14][1520/1879] lr: 2.0000e-02 eta: 16:39:06 time: 0.3107 data_time: 0.0146 memory: 6717 grad_norm: 2.8712 loss: 1.7765 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7765 2023/04/13 22:50:18 - mmengine - INFO - Epoch(train) [14][1540/1879] lr: 2.0000e-02 eta: 16:39:03 time: 0.4056 data_time: 0.0128 memory: 6717 grad_norm: 2.9696 loss: 2.2192 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.2192 2023/04/13 22:50:24 - mmengine - INFO - Epoch(train) [14][1560/1879] lr: 2.0000e-02 eta: 16:38:50 time: 0.3210 data_time: 0.0143 memory: 6717 grad_norm: 2.8447 loss: 1.7705 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7705 2023/04/13 22:50:29 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 22:50:32 - mmengine - INFO - Epoch(train) [14][1580/1879] lr: 2.0000e-02 eta: 16:38:45 time: 0.3865 data_time: 0.0137 memory: 6717 grad_norm: 2.8484 loss: 1.9329 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9329 2023/04/13 22:50:39 - mmengine - INFO - Epoch(train) [14][1600/1879] lr: 2.0000e-02 eta: 16:38:37 time: 0.3655 data_time: 0.0439 memory: 6717 grad_norm: 2.8939 loss: 2.1394 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1394 2023/04/13 22:50:46 - mmengine - INFO - Epoch(train) [14][1620/1879] lr: 2.0000e-02 eta: 16:38:25 time: 0.3390 data_time: 0.0839 memory: 6717 grad_norm: 2.8937 loss: 1.8964 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.8964 2023/04/13 22:50:54 - mmengine - INFO - Epoch(train) [14][1640/1879] lr: 2.0000e-02 eta: 16:38:21 time: 0.3988 data_time: 0.1975 memory: 6717 grad_norm: 2.9000 loss: 2.0345 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.0345 2023/04/13 22:51:00 - mmengine - INFO - Epoch(train) [14][1660/1879] lr: 2.0000e-02 eta: 16:38:07 time: 0.3146 data_time: 0.1375 memory: 6717 grad_norm: 2.9272 loss: 1.9529 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.9529 2023/04/13 22:51:09 - mmengine - INFO - Epoch(train) [14][1680/1879] lr: 2.0000e-02 eta: 16:38:06 time: 0.4179 data_time: 0.1997 memory: 6717 grad_norm: 2.8233 loss: 2.0694 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 2.0694 2023/04/13 22:51:15 - mmengine - INFO - Epoch(train) [14][1700/1879] lr: 2.0000e-02 eta: 16:37:51 time: 0.3079 data_time: 0.1562 memory: 6717 grad_norm: 2.8711 loss: 1.8521 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8521 2023/04/13 22:51:23 - mmengine - INFO - Epoch(train) [14][1720/1879] lr: 2.0000e-02 eta: 16:37:49 time: 0.4132 data_time: 0.2123 memory: 6717 grad_norm: 2.8470 loss: 1.8999 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8999 2023/04/13 22:51:30 - mmengine - INFO - Epoch(train) [14][1740/1879] lr: 2.0000e-02 eta: 16:37:36 time: 0.3263 data_time: 0.1499 memory: 6717 grad_norm: 2.8849 loss: 1.9065 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9065 2023/04/13 22:51:38 - mmengine - INFO - Epoch(train) [14][1760/1879] lr: 2.0000e-02 eta: 16:37:35 time: 0.4225 data_time: 0.2287 memory: 6717 grad_norm: 2.8293 loss: 1.6463 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6463 2023/04/13 22:51:46 - mmengine - INFO - Epoch(train) [14][1780/1879] lr: 2.0000e-02 eta: 16:37:27 time: 0.3665 data_time: 0.1470 memory: 6717 grad_norm: 2.9069 loss: 2.1049 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1049 2023/04/13 22:51:53 - mmengine - INFO - Epoch(train) [14][1800/1879] lr: 2.0000e-02 eta: 16:37:17 time: 0.3496 data_time: 0.1209 memory: 6717 grad_norm: 2.9024 loss: 1.9487 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9487 2023/04/13 22:52:00 - mmengine - INFO - Epoch(train) [14][1820/1879] lr: 2.0000e-02 eta: 16:37:09 time: 0.3629 data_time: 0.0882 memory: 6717 grad_norm: 2.8263 loss: 1.9174 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.9174 2023/04/13 22:52:07 - mmengine - INFO - Epoch(train) [14][1840/1879] lr: 2.0000e-02 eta: 16:37:00 time: 0.3593 data_time: 0.1245 memory: 6717 grad_norm: 2.9501 loss: 1.9633 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9633 2023/04/13 22:52:14 - mmengine - INFO - Epoch(train) [14][1860/1879] lr: 2.0000e-02 eta: 16:36:49 time: 0.3444 data_time: 0.1011 memory: 6717 grad_norm: 2.9243 loss: 2.1006 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1006 2023/04/13 22:52:21 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 22:52:21 - mmengine - INFO - Epoch(train) [14][1879/1879] lr: 2.0000e-02 eta: 16:36:43 time: 0.3625 data_time: 0.2032 memory: 6717 grad_norm: 2.9373 loss: 1.8745 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.8745 2023/04/13 22:52:30 - mmengine - INFO - Epoch(val) [14][ 20/155] eta: 0:01:00 time: 0.4463 data_time: 0.4135 memory: 1391 2023/04/13 22:52:36 - mmengine - INFO - Epoch(val) [14][ 40/155] eta: 0:00:44 time: 0.3197 data_time: 0.2867 memory: 1391 2023/04/13 22:52:45 - mmengine - INFO - Epoch(val) [14][ 60/155] eta: 0:00:38 time: 0.4401 data_time: 0.4073 memory: 1391 2023/04/13 22:52:51 - mmengine - INFO - Epoch(val) [14][ 80/155] eta: 0:00:28 time: 0.3155 data_time: 0.2819 memory: 1391 2023/04/13 22:53:01 - mmengine - INFO - Epoch(val) [14][100/155] eta: 0:00:21 time: 0.4530 data_time: 0.4200 memory: 1391 2023/04/13 22:53:07 - mmengine - INFO - Epoch(val) [14][120/155] eta: 0:00:13 time: 0.3005 data_time: 0.2671 memory: 1391 2023/04/13 22:53:16 - mmengine - INFO - Epoch(val) [14][140/155] eta: 0:00:05 time: 0.4847 data_time: 0.4519 memory: 1391 2023/04/13 22:53:23 - mmengine - INFO - Epoch(val) [14][155/155] acc/top1: 0.5544 acc/top5: 0.8033 acc/mean1: 0.5543 data_time: 0.4170 time: 0.4494 2023/04/13 22:53:33 - mmengine - INFO - Epoch(train) [15][ 20/1879] lr: 2.0000e-02 eta: 16:36:49 time: 0.4791 data_time: 0.3172 memory: 6717 grad_norm: 2.9452 loss: 1.9484 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9484 2023/04/13 22:53:40 - mmengine - INFO - Epoch(train) [15][ 40/1879] lr: 2.0000e-02 eta: 16:36:37 time: 0.3323 data_time: 0.1234 memory: 6717 grad_norm: 2.9005 loss: 1.9348 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9348 2023/04/13 22:53:48 - mmengine - INFO - Epoch(train) [15][ 60/1879] lr: 2.0000e-02 eta: 16:36:35 time: 0.4159 data_time: 0.0489 memory: 6717 grad_norm: 2.9275 loss: 1.9446 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9446 2023/04/13 22:53:55 - mmengine - INFO - Epoch(train) [15][ 80/1879] lr: 2.0000e-02 eta: 16:36:23 time: 0.3344 data_time: 0.0442 memory: 6717 grad_norm: 2.9166 loss: 1.6130 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6130 2023/04/13 22:54:03 - mmengine - INFO - Epoch(train) [15][ 100/1879] lr: 2.0000e-02 eta: 16:36:19 time: 0.3996 data_time: 0.0152 memory: 6717 grad_norm: 2.8804 loss: 1.9465 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9465 2023/04/13 22:54:09 - mmengine - INFO - Epoch(train) [15][ 120/1879] lr: 2.0000e-02 eta: 16:36:08 time: 0.3348 data_time: 0.0128 memory: 6717 grad_norm: 2.8581 loss: 1.9616 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9616 2023/04/13 22:54:17 - mmengine - INFO - Epoch(train) [15][ 140/1879] lr: 2.0000e-02 eta: 16:36:04 time: 0.3974 data_time: 0.0152 memory: 6717 grad_norm: 3.0865 loss: 1.9846 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9846 2023/04/13 22:54:24 - mmengine - INFO - Epoch(train) [15][ 160/1879] lr: 2.0000e-02 eta: 16:35:53 time: 0.3425 data_time: 0.0133 memory: 6717 grad_norm: 2.8500 loss: 1.8475 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8475 2023/04/13 22:54:32 - mmengine - INFO - Epoch(train) [15][ 180/1879] lr: 2.0000e-02 eta: 16:35:46 time: 0.3751 data_time: 0.0147 memory: 6717 grad_norm: 2.8606 loss: 1.8956 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8956 2023/04/13 22:54:39 - mmengine - INFO - Epoch(train) [15][ 200/1879] lr: 2.0000e-02 eta: 16:35:41 time: 0.3876 data_time: 0.0134 memory: 6717 grad_norm: 2.8814 loss: 1.9156 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.9156 2023/04/13 22:54:47 - mmengine - INFO - Epoch(train) [15][ 220/1879] lr: 2.0000e-02 eta: 16:35:34 time: 0.3714 data_time: 0.0143 memory: 6717 grad_norm: 2.8678 loss: 1.8999 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.8999 2023/04/13 22:54:54 - mmengine - INFO - Epoch(train) [15][ 240/1879] lr: 2.0000e-02 eta: 16:35:26 time: 0.3701 data_time: 0.0130 memory: 6717 grad_norm: 2.9191 loss: 2.0121 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0121 2023/04/13 22:55:02 - mmengine - INFO - Epoch(train) [15][ 260/1879] lr: 2.0000e-02 eta: 16:35:23 time: 0.4041 data_time: 0.0143 memory: 6717 grad_norm: 2.9054 loss: 1.8401 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8401 2023/04/13 22:55:08 - mmengine - INFO - Epoch(train) [15][ 280/1879] lr: 2.0000e-02 eta: 16:35:08 time: 0.3100 data_time: 0.0133 memory: 6717 grad_norm: 2.9206 loss: 2.0367 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.0367 2023/04/13 22:55:17 - mmengine - INFO - Epoch(train) [15][ 300/1879] lr: 2.0000e-02 eta: 16:35:04 time: 0.4007 data_time: 0.0138 memory: 6717 grad_norm: 2.8581 loss: 1.8063 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8063 2023/04/13 22:55:23 - mmengine - INFO - Epoch(train) [15][ 320/1879] lr: 2.0000e-02 eta: 16:34:50 time: 0.3132 data_time: 0.0135 memory: 6717 grad_norm: 2.9352 loss: 1.9218 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9218 2023/04/13 22:55:31 - mmengine - INFO - Epoch(train) [15][ 340/1879] lr: 2.0000e-02 eta: 16:34:47 time: 0.4053 data_time: 0.0150 memory: 6717 grad_norm: 2.8444 loss: 1.9612 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.9612 2023/04/13 22:55:37 - mmengine - INFO - Epoch(train) [15][ 360/1879] lr: 2.0000e-02 eta: 16:34:33 time: 0.3196 data_time: 0.0131 memory: 6717 grad_norm: 2.9224 loss: 1.8578 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8578 2023/04/13 22:55:45 - mmengine - INFO - Epoch(train) [15][ 380/1879] lr: 2.0000e-02 eta: 16:34:30 time: 0.4034 data_time: 0.0180 memory: 6717 grad_norm: 2.9076 loss: 1.9223 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.9223 2023/04/13 22:55:53 - mmengine - INFO - Epoch(train) [15][ 400/1879] lr: 2.0000e-02 eta: 16:34:21 time: 0.3601 data_time: 0.0122 memory: 6717 grad_norm: 2.9190 loss: 1.9498 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9498 2023/04/13 22:56:00 - mmengine - INFO - Epoch(train) [15][ 420/1879] lr: 2.0000e-02 eta: 16:34:17 time: 0.3961 data_time: 0.0160 memory: 6717 grad_norm: 2.8440 loss: 2.1234 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1234 2023/04/13 22:56:08 - mmengine - INFO - Epoch(train) [15][ 440/1879] lr: 2.0000e-02 eta: 16:34:08 time: 0.3593 data_time: 0.0136 memory: 6717 grad_norm: 2.9148 loss: 1.7610 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7610 2023/04/13 22:56:15 - mmengine - INFO - Epoch(train) [15][ 460/1879] lr: 2.0000e-02 eta: 16:34:00 time: 0.3587 data_time: 0.0170 memory: 6717 grad_norm: 2.9438 loss: 2.0574 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 2.0574 2023/04/13 22:56:22 - mmengine - INFO - Epoch(train) [15][ 480/1879] lr: 2.0000e-02 eta: 16:33:52 time: 0.3717 data_time: 0.0121 memory: 6717 grad_norm: 2.8725 loss: 2.0198 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0198 2023/04/13 22:56:30 - mmengine - INFO - Epoch(train) [15][ 500/1879] lr: 2.0000e-02 eta: 16:33:45 time: 0.3696 data_time: 0.0188 memory: 6717 grad_norm: 2.9428 loss: 2.0106 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 2.0106 2023/04/13 22:56:36 - mmengine - INFO - Epoch(train) [15][ 520/1879] lr: 2.0000e-02 eta: 16:33:34 time: 0.3398 data_time: 0.0138 memory: 6717 grad_norm: 2.9173 loss: 1.8023 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8023 2023/04/13 22:56:45 - mmengine - INFO - Epoch(train) [15][ 540/1879] lr: 2.0000e-02 eta: 16:33:31 time: 0.4058 data_time: 0.0690 memory: 6717 grad_norm: 2.9218 loss: 1.9071 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9071 2023/04/13 22:56:52 - mmengine - INFO - Epoch(train) [15][ 560/1879] lr: 2.0000e-02 eta: 16:33:23 time: 0.3695 data_time: 0.0507 memory: 6717 grad_norm: 2.8938 loss: 1.9914 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.9914 2023/04/13 22:56:59 - mmengine - INFO - Epoch(train) [15][ 580/1879] lr: 2.0000e-02 eta: 16:33:12 time: 0.3408 data_time: 0.0145 memory: 6717 grad_norm: 2.9447 loss: 1.9423 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.9423 2023/04/13 22:57:06 - mmengine - INFO - Epoch(train) [15][ 600/1879] lr: 2.0000e-02 eta: 16:33:03 time: 0.3527 data_time: 0.0119 memory: 6717 grad_norm: 2.9163 loss: 1.8802 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8802 2023/04/13 22:57:14 - mmengine - INFO - Epoch(train) [15][ 620/1879] lr: 2.0000e-02 eta: 16:33:02 time: 0.4262 data_time: 0.0156 memory: 6717 grad_norm: 2.8489 loss: 1.8410 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8410 2023/04/13 22:57:21 - mmengine - INFO - Epoch(train) [15][ 640/1879] lr: 2.0000e-02 eta: 16:32:49 time: 0.3199 data_time: 0.0128 memory: 6717 grad_norm: 2.8949 loss: 1.9506 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.9506 2023/04/13 22:57:28 - mmengine - INFO - Epoch(train) [15][ 660/1879] lr: 2.0000e-02 eta: 16:32:42 time: 0.3707 data_time: 0.0145 memory: 6717 grad_norm: 2.9057 loss: 1.8389 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.8389 2023/04/13 22:57:36 - mmengine - INFO - Epoch(train) [15][ 680/1879] lr: 2.0000e-02 eta: 16:32:34 time: 0.3717 data_time: 0.0134 memory: 6717 grad_norm: 2.9066 loss: 2.1397 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.1397 2023/04/13 22:57:41 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 22:57:43 - mmengine - INFO - Epoch(train) [15][ 700/1879] lr: 2.0000e-02 eta: 16:32:25 time: 0.3560 data_time: 0.0136 memory: 6717 grad_norm: 2.8829 loss: 1.9225 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.9225 2023/04/13 22:57:49 - mmengine - INFO - Epoch(train) [15][ 720/1879] lr: 2.0000e-02 eta: 16:32:13 time: 0.3254 data_time: 0.0137 memory: 6717 grad_norm: 3.0887 loss: 2.1627 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.1627 2023/04/13 22:57:58 - mmengine - INFO - Epoch(train) [15][ 740/1879] lr: 2.0000e-02 eta: 16:32:10 time: 0.4139 data_time: 0.0138 memory: 6717 grad_norm: 2.8164 loss: 1.9881 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.9881 2023/04/13 22:58:04 - mmengine - INFO - Epoch(train) [15][ 760/1879] lr: 2.0000e-02 eta: 16:31:58 time: 0.3258 data_time: 0.0134 memory: 6717 grad_norm: 2.8945 loss: 1.9226 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9226 2023/04/13 22:58:12 - mmengine - INFO - Epoch(train) [15][ 780/1879] lr: 2.0000e-02 eta: 16:31:55 time: 0.4103 data_time: 0.0144 memory: 6717 grad_norm: 2.9493 loss: 2.0518 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.0518 2023/04/13 22:58:20 - mmengine - INFO - Epoch(train) [15][ 800/1879] lr: 2.0000e-02 eta: 16:31:49 time: 0.3809 data_time: 0.0137 memory: 6717 grad_norm: 2.8886 loss: 1.9424 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.9424 2023/04/13 22:58:27 - mmengine - INFO - Epoch(train) [15][ 820/1879] lr: 2.0000e-02 eta: 16:31:40 time: 0.3557 data_time: 0.0139 memory: 6717 grad_norm: 2.8743 loss: 1.8514 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8514 2023/04/13 22:58:34 - mmengine - INFO - Epoch(train) [15][ 840/1879] lr: 2.0000e-02 eta: 16:31:32 time: 0.3622 data_time: 0.0130 memory: 6717 grad_norm: 2.9294 loss: 2.0193 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0193 2023/04/13 22:58:42 - mmengine - INFO - Epoch(train) [15][ 860/1879] lr: 2.0000e-02 eta: 16:31:26 time: 0.3844 data_time: 0.0141 memory: 6717 grad_norm: 2.8484 loss: 1.9820 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.9820 2023/04/13 22:58:49 - mmengine - INFO - Epoch(train) [15][ 880/1879] lr: 2.0000e-02 eta: 16:31:16 time: 0.3520 data_time: 0.0134 memory: 6717 grad_norm: 2.9528 loss: 2.0639 top1_acc: 0.2500 top5_acc: 0.9375 loss_cls: 2.0639 2023/04/13 22:58:57 - mmengine - INFO - Epoch(train) [15][ 900/1879] lr: 2.0000e-02 eta: 16:31:13 time: 0.4089 data_time: 0.0149 memory: 6717 grad_norm: 2.8843 loss: 1.8259 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.8259 2023/04/13 22:59:04 - mmengine - INFO - Epoch(train) [15][ 920/1879] lr: 2.0000e-02 eta: 16:31:01 time: 0.3303 data_time: 0.0127 memory: 6717 grad_norm: 2.9564 loss: 2.0511 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0511 2023/04/13 22:59:12 - mmengine - INFO - Epoch(train) [15][ 940/1879] lr: 2.0000e-02 eta: 16:30:58 time: 0.4065 data_time: 0.0138 memory: 6717 grad_norm: 2.9504 loss: 1.8891 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8891 2023/04/13 22:59:18 - mmengine - INFO - Epoch(train) [15][ 960/1879] lr: 2.0000e-02 eta: 16:30:43 time: 0.3034 data_time: 0.0141 memory: 6717 grad_norm: 2.9362 loss: 2.0431 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0431 2023/04/13 22:59:26 - mmengine - INFO - Epoch(train) [15][ 980/1879] lr: 2.0000e-02 eta: 16:30:39 time: 0.3966 data_time: 0.0127 memory: 6717 grad_norm: 2.8562 loss: 1.9879 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.9879 2023/04/13 22:59:33 - mmengine - INFO - Epoch(train) [15][1000/1879] lr: 2.0000e-02 eta: 16:30:29 time: 0.3509 data_time: 0.0139 memory: 6717 grad_norm: 2.9470 loss: 2.0264 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0264 2023/04/13 22:59:41 - mmengine - INFO - Epoch(train) [15][1020/1879] lr: 2.0000e-02 eta: 16:30:26 time: 0.4101 data_time: 0.0137 memory: 6717 grad_norm: 2.8820 loss: 2.1364 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.1364 2023/04/13 22:59:47 - mmengine - INFO - Epoch(train) [15][1040/1879] lr: 2.0000e-02 eta: 16:30:13 time: 0.3161 data_time: 0.0145 memory: 6717 grad_norm: 2.9081 loss: 1.9233 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9233 2023/04/13 22:59:56 - mmengine - INFO - Epoch(train) [15][1060/1879] lr: 2.0000e-02 eta: 16:30:14 time: 0.4493 data_time: 0.0125 memory: 6717 grad_norm: 2.8305 loss: 2.0876 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0876 2023/04/13 23:00:03 - mmengine - INFO - Epoch(train) [15][1080/1879] lr: 2.0000e-02 eta: 16:29:59 time: 0.3052 data_time: 0.0156 memory: 6717 grad_norm: 2.8672 loss: 1.9863 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9863 2023/04/13 23:00:10 - mmengine - INFO - Epoch(train) [15][1100/1879] lr: 2.0000e-02 eta: 16:29:55 time: 0.3987 data_time: 0.0141 memory: 6717 grad_norm: 2.9674 loss: 1.8193 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8193 2023/04/13 23:00:17 - mmengine - INFO - Epoch(train) [15][1120/1879] lr: 2.0000e-02 eta: 16:29:41 time: 0.3140 data_time: 0.0145 memory: 6717 grad_norm: 2.9031 loss: 1.7788 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7788 2023/04/13 23:00:25 - mmengine - INFO - Epoch(train) [15][1140/1879] lr: 2.0000e-02 eta: 16:29:38 time: 0.3995 data_time: 0.0138 memory: 6717 grad_norm: 2.9262 loss: 1.9693 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.9693 2023/04/13 23:00:32 - mmengine - INFO - Epoch(train) [15][1160/1879] lr: 2.0000e-02 eta: 16:29:27 time: 0.3419 data_time: 0.0140 memory: 6717 grad_norm: 2.8465 loss: 2.0085 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0085 2023/04/13 23:00:39 - mmengine - INFO - Epoch(train) [15][1180/1879] lr: 2.0000e-02 eta: 16:29:21 time: 0.3814 data_time: 0.0139 memory: 6717 grad_norm: 2.9129 loss: 1.9514 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.9514 2023/04/13 23:00:47 - mmengine - INFO - Epoch(train) [15][1200/1879] lr: 2.0000e-02 eta: 16:29:14 time: 0.3792 data_time: 0.0145 memory: 6717 grad_norm: 2.8157 loss: 1.7932 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7932 2023/04/13 23:00:55 - mmengine - INFO - Epoch(train) [15][1220/1879] lr: 2.0000e-02 eta: 16:29:14 time: 0.4274 data_time: 0.0147 memory: 6717 grad_norm: 2.8506 loss: 1.9133 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9133 2023/04/13 23:01:01 - mmengine - INFO - Epoch(train) [15][1240/1879] lr: 2.0000e-02 eta: 16:28:57 time: 0.2919 data_time: 0.0133 memory: 6717 grad_norm: 2.8426 loss: 1.7443 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7443 2023/04/13 23:01:09 - mmengine - INFO - Epoch(train) [15][1260/1879] lr: 2.0000e-02 eta: 16:28:54 time: 0.4064 data_time: 0.0148 memory: 6717 grad_norm: 2.9695 loss: 1.8645 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8645 2023/04/13 23:01:16 - mmengine - INFO - Epoch(train) [15][1280/1879] lr: 2.0000e-02 eta: 16:28:42 time: 0.3318 data_time: 0.0132 memory: 6717 grad_norm: 2.8898 loss: 1.7549 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.7549 2023/04/13 23:01:24 - mmengine - INFO - Epoch(train) [15][1300/1879] lr: 2.0000e-02 eta: 16:28:36 time: 0.3822 data_time: 0.0131 memory: 6717 grad_norm: 2.9308 loss: 1.8612 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8612 2023/04/13 23:01:31 - mmengine - INFO - Epoch(train) [15][1320/1879] lr: 2.0000e-02 eta: 16:28:26 time: 0.3492 data_time: 0.0147 memory: 6717 grad_norm: 2.8664 loss: 1.7986 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7986 2023/04/13 23:01:38 - mmengine - INFO - Epoch(train) [15][1340/1879] lr: 2.0000e-02 eta: 16:28:18 time: 0.3603 data_time: 0.0234 memory: 6717 grad_norm: 3.0222 loss: 1.9221 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 1.9221 2023/04/13 23:01:45 - mmengine - INFO - Epoch(train) [15][1360/1879] lr: 2.0000e-02 eta: 16:28:11 time: 0.3768 data_time: 0.0144 memory: 6717 grad_norm: 2.9310 loss: 1.7950 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7950 2023/04/13 23:01:53 - mmengine - INFO - Epoch(train) [15][1380/1879] lr: 2.0000e-02 eta: 16:28:04 time: 0.3724 data_time: 0.0131 memory: 6717 grad_norm: 2.8508 loss: 1.9462 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.9462 2023/04/13 23:02:00 - mmengine - INFO - Epoch(train) [15][1400/1879] lr: 2.0000e-02 eta: 16:27:57 time: 0.3751 data_time: 0.0149 memory: 6717 grad_norm: 2.8055 loss: 1.8185 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8185 2023/04/13 23:02:07 - mmengine - INFO - Epoch(train) [15][1420/1879] lr: 2.0000e-02 eta: 16:27:45 time: 0.3311 data_time: 0.0132 memory: 6717 grad_norm: 2.8228 loss: 1.9691 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9691 2023/04/13 23:02:14 - mmengine - INFO - Epoch(train) [15][1440/1879] lr: 2.0000e-02 eta: 16:27:39 time: 0.3754 data_time: 0.0151 memory: 6717 grad_norm: 2.9991 loss: 1.9853 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9853 2023/04/13 23:02:21 - mmengine - INFO - Epoch(train) [15][1460/1879] lr: 2.0000e-02 eta: 16:27:29 time: 0.3526 data_time: 0.0135 memory: 6717 grad_norm: 2.9123 loss: 1.7464 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7464 2023/04/13 23:02:29 - mmengine - INFO - Epoch(train) [15][1480/1879] lr: 2.0000e-02 eta: 16:27:24 time: 0.3855 data_time: 0.0161 memory: 6717 grad_norm: 2.8845 loss: 1.8667 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8667 2023/04/13 23:02:36 - mmengine - INFO - Epoch(train) [15][1500/1879] lr: 2.0000e-02 eta: 16:27:11 time: 0.3276 data_time: 0.0120 memory: 6717 grad_norm: 2.8735 loss: 1.8569 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8569 2023/04/13 23:02:44 - mmengine - INFO - Epoch(train) [15][1520/1879] lr: 2.0000e-02 eta: 16:27:09 time: 0.4156 data_time: 0.0156 memory: 6717 grad_norm: 2.8830 loss: 2.0863 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0863 2023/04/13 23:02:51 - mmengine - INFO - Epoch(train) [15][1540/1879] lr: 2.0000e-02 eta: 16:26:56 time: 0.3226 data_time: 0.0126 memory: 6717 grad_norm: 2.8484 loss: 1.8966 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8966 2023/04/13 23:02:59 - mmengine - INFO - Epoch(train) [15][1560/1879] lr: 2.0000e-02 eta: 16:26:54 time: 0.4143 data_time: 0.0147 memory: 6717 grad_norm: 2.9287 loss: 2.0818 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0818 2023/04/13 23:03:05 - mmengine - INFO - Epoch(train) [15][1580/1879] lr: 2.0000e-02 eta: 16:26:40 time: 0.3098 data_time: 0.0128 memory: 6717 grad_norm: 2.9071 loss: 2.0925 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0925 2023/04/13 23:03:14 - mmengine - INFO - Epoch(train) [15][1600/1879] lr: 2.0000e-02 eta: 16:26:39 time: 0.4252 data_time: 0.0337 memory: 6717 grad_norm: 2.9689 loss: 2.1103 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.1103 2023/04/13 23:03:20 - mmengine - INFO - Epoch(train) [15][1620/1879] lr: 2.0000e-02 eta: 16:26:27 time: 0.3350 data_time: 0.0326 memory: 6717 grad_norm: 2.8482 loss: 1.9410 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9410 2023/04/13 23:03:29 - mmengine - INFO - Epoch(train) [15][1640/1879] lr: 2.0000e-02 eta: 16:26:26 time: 0.4264 data_time: 0.0492 memory: 6717 grad_norm: 2.8919 loss: 1.8070 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8070 2023/04/13 23:03:35 - mmengine - INFO - Epoch(train) [15][1660/1879] lr: 2.0000e-02 eta: 16:26:14 time: 0.3236 data_time: 0.0219 memory: 6717 grad_norm: 2.8676 loss: 1.9808 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9808 2023/04/13 23:03:43 - mmengine - INFO - Epoch(train) [15][1680/1879] lr: 2.0000e-02 eta: 16:26:07 time: 0.3745 data_time: 0.0395 memory: 6717 grad_norm: 2.8798 loss: 1.7144 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7144 2023/04/13 23:03:47 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 23:03:49 - mmengine - INFO - Epoch(train) [15][1700/1879] lr: 2.0000e-02 eta: 16:25:55 time: 0.3329 data_time: 0.0391 memory: 6717 grad_norm: 3.0336 loss: 2.2230 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.2230 2023/04/13 23:03:58 - mmengine - INFO - Epoch(train) [15][1720/1879] lr: 2.0000e-02 eta: 16:25:53 time: 0.4134 data_time: 0.0356 memory: 6717 grad_norm: 2.8806 loss: 1.8550 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8550 2023/04/13 23:04:04 - mmengine - INFO - Epoch(train) [15][1740/1879] lr: 2.0000e-02 eta: 16:25:39 time: 0.3157 data_time: 0.0150 memory: 6717 grad_norm: 2.8996 loss: 1.6954 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6954 2023/04/13 23:04:12 - mmengine - INFO - Epoch(train) [15][1760/1879] lr: 2.0000e-02 eta: 16:25:35 time: 0.4008 data_time: 0.1010 memory: 6717 grad_norm: 2.9200 loss: 1.9693 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9693 2023/04/13 23:04:18 - mmengine - INFO - Epoch(train) [15][1780/1879] lr: 2.0000e-02 eta: 16:25:21 time: 0.3146 data_time: 0.1197 memory: 6717 grad_norm: 2.8913 loss: 1.9361 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9361 2023/04/13 23:04:26 - mmengine - INFO - Epoch(train) [15][1800/1879] lr: 2.0000e-02 eta: 16:25:16 time: 0.3865 data_time: 0.1552 memory: 6717 grad_norm: 2.8953 loss: 1.9296 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 1.9296 2023/04/13 23:04:32 - mmengine - INFO - Epoch(train) [15][1820/1879] lr: 2.0000e-02 eta: 16:25:03 time: 0.3210 data_time: 0.1395 memory: 6717 grad_norm: 2.8539 loss: 1.8597 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8597 2023/04/13 23:04:41 - mmengine - INFO - Epoch(train) [15][1840/1879] lr: 2.0000e-02 eta: 16:25:01 time: 0.4208 data_time: 0.2862 memory: 6717 grad_norm: 2.8815 loss: 1.9242 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9242 2023/04/13 23:04:47 - mmengine - INFO - Epoch(train) [15][1860/1879] lr: 2.0000e-02 eta: 16:24:48 time: 0.3213 data_time: 0.1739 memory: 6717 grad_norm: 2.8452 loss: 1.8662 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8662 2023/04/13 23:04:54 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 23:04:54 - mmengine - INFO - Epoch(train) [15][1879/1879] lr: 2.0000e-02 eta: 16:24:40 time: 0.3442 data_time: 0.2174 memory: 6717 grad_norm: 3.0030 loss: 1.7979 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.7979 2023/04/13 23:04:54 - mmengine - INFO - Saving checkpoint at 15 epochs 2023/04/13 23:05:04 - mmengine - INFO - Epoch(val) [15][ 20/155] eta: 0:01:01 time: 0.4592 data_time: 0.4259 memory: 1391 2023/04/13 23:05:10 - mmengine - INFO - Epoch(val) [15][ 40/155] eta: 0:00:44 time: 0.3138 data_time: 0.2804 memory: 1391 2023/04/13 23:05:19 - mmengine - INFO - Epoch(val) [15][ 60/155] eta: 0:00:38 time: 0.4297 data_time: 0.3967 memory: 1391 2023/04/13 23:05:25 - mmengine - INFO - Epoch(val) [15][ 80/155] eta: 0:00:28 time: 0.3210 data_time: 0.2873 memory: 1391 2023/04/13 23:05:33 - mmengine - INFO - Epoch(val) [15][100/155] eta: 0:00:21 time: 0.4200 data_time: 0.3865 memory: 1391 2023/04/13 23:05:40 - mmengine - INFO - Epoch(val) [15][120/155] eta: 0:00:13 time: 0.3377 data_time: 0.3054 memory: 1391 2023/04/13 23:05:50 - mmengine - INFO - Epoch(val) [15][140/155] eta: 0:00:05 time: 0.4813 data_time: 0.4486 memory: 1391 2023/04/13 23:05:57 - mmengine - INFO - Epoch(val) [15][155/155] acc/top1: 0.5669 acc/top5: 0.8108 acc/mean1: 0.5667 data_time: 0.4157 time: 0.4478 2023/04/13 23:05:57 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_13.pth is removed 2023/04/13 23:05:57 - mmengine - INFO - The best checkpoint with 0.5669 acc/top1 at 15 epoch is saved to best_acc_top1_epoch_15.pth. 2023/04/13 23:06:07 - mmengine - INFO - Epoch(train) [16][ 20/1879] lr: 2.0000e-02 eta: 16:24:48 time: 0.5107 data_time: 0.2918 memory: 6717 grad_norm: 2.9055 loss: 1.8625 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8625 2023/04/13 23:06:14 - mmengine - INFO - Epoch(train) [16][ 40/1879] lr: 2.0000e-02 eta: 16:24:37 time: 0.3345 data_time: 0.0661 memory: 6717 grad_norm: 2.8583 loss: 1.8848 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8848 2023/04/13 23:06:23 - mmengine - INFO - Epoch(train) [16][ 60/1879] lr: 2.0000e-02 eta: 16:24:36 time: 0.4235 data_time: 0.1473 memory: 6717 grad_norm: 2.8746 loss: 1.9061 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.9061 2023/04/13 23:06:28 - mmengine - INFO - Epoch(train) [16][ 80/1879] lr: 2.0000e-02 eta: 16:24:17 time: 0.2732 data_time: 0.0916 memory: 6717 grad_norm: 2.8784 loss: 2.0722 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0722 2023/04/13 23:06:37 - mmengine - INFO - Epoch(train) [16][ 100/1879] lr: 2.0000e-02 eta: 16:24:16 time: 0.4289 data_time: 0.1649 memory: 6717 grad_norm: 2.8354 loss: 1.7421 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7421 2023/04/13 23:06:43 - mmengine - INFO - Epoch(train) [16][ 120/1879] lr: 2.0000e-02 eta: 16:24:02 time: 0.3101 data_time: 0.1066 memory: 6717 grad_norm: 2.8935 loss: 1.7050 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7050 2023/04/13 23:06:52 - mmengine - INFO - Epoch(train) [16][ 140/1879] lr: 2.0000e-02 eta: 16:24:03 time: 0.4381 data_time: 0.2716 memory: 6717 grad_norm: 2.8440 loss: 1.9013 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.9013 2023/04/13 23:06:58 - mmengine - INFO - Epoch(train) [16][ 160/1879] lr: 2.0000e-02 eta: 16:23:50 time: 0.3212 data_time: 0.1790 memory: 6717 grad_norm: 2.9222 loss: 2.0639 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0639 2023/04/13 23:07:07 - mmengine - INFO - Epoch(train) [16][ 180/1879] lr: 2.0000e-02 eta: 16:23:49 time: 0.4285 data_time: 0.2853 memory: 6717 grad_norm: 2.8169 loss: 1.8550 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8550 2023/04/13 23:07:13 - mmengine - INFO - Epoch(train) [16][ 200/1879] lr: 2.0000e-02 eta: 16:23:34 time: 0.3075 data_time: 0.1677 memory: 6717 grad_norm: 2.8952 loss: 1.8824 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8824 2023/04/13 23:07:22 - mmengine - INFO - Epoch(train) [16][ 220/1879] lr: 2.0000e-02 eta: 16:23:35 time: 0.4401 data_time: 0.2987 memory: 6717 grad_norm: 2.9077 loss: 1.9095 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9095 2023/04/13 23:07:28 - mmengine - INFO - Epoch(train) [16][ 240/1879] lr: 2.0000e-02 eta: 16:23:22 time: 0.3200 data_time: 0.1802 memory: 6717 grad_norm: 2.8693 loss: 1.7887 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7887 2023/04/13 23:07:37 - mmengine - INFO - Epoch(train) [16][ 260/1879] lr: 2.0000e-02 eta: 16:23:24 time: 0.4546 data_time: 0.3160 memory: 6717 grad_norm: 2.8949 loss: 1.7849 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.7849 2023/04/13 23:07:44 - mmengine - INFO - Epoch(train) [16][ 280/1879] lr: 2.0000e-02 eta: 16:23:13 time: 0.3414 data_time: 0.2045 memory: 6717 grad_norm: 2.8252 loss: 1.7091 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7091 2023/04/13 23:07:52 - mmengine - INFO - Epoch(train) [16][ 300/1879] lr: 2.0000e-02 eta: 16:23:10 time: 0.4078 data_time: 0.2674 memory: 6717 grad_norm: 2.8854 loss: 1.8590 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8590 2023/04/13 23:07:59 - mmengine - INFO - Epoch(train) [16][ 320/1879] lr: 2.0000e-02 eta: 16:22:59 time: 0.3324 data_time: 0.1932 memory: 6717 grad_norm: 2.9520 loss: 1.7782 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7782 2023/04/13 23:08:07 - mmengine - INFO - Epoch(train) [16][ 340/1879] lr: 2.0000e-02 eta: 16:22:57 time: 0.4197 data_time: 0.2798 memory: 6717 grad_norm: 2.8663 loss: 1.6476 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.6476 2023/04/13 23:08:13 - mmengine - INFO - Epoch(train) [16][ 360/1879] lr: 2.0000e-02 eta: 16:22:41 time: 0.2917 data_time: 0.1513 memory: 6717 grad_norm: 2.8887 loss: 1.9035 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9035 2023/04/13 23:08:21 - mmengine - INFO - Epoch(train) [16][ 380/1879] lr: 2.0000e-02 eta: 16:22:36 time: 0.3941 data_time: 0.2535 memory: 6717 grad_norm: 2.8616 loss: 1.9379 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9379 2023/04/13 23:08:28 - mmengine - INFO - Epoch(train) [16][ 400/1879] lr: 2.0000e-02 eta: 16:22:25 time: 0.3401 data_time: 0.1747 memory: 6717 grad_norm: 2.8583 loss: 1.9810 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9810 2023/04/13 23:08:36 - mmengine - INFO - Epoch(train) [16][ 420/1879] lr: 2.0000e-02 eta: 16:22:22 time: 0.4095 data_time: 0.1782 memory: 6717 grad_norm: 2.9553 loss: 1.7844 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.7844 2023/04/13 23:08:42 - mmengine - INFO - Epoch(train) [16][ 440/1879] lr: 2.0000e-02 eta: 16:22:10 time: 0.3243 data_time: 0.1509 memory: 6717 grad_norm: 2.8819 loss: 1.9410 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9410 2023/04/13 23:08:50 - mmengine - INFO - Epoch(train) [16][ 460/1879] lr: 2.0000e-02 eta: 16:22:04 time: 0.3894 data_time: 0.2464 memory: 6717 grad_norm: 2.8852 loss: 1.9842 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 1.9842 2023/04/13 23:08:56 - mmengine - INFO - Epoch(train) [16][ 480/1879] lr: 2.0000e-02 eta: 16:21:51 time: 0.3153 data_time: 0.1610 memory: 6717 grad_norm: 2.9270 loss: 1.8849 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8849 2023/04/13 23:09:06 - mmengine - INFO - Epoch(train) [16][ 500/1879] lr: 2.0000e-02 eta: 16:21:53 time: 0.4583 data_time: 0.1932 memory: 6717 grad_norm: 2.9026 loss: 1.9193 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9193 2023/04/13 23:09:13 - mmengine - INFO - Epoch(train) [16][ 520/1879] lr: 2.0000e-02 eta: 16:21:44 time: 0.3479 data_time: 0.0417 memory: 6717 grad_norm: 2.8320 loss: 1.7507 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7507 2023/04/13 23:09:21 - mmengine - INFO - Epoch(train) [16][ 540/1879] lr: 2.0000e-02 eta: 16:21:44 time: 0.4381 data_time: 0.0650 memory: 6717 grad_norm: 2.8572 loss: 1.8779 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.8779 2023/04/13 23:09:28 - mmengine - INFO - Epoch(train) [16][ 560/1879] lr: 2.0000e-02 eta: 16:21:31 time: 0.3231 data_time: 0.0119 memory: 6717 grad_norm: 2.9882 loss: 1.8583 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8583 2023/04/13 23:09:35 - mmengine - INFO - Epoch(train) [16][ 580/1879] lr: 2.0000e-02 eta: 16:21:25 time: 0.3817 data_time: 0.0758 memory: 6717 grad_norm: 2.8844 loss: 1.6731 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.6731 2023/04/13 23:09:41 - mmengine - INFO - Epoch(train) [16][ 600/1879] lr: 2.0000e-02 eta: 16:21:10 time: 0.2991 data_time: 0.1506 memory: 6717 grad_norm: 2.9746 loss: 1.9442 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9442 2023/04/13 23:09:49 - mmengine - INFO - Epoch(train) [16][ 620/1879] lr: 2.0000e-02 eta: 16:21:03 time: 0.3738 data_time: 0.2259 memory: 6717 grad_norm: 2.8908 loss: 2.0421 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0421 2023/04/13 23:09:56 - mmengine - INFO - Epoch(train) [16][ 640/1879] lr: 2.0000e-02 eta: 16:20:54 time: 0.3601 data_time: 0.1203 memory: 6717 grad_norm: 2.8975 loss: 1.9000 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9000 2023/04/13 23:10:04 - mmengine - INFO - Epoch(train) [16][ 660/1879] lr: 2.0000e-02 eta: 16:20:52 time: 0.4174 data_time: 0.0490 memory: 6717 grad_norm: 2.8394 loss: 1.9439 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9439 2023/04/13 23:10:11 - mmengine - INFO - Epoch(train) [16][ 680/1879] lr: 2.0000e-02 eta: 16:20:39 time: 0.3214 data_time: 0.0155 memory: 6717 grad_norm: 2.9337 loss: 1.6982 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6982 2023/04/13 23:10:19 - mmengine - INFO - Epoch(train) [16][ 700/1879] lr: 2.0000e-02 eta: 16:20:34 time: 0.3848 data_time: 0.0503 memory: 6717 grad_norm: 2.9394 loss: 1.7864 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7864 2023/04/13 23:10:25 - mmengine - INFO - Epoch(train) [16][ 720/1879] lr: 2.0000e-02 eta: 16:20:20 time: 0.3146 data_time: 0.0806 memory: 6717 grad_norm: 2.9311 loss: 1.9517 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9517 2023/04/13 23:10:33 - mmengine - INFO - Epoch(train) [16][ 740/1879] lr: 2.0000e-02 eta: 16:20:15 time: 0.3941 data_time: 0.1235 memory: 6717 grad_norm: 2.9410 loss: 1.9885 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9885 2023/04/13 23:10:40 - mmengine - INFO - Epoch(train) [16][ 760/1879] lr: 2.0000e-02 eta: 16:20:05 time: 0.3424 data_time: 0.1037 memory: 6717 grad_norm: 2.8293 loss: 1.8651 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.8651 2023/04/13 23:10:48 - mmengine - INFO - Epoch(train) [16][ 780/1879] lr: 2.0000e-02 eta: 16:20:02 time: 0.4129 data_time: 0.0870 memory: 6717 grad_norm: 2.8077 loss: 1.7957 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7957 2023/04/13 23:10:54 - mmengine - INFO - Epoch(train) [16][ 800/1879] lr: 2.0000e-02 eta: 16:19:48 time: 0.3040 data_time: 0.0410 memory: 6717 grad_norm: 2.8913 loss: 1.9204 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9204 2023/04/13 23:10:59 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 23:11:02 - mmengine - INFO - Epoch(train) [16][ 820/1879] lr: 2.0000e-02 eta: 16:19:42 time: 0.3823 data_time: 0.1199 memory: 6717 grad_norm: 2.8404 loss: 2.0624 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0624 2023/04/13 23:11:09 - mmengine - INFO - Epoch(train) [16][ 840/1879] lr: 2.0000e-02 eta: 16:19:35 time: 0.3741 data_time: 0.0471 memory: 6717 grad_norm: 2.8602 loss: 1.8731 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.8731 2023/04/13 23:11:17 - mmengine - INFO - Epoch(train) [16][ 860/1879] lr: 2.0000e-02 eta: 16:19:30 time: 0.3939 data_time: 0.0153 memory: 6717 grad_norm: 2.8524 loss: 1.8987 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8987 2023/04/13 23:11:24 - mmengine - INFO - Epoch(train) [16][ 880/1879] lr: 2.0000e-02 eta: 16:19:23 time: 0.3776 data_time: 0.0138 memory: 6717 grad_norm: 3.0021 loss: 1.7339 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7339 2023/04/13 23:11:31 - mmengine - INFO - Epoch(train) [16][ 900/1879] lr: 2.0000e-02 eta: 16:19:14 time: 0.3504 data_time: 0.0139 memory: 6717 grad_norm: 2.8879 loss: 2.0208 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0208 2023/04/13 23:11:39 - mmengine - INFO - Epoch(train) [16][ 920/1879] lr: 2.0000e-02 eta: 16:19:07 time: 0.3737 data_time: 0.0598 memory: 6717 grad_norm: 2.9673 loss: 1.8731 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8731 2023/04/13 23:11:46 - mmengine - INFO - Epoch(train) [16][ 940/1879] lr: 2.0000e-02 eta: 16:18:56 time: 0.3400 data_time: 0.1043 memory: 6717 grad_norm: 2.9889 loss: 1.8611 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8611 2023/04/13 23:11:53 - mmengine - INFO - Epoch(train) [16][ 960/1879] lr: 2.0000e-02 eta: 16:18:47 time: 0.3570 data_time: 0.1052 memory: 6717 grad_norm: 2.8948 loss: 1.8753 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8753 2023/04/13 23:12:00 - mmengine - INFO - Epoch(train) [16][ 980/1879] lr: 2.0000e-02 eta: 16:18:40 time: 0.3708 data_time: 0.0930 memory: 6717 grad_norm: 2.8374 loss: 1.8567 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8567 2023/04/13 23:12:08 - mmengine - INFO - Epoch(train) [16][1000/1879] lr: 2.0000e-02 eta: 16:18:33 time: 0.3775 data_time: 0.0450 memory: 6717 grad_norm: 2.8707 loss: 2.0004 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.0004 2023/04/13 23:12:15 - mmengine - INFO - Epoch(train) [16][1020/1879] lr: 2.0000e-02 eta: 16:18:24 time: 0.3558 data_time: 0.0315 memory: 6717 grad_norm: 2.8349 loss: 2.0333 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0333 2023/04/13 23:12:22 - mmengine - INFO - Epoch(train) [16][1040/1879] lr: 2.0000e-02 eta: 16:18:16 time: 0.3616 data_time: 0.0663 memory: 6717 grad_norm: 2.8424 loss: 1.9402 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9402 2023/04/13 23:12:30 - mmengine - INFO - Epoch(train) [16][1060/1879] lr: 2.0000e-02 eta: 16:18:08 time: 0.3668 data_time: 0.0478 memory: 6717 grad_norm: 2.8555 loss: 1.9122 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9122 2023/04/13 23:12:37 - mmengine - INFO - Epoch(train) [16][1080/1879] lr: 2.0000e-02 eta: 16:18:04 time: 0.3935 data_time: 0.0318 memory: 6717 grad_norm: 2.7785 loss: 2.3469 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3469 2023/04/13 23:12:44 - mmengine - INFO - Epoch(train) [16][1100/1879] lr: 2.0000e-02 eta: 16:17:54 time: 0.3458 data_time: 0.0252 memory: 6717 grad_norm: 2.8800 loss: 1.6652 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.6652 2023/04/13 23:12:52 - mmengine - INFO - Epoch(train) [16][1120/1879] lr: 2.0000e-02 eta: 16:17:45 time: 0.3587 data_time: 0.1202 memory: 6717 grad_norm: 2.8398 loss: 1.8043 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8043 2023/04/13 23:12:59 - mmengine - INFO - Epoch(train) [16][1140/1879] lr: 2.0000e-02 eta: 16:17:40 time: 0.3967 data_time: 0.0380 memory: 6717 grad_norm: 2.8991 loss: 1.9774 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9774 2023/04/13 23:13:07 - mmengine - INFO - Epoch(train) [16][1160/1879] lr: 2.0000e-02 eta: 16:17:35 time: 0.3876 data_time: 0.1003 memory: 6717 grad_norm: 2.8611 loss: 1.8916 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8916 2023/04/13 23:13:15 - mmengine - INFO - Epoch(train) [16][1180/1879] lr: 2.0000e-02 eta: 16:17:28 time: 0.3736 data_time: 0.1232 memory: 6717 grad_norm: 2.9112 loss: 1.8082 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8082 2023/04/13 23:13:22 - mmengine - INFO - Epoch(train) [16][1200/1879] lr: 2.0000e-02 eta: 16:17:18 time: 0.3488 data_time: 0.1321 memory: 6717 grad_norm: 2.8345 loss: 1.7821 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.7821 2023/04/13 23:13:30 - mmengine - INFO - Epoch(train) [16][1220/1879] lr: 2.0000e-02 eta: 16:17:17 time: 0.4258 data_time: 0.0149 memory: 6717 grad_norm: 2.8089 loss: 1.8971 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8971 2023/04/13 23:13:36 - mmengine - INFO - Epoch(train) [16][1240/1879] lr: 2.0000e-02 eta: 16:17:01 time: 0.2897 data_time: 0.0139 memory: 6717 grad_norm: 2.7723 loss: 1.7783 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7783 2023/04/13 23:13:44 - mmengine - INFO - Epoch(train) [16][1260/1879] lr: 2.0000e-02 eta: 16:16:58 time: 0.4168 data_time: 0.0135 memory: 6717 grad_norm: 2.8836 loss: 1.7736 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7736 2023/04/13 23:13:51 - mmengine - INFO - Epoch(train) [16][1280/1879] lr: 2.0000e-02 eta: 16:16:47 time: 0.3350 data_time: 0.0143 memory: 6717 grad_norm: 2.8719 loss: 2.0256 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0256 2023/04/13 23:13:59 - mmengine - INFO - Epoch(train) [16][1300/1879] lr: 2.0000e-02 eta: 16:16:43 time: 0.3978 data_time: 0.0128 memory: 6717 grad_norm: 2.9917 loss: 1.9572 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9572 2023/04/13 23:14:06 - mmengine - INFO - Epoch(train) [16][1320/1879] lr: 2.0000e-02 eta: 16:16:31 time: 0.3267 data_time: 0.0142 memory: 6717 grad_norm: 2.9006 loss: 1.8772 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8772 2023/04/13 23:14:14 - mmengine - INFO - Epoch(train) [16][1340/1879] lr: 2.0000e-02 eta: 16:16:28 time: 0.4143 data_time: 0.0134 memory: 6717 grad_norm: 2.7720 loss: 2.0157 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0157 2023/04/13 23:14:20 - mmengine - INFO - Epoch(train) [16][1360/1879] lr: 2.0000e-02 eta: 16:16:16 time: 0.3218 data_time: 0.0137 memory: 6717 grad_norm: 2.8689 loss: 2.0110 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.0110 2023/04/13 23:14:28 - mmengine - INFO - Epoch(train) [16][1380/1879] lr: 2.0000e-02 eta: 16:16:10 time: 0.3877 data_time: 0.0132 memory: 6717 grad_norm: 2.9645 loss: 2.0975 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0975 2023/04/13 23:14:35 - mmengine - INFO - Epoch(train) [16][1400/1879] lr: 2.0000e-02 eta: 16:16:00 time: 0.3415 data_time: 0.0141 memory: 6717 grad_norm: 2.8413 loss: 1.8871 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.8871 2023/04/13 23:14:43 - mmengine - INFO - Epoch(train) [16][1420/1879] lr: 2.0000e-02 eta: 16:15:58 time: 0.4197 data_time: 0.0159 memory: 6717 grad_norm: 2.8852 loss: 1.7838 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7838 2023/04/13 23:14:50 - mmengine - INFO - Epoch(train) [16][1440/1879] lr: 2.0000e-02 eta: 16:15:45 time: 0.3208 data_time: 0.0147 memory: 6717 grad_norm: 2.9135 loss: 1.9422 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.9422 2023/04/13 23:14:58 - mmengine - INFO - Epoch(train) [16][1460/1879] lr: 2.0000e-02 eta: 16:15:44 time: 0.4312 data_time: 0.0153 memory: 6717 grad_norm: 2.8739 loss: 1.9105 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9105 2023/04/13 23:15:05 - mmengine - INFO - Epoch(train) [16][1480/1879] lr: 2.0000e-02 eta: 16:15:35 time: 0.3536 data_time: 0.0154 memory: 6717 grad_norm: 2.8359 loss: 1.8352 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.8352 2023/04/13 23:15:13 - mmengine - INFO - Epoch(train) [16][1500/1879] lr: 2.0000e-02 eta: 16:15:32 time: 0.4079 data_time: 0.0136 memory: 6717 grad_norm: 2.8487 loss: 1.9150 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9150 2023/04/13 23:15:20 - mmengine - INFO - Epoch(train) [16][1520/1879] lr: 2.0000e-02 eta: 16:15:21 time: 0.3391 data_time: 0.0148 memory: 6717 grad_norm: 2.8627 loss: 1.8781 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8781 2023/04/13 23:15:29 - mmengine - INFO - Epoch(train) [16][1540/1879] lr: 2.0000e-02 eta: 16:15:19 time: 0.4190 data_time: 0.0132 memory: 6717 grad_norm: 2.8349 loss: 1.8117 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.8117 2023/04/13 23:15:36 - mmengine - INFO - Epoch(train) [16][1560/1879] lr: 2.0000e-02 eta: 16:15:09 time: 0.3460 data_time: 0.0144 memory: 6717 grad_norm: 2.9730 loss: 2.0591 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 2.0591 2023/04/13 23:15:43 - mmengine - INFO - Epoch(train) [16][1580/1879] lr: 2.0000e-02 eta: 16:15:03 time: 0.3884 data_time: 0.0143 memory: 6717 grad_norm: 2.8420 loss: 1.9132 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9132 2023/04/13 23:15:50 - mmengine - INFO - Epoch(train) [16][1600/1879] lr: 2.0000e-02 eta: 16:14:52 time: 0.3333 data_time: 0.0142 memory: 6717 grad_norm: 2.8397 loss: 1.9395 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9395 2023/04/13 23:15:59 - mmengine - INFO - Epoch(train) [16][1620/1879] lr: 2.0000e-02 eta: 16:14:51 time: 0.4279 data_time: 0.0124 memory: 6717 grad_norm: 2.7990 loss: 1.7748 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7748 2023/04/13 23:16:05 - mmengine - INFO - Epoch(train) [16][1640/1879] lr: 2.0000e-02 eta: 16:14:37 time: 0.3125 data_time: 0.0151 memory: 6717 grad_norm: 2.8572 loss: 1.6971 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6971 2023/04/13 23:16:13 - mmengine - INFO - Epoch(train) [16][1660/1879] lr: 2.0000e-02 eta: 16:14:35 time: 0.4151 data_time: 0.0126 memory: 6717 grad_norm: 2.8281 loss: 2.1898 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.1898 2023/04/13 23:16:19 - mmengine - INFO - Epoch(train) [16][1680/1879] lr: 2.0000e-02 eta: 16:14:19 time: 0.2950 data_time: 0.0156 memory: 6717 grad_norm: 2.8978 loss: 1.8110 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8110 2023/04/13 23:16:27 - mmengine - INFO - Epoch(train) [16][1700/1879] lr: 2.0000e-02 eta: 16:14:15 time: 0.3943 data_time: 0.0146 memory: 6717 grad_norm: 2.9136 loss: 2.0237 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0237 2023/04/13 23:16:35 - mmengine - INFO - Epoch(train) [16][1720/1879] lr: 2.0000e-02 eta: 16:14:09 time: 0.3909 data_time: 0.0145 memory: 6717 grad_norm: 2.8930 loss: 2.0909 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.0909 2023/04/13 23:16:42 - mmengine - INFO - Epoch(train) [16][1740/1879] lr: 2.0000e-02 eta: 16:13:59 time: 0.3389 data_time: 0.0191 memory: 6717 grad_norm: 2.8115 loss: 1.8831 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.8831 2023/04/13 23:16:49 - mmengine - INFO - Epoch(train) [16][1760/1879] lr: 2.0000e-02 eta: 16:13:54 time: 0.3982 data_time: 0.0151 memory: 6717 grad_norm: 2.8888 loss: 1.9192 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9192 2023/04/13 23:16:56 - mmengine - INFO - Epoch(train) [16][1780/1879] lr: 2.0000e-02 eta: 16:13:41 time: 0.3133 data_time: 0.0123 memory: 6717 grad_norm: 2.9341 loss: 1.9774 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9774 2023/04/13 23:17:04 - mmengine - INFO - Epoch(train) [16][1800/1879] lr: 2.0000e-02 eta: 16:13:38 time: 0.4159 data_time: 0.0430 memory: 6717 grad_norm: 2.8724 loss: 1.6968 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6968 2023/04/13 23:17:10 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 23:17:10 - mmengine - INFO - Epoch(train) [16][1820/1879] lr: 2.0000e-02 eta: 16:13:26 time: 0.3194 data_time: 0.0126 memory: 6717 grad_norm: 2.8138 loss: 1.8889 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8889 2023/04/13 23:17:19 - mmengine - INFO - Epoch(train) [16][1840/1879] lr: 2.0000e-02 eta: 16:13:25 time: 0.4357 data_time: 0.0239 memory: 6717 grad_norm: 2.8345 loss: 2.0299 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0299 2023/04/13 23:17:25 - mmengine - INFO - Epoch(train) [16][1860/1879] lr: 2.0000e-02 eta: 16:13:12 time: 0.3128 data_time: 0.0154 memory: 6717 grad_norm: 2.8628 loss: 1.9138 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9138 2023/04/13 23:17:31 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 23:17:31 - mmengine - INFO - Epoch(train) [16][1879/1879] lr: 2.0000e-02 eta: 16:12:57 time: 0.2843 data_time: 0.0290 memory: 6717 grad_norm: 2.9394 loss: 1.9912 top1_acc: 0.4286 top5_acc: 0.7143 loss_cls: 1.9912 2023/04/13 23:17:40 - mmengine - INFO - Epoch(val) [16][ 20/155] eta: 0:01:00 time: 0.4515 data_time: 0.4182 memory: 1391 2023/04/13 23:17:46 - mmengine - INFO - Epoch(val) [16][ 40/155] eta: 0:00:44 time: 0.3230 data_time: 0.2898 memory: 1391 2023/04/13 23:17:55 - mmengine - INFO - Epoch(val) [16][ 60/155] eta: 0:00:38 time: 0.4315 data_time: 0.3981 memory: 1391 2023/04/13 23:18:01 - mmengine - INFO - Epoch(val) [16][ 80/155] eta: 0:00:28 time: 0.3152 data_time: 0.2823 memory: 1391 2023/04/13 23:18:11 - mmengine - INFO - Epoch(val) [16][100/155] eta: 0:00:21 time: 0.4542 data_time: 0.4207 memory: 1391 2023/04/13 23:18:17 - mmengine - INFO - Epoch(val) [16][120/155] eta: 0:00:13 time: 0.3073 data_time: 0.2743 memory: 1391 2023/04/13 23:18:26 - mmengine - INFO - Epoch(val) [16][140/155] eta: 0:00:05 time: 0.4833 data_time: 0.4501 memory: 1391 2023/04/13 23:18:34 - mmengine - INFO - Epoch(val) [16][155/155] acc/top1: 0.5727 acc/top5: 0.8163 acc/mean1: 0.5727 data_time: 0.4169 time: 0.4497 2023/04/13 23:18:34 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_15.pth is removed 2023/04/13 23:18:34 - mmengine - INFO - The best checkpoint with 0.5727 acc/top1 at 16 epoch is saved to best_acc_top1_epoch_16.pth. 2023/04/13 23:18:50 - mmengine - INFO - Epoch(train) [17][ 20/1879] lr: 2.0000e-02 eta: 16:13:34 time: 0.7939 data_time: 0.1484 memory: 6717 grad_norm: 2.8749 loss: 1.8931 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8931 2023/04/13 23:18:59 - mmengine - INFO - Epoch(train) [17][ 40/1879] lr: 2.0000e-02 eta: 16:13:34 time: 0.4391 data_time: 0.0133 memory: 6717 grad_norm: 2.7913 loss: 1.5693 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5693 2023/04/13 23:19:05 - mmengine - INFO - Epoch(train) [17][ 60/1879] lr: 2.0000e-02 eta: 16:13:22 time: 0.3311 data_time: 0.0146 memory: 6717 grad_norm: 2.8691 loss: 1.8452 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8452 2023/04/13 23:19:14 - mmengine - INFO - Epoch(train) [17][ 80/1879] lr: 2.0000e-02 eta: 16:13:20 time: 0.4169 data_time: 0.0130 memory: 6717 grad_norm: 2.9026 loss: 1.9377 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.9377 2023/04/13 23:19:21 - mmengine - INFO - Epoch(train) [17][ 100/1879] lr: 2.0000e-02 eta: 16:13:11 time: 0.3527 data_time: 0.0140 memory: 6717 grad_norm: 2.8597 loss: 1.9546 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9546 2023/04/13 23:19:28 - mmengine - INFO - Epoch(train) [17][ 120/1879] lr: 2.0000e-02 eta: 16:13:04 time: 0.3770 data_time: 0.0134 memory: 6717 grad_norm: 2.7975 loss: 1.7095 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.7095 2023/04/13 23:19:34 - mmengine - INFO - Epoch(train) [17][ 140/1879] lr: 2.0000e-02 eta: 16:12:51 time: 0.3161 data_time: 0.0144 memory: 6717 grad_norm: 2.9805 loss: 1.8576 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8576 2023/04/13 23:19:42 - mmengine - INFO - Epoch(train) [17][ 160/1879] lr: 2.0000e-02 eta: 16:12:44 time: 0.3742 data_time: 0.0162 memory: 6717 grad_norm: 2.8752 loss: 1.9446 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.9446 2023/04/13 23:19:49 - mmengine - INFO - Epoch(train) [17][ 180/1879] lr: 2.0000e-02 eta: 16:12:33 time: 0.3402 data_time: 0.0146 memory: 6717 grad_norm: 2.8793 loss: 1.8711 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8711 2023/04/13 23:19:58 - mmengine - INFO - Epoch(train) [17][ 200/1879] lr: 2.0000e-02 eta: 16:12:35 time: 0.4521 data_time: 0.0157 memory: 6717 grad_norm: 2.9313 loss: 1.8090 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8090 2023/04/13 23:20:04 - mmengine - INFO - Epoch(train) [17][ 220/1879] lr: 2.0000e-02 eta: 16:12:23 time: 0.3327 data_time: 0.0122 memory: 6717 grad_norm: 2.9023 loss: 1.8500 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8500 2023/04/13 23:20:13 - mmengine - INFO - Epoch(train) [17][ 240/1879] lr: 2.0000e-02 eta: 16:12:22 time: 0.4262 data_time: 0.0147 memory: 6717 grad_norm: 2.8278 loss: 1.8142 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8142 2023/04/13 23:20:19 - mmengine - INFO - Epoch(train) [17][ 260/1879] lr: 2.0000e-02 eta: 16:12:09 time: 0.3218 data_time: 0.0114 memory: 6717 grad_norm: 2.8341 loss: 1.7737 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7737 2023/04/13 23:20:27 - mmengine - INFO - Epoch(train) [17][ 280/1879] lr: 2.0000e-02 eta: 16:12:05 time: 0.3963 data_time: 0.0142 memory: 6717 grad_norm: 3.0232 loss: 1.8571 top1_acc: 0.3125 top5_acc: 0.8750 loss_cls: 1.8571 2023/04/13 23:20:34 - mmengine - INFO - Epoch(train) [17][ 300/1879] lr: 2.0000e-02 eta: 16:11:52 time: 0.3158 data_time: 0.0137 memory: 6717 grad_norm: 2.9499 loss: 1.9004 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9004 2023/04/13 23:20:42 - mmengine - INFO - Epoch(train) [17][ 320/1879] lr: 2.0000e-02 eta: 16:11:49 time: 0.4159 data_time: 0.0156 memory: 6717 grad_norm: 2.8348 loss: 1.7967 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7967 2023/04/13 23:20:48 - mmengine - INFO - Epoch(train) [17][ 340/1879] lr: 2.0000e-02 eta: 16:11:35 time: 0.3087 data_time: 0.0134 memory: 6717 grad_norm: 2.8061 loss: 1.9739 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9739 2023/04/13 23:20:56 - mmengine - INFO - Epoch(train) [17][ 360/1879] lr: 2.0000e-02 eta: 16:11:30 time: 0.3912 data_time: 0.0159 memory: 6717 grad_norm: 2.8801 loss: 1.8072 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8072 2023/04/13 23:21:03 - mmengine - INFO - Epoch(train) [17][ 380/1879] lr: 2.0000e-02 eta: 16:11:20 time: 0.3469 data_time: 0.0173 memory: 6717 grad_norm: 2.8533 loss: 2.1140 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 2.1140 2023/04/13 23:21:12 - mmengine - INFO - Epoch(train) [17][ 400/1879] lr: 2.0000e-02 eta: 16:11:20 time: 0.4372 data_time: 0.0278 memory: 6717 grad_norm: 2.9059 loss: 1.8567 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8567 2023/04/13 23:21:19 - mmengine - INFO - Epoch(train) [17][ 420/1879] lr: 2.0000e-02 eta: 16:11:10 time: 0.3474 data_time: 0.0315 memory: 6717 grad_norm: 2.9159 loss: 1.7833 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7833 2023/04/13 23:21:26 - mmengine - INFO - Epoch(train) [17][ 440/1879] lr: 2.0000e-02 eta: 16:11:05 time: 0.3940 data_time: 0.0131 memory: 6717 grad_norm: 2.8965 loss: 1.8638 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8638 2023/04/13 23:21:33 - mmengine - INFO - Epoch(train) [17][ 460/1879] lr: 2.0000e-02 eta: 16:10:53 time: 0.3289 data_time: 0.0142 memory: 6717 grad_norm: 2.8552 loss: 1.7947 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.7947 2023/04/13 23:21:41 - mmengine - INFO - Epoch(train) [17][ 480/1879] lr: 2.0000e-02 eta: 16:10:47 time: 0.3789 data_time: 0.0149 memory: 6717 grad_norm: 2.8972 loss: 1.7841 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7841 2023/04/13 23:21:47 - mmengine - INFO - Epoch(train) [17][ 500/1879] lr: 2.0000e-02 eta: 16:10:36 time: 0.3350 data_time: 0.0139 memory: 6717 grad_norm: 2.9200 loss: 1.8417 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8417 2023/04/13 23:21:55 - mmengine - INFO - Epoch(train) [17][ 520/1879] lr: 2.0000e-02 eta: 16:10:30 time: 0.3891 data_time: 0.0749 memory: 6717 grad_norm: 2.8196 loss: 1.9854 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9854 2023/04/13 23:22:03 - mmengine - INFO - Epoch(train) [17][ 540/1879] lr: 2.0000e-02 eta: 16:10:23 time: 0.3751 data_time: 0.0539 memory: 6717 grad_norm: 2.8301 loss: 1.9573 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9573 2023/04/13 23:22:10 - mmengine - INFO - Epoch(train) [17][ 560/1879] lr: 2.0000e-02 eta: 16:10:17 time: 0.3750 data_time: 0.0586 memory: 6717 grad_norm: 2.9385 loss: 1.9810 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.9810 2023/04/13 23:22:17 - mmengine - INFO - Epoch(train) [17][ 580/1879] lr: 2.0000e-02 eta: 16:10:07 time: 0.3522 data_time: 0.0474 memory: 6717 grad_norm: 2.9128 loss: 1.8344 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.8344 2023/04/13 23:22:25 - mmengine - INFO - Epoch(train) [17][ 600/1879] lr: 2.0000e-02 eta: 16:10:02 time: 0.3907 data_time: 0.0446 memory: 6717 grad_norm: 2.8850 loss: 1.8080 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8080 2023/04/13 23:22:32 - mmengine - INFO - Epoch(train) [17][ 620/1879] lr: 2.0000e-02 eta: 16:09:52 time: 0.3413 data_time: 0.0267 memory: 6717 grad_norm: 2.8310 loss: 1.7574 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7574 2023/04/13 23:22:40 - mmengine - INFO - Epoch(train) [17][ 640/1879] lr: 2.0000e-02 eta: 16:09:47 time: 0.3955 data_time: 0.0266 memory: 6717 grad_norm: 2.8752 loss: 1.8552 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8552 2023/04/13 23:22:47 - mmengine - INFO - Epoch(train) [17][ 660/1879] lr: 2.0000e-02 eta: 16:09:38 time: 0.3524 data_time: 0.0338 memory: 6717 grad_norm: 2.8304 loss: 1.7273 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7273 2023/04/13 23:22:54 - mmengine - INFO - Epoch(train) [17][ 680/1879] lr: 2.0000e-02 eta: 16:09:28 time: 0.3506 data_time: 0.0687 memory: 6717 grad_norm: 2.9069 loss: 1.8924 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.8924 2023/04/13 23:23:01 - mmengine - INFO - Epoch(train) [17][ 700/1879] lr: 2.0000e-02 eta: 16:09:22 time: 0.3845 data_time: 0.0929 memory: 6717 grad_norm: 2.8038 loss: 1.9465 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.9465 2023/04/13 23:23:09 - mmengine - INFO - Epoch(train) [17][ 720/1879] lr: 2.0000e-02 eta: 16:09:16 time: 0.3817 data_time: 0.1431 memory: 6717 grad_norm: 2.9195 loss: 1.9446 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9446 2023/04/13 23:23:17 - mmengine - INFO - Epoch(train) [17][ 740/1879] lr: 2.0000e-02 eta: 16:09:13 time: 0.4095 data_time: 0.0558 memory: 6717 grad_norm: 2.7478 loss: 1.9128 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.9128 2023/04/13 23:23:25 - mmengine - INFO - Epoch(train) [17][ 760/1879] lr: 2.0000e-02 eta: 16:09:05 time: 0.3691 data_time: 0.0759 memory: 6717 grad_norm: 2.8337 loss: 1.8800 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8800 2023/04/13 23:23:32 - mmengine - INFO - Epoch(train) [17][ 780/1879] lr: 2.0000e-02 eta: 16:08:56 time: 0.3491 data_time: 0.0651 memory: 6717 grad_norm: 2.8534 loss: 1.9455 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9455 2023/04/13 23:23:39 - mmengine - INFO - Epoch(train) [17][ 800/1879] lr: 2.0000e-02 eta: 16:08:46 time: 0.3479 data_time: 0.1236 memory: 6717 grad_norm: 2.8537 loss: 1.8916 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.8916 2023/04/13 23:23:46 - mmengine - INFO - Epoch(train) [17][ 820/1879] lr: 2.0000e-02 eta: 16:08:39 time: 0.3797 data_time: 0.1060 memory: 6717 grad_norm: 2.8367 loss: 1.7319 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7319 2023/04/13 23:23:53 - mmengine - INFO - Epoch(train) [17][ 840/1879] lr: 2.0000e-02 eta: 16:08:28 time: 0.3311 data_time: 0.0485 memory: 6717 grad_norm: 2.8541 loss: 1.8704 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.8704 2023/04/13 23:24:01 - mmengine - INFO - Epoch(train) [17][ 860/1879] lr: 2.0000e-02 eta: 16:08:24 time: 0.4034 data_time: 0.0256 memory: 6717 grad_norm: 2.9660 loss: 2.1349 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.1349 2023/04/13 23:24:07 - mmengine - INFO - Epoch(train) [17][ 880/1879] lr: 2.0000e-02 eta: 16:08:12 time: 0.3252 data_time: 0.0411 memory: 6717 grad_norm: 2.8602 loss: 1.8732 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.8732 2023/04/13 23:24:15 - mmengine - INFO - Epoch(train) [17][ 900/1879] lr: 2.0000e-02 eta: 16:08:06 time: 0.3834 data_time: 0.0200 memory: 6717 grad_norm: 2.8579 loss: 1.6110 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6110 2023/04/13 23:24:22 - mmengine - INFO - Epoch(train) [17][ 920/1879] lr: 2.0000e-02 eta: 16:07:55 time: 0.3351 data_time: 0.0647 memory: 6717 grad_norm: 2.9046 loss: 1.8017 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8017 2023/04/13 23:24:28 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 23:24:30 - mmengine - INFO - Epoch(train) [17][ 940/1879] lr: 2.0000e-02 eta: 16:07:51 time: 0.4053 data_time: 0.0725 memory: 6717 grad_norm: 2.8604 loss: 1.8520 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8520 2023/04/13 23:24:37 - mmengine - INFO - Epoch(train) [17][ 960/1879] lr: 2.0000e-02 eta: 16:07:43 time: 0.3605 data_time: 0.1445 memory: 6717 grad_norm: 3.0043 loss: 1.9162 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9162 2023/04/13 23:24:44 - mmengine - INFO - Epoch(train) [17][ 980/1879] lr: 2.0000e-02 eta: 16:07:33 time: 0.3445 data_time: 0.0786 memory: 6717 grad_norm: 2.8891 loss: 2.0056 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 2.0056 2023/04/13 23:24:52 - mmengine - INFO - Epoch(train) [17][1000/1879] lr: 2.0000e-02 eta: 16:07:28 time: 0.3983 data_time: 0.1982 memory: 6717 grad_norm: 2.8419 loss: 1.9676 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9676 2023/04/13 23:24:59 - mmengine - INFO - Epoch(train) [17][1020/1879] lr: 2.0000e-02 eta: 16:07:19 time: 0.3466 data_time: 0.1095 memory: 6717 grad_norm: 2.8842 loss: 1.8477 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8477 2023/04/13 23:25:07 - mmengine - INFO - Epoch(train) [17][1040/1879] lr: 2.0000e-02 eta: 16:07:13 time: 0.3887 data_time: 0.2245 memory: 6717 grad_norm: 2.8150 loss: 1.9383 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.9383 2023/04/13 23:25:14 - mmengine - INFO - Epoch(train) [17][1060/1879] lr: 2.0000e-02 eta: 16:07:05 time: 0.3624 data_time: 0.1693 memory: 6717 grad_norm: 2.8690 loss: 1.7868 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7868 2023/04/13 23:25:22 - mmengine - INFO - Epoch(train) [17][1080/1879] lr: 2.0000e-02 eta: 16:07:02 time: 0.4161 data_time: 0.2340 memory: 6717 grad_norm: 2.8355 loss: 1.8560 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8560 2023/04/13 23:25:29 - mmengine - INFO - Epoch(train) [17][1100/1879] lr: 2.0000e-02 eta: 16:06:49 time: 0.3166 data_time: 0.1701 memory: 6717 grad_norm: 2.8655 loss: 1.9097 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.9097 2023/04/13 23:25:37 - mmengine - INFO - Epoch(train) [17][1120/1879] lr: 2.0000e-02 eta: 16:06:46 time: 0.4108 data_time: 0.2614 memory: 6717 grad_norm: 2.8257 loss: 1.7861 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7861 2023/04/13 23:25:44 - mmengine - INFO - Epoch(train) [17][1140/1879] lr: 2.0000e-02 eta: 16:06:36 time: 0.3409 data_time: 0.1139 memory: 6717 grad_norm: 2.7653 loss: 1.8100 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8100 2023/04/13 23:25:52 - mmengine - INFO - Epoch(train) [17][1160/1879] lr: 2.0000e-02 eta: 16:06:31 time: 0.3945 data_time: 0.2047 memory: 6717 grad_norm: 2.8418 loss: 1.7269 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7269 2023/04/13 23:25:58 - mmengine - INFO - Epoch(train) [17][1180/1879] lr: 2.0000e-02 eta: 16:06:17 time: 0.3103 data_time: 0.1137 memory: 6717 grad_norm: 2.8435 loss: 1.9004 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.9004 2023/04/13 23:26:05 - mmengine - INFO - Epoch(train) [17][1200/1879] lr: 2.0000e-02 eta: 16:06:08 time: 0.3542 data_time: 0.1256 memory: 6717 grad_norm: 2.8393 loss: 1.9630 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.9630 2023/04/13 23:26:12 - mmengine - INFO - Epoch(train) [17][1220/1879] lr: 2.0000e-02 eta: 16:06:02 time: 0.3797 data_time: 0.0119 memory: 6717 grad_norm: 2.8356 loss: 1.8888 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8888 2023/04/13 23:26:20 - mmengine - INFO - Epoch(train) [17][1240/1879] lr: 2.0000e-02 eta: 16:05:57 time: 0.3939 data_time: 0.0229 memory: 6717 grad_norm: 2.8023 loss: 1.7370 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.7370 2023/04/13 23:26:27 - mmengine - INFO - Epoch(train) [17][1260/1879] lr: 2.0000e-02 eta: 16:05:45 time: 0.3261 data_time: 0.0130 memory: 6717 grad_norm: 2.8448 loss: 1.9912 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9912 2023/04/13 23:26:35 - mmengine - INFO - Epoch(train) [17][1280/1879] lr: 2.0000e-02 eta: 16:05:40 time: 0.3915 data_time: 0.0152 memory: 6717 grad_norm: 2.8475 loss: 1.8282 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8282 2023/04/13 23:26:41 - mmengine - INFO - Epoch(train) [17][1300/1879] lr: 2.0000e-02 eta: 16:05:29 time: 0.3335 data_time: 0.0130 memory: 6717 grad_norm: 2.8835 loss: 1.6492 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6492 2023/04/13 23:26:49 - mmengine - INFO - Epoch(train) [17][1320/1879] lr: 2.0000e-02 eta: 16:05:21 time: 0.3704 data_time: 0.0287 memory: 6717 grad_norm: 2.7149 loss: 1.8928 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8928 2023/04/13 23:26:56 - mmengine - INFO - Epoch(train) [17][1340/1879] lr: 2.0000e-02 eta: 16:05:11 time: 0.3420 data_time: 0.0258 memory: 6717 grad_norm: 2.8391 loss: 1.9511 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9511 2023/04/13 23:27:04 - mmengine - INFO - Epoch(train) [17][1360/1879] lr: 2.0000e-02 eta: 16:05:08 time: 0.4153 data_time: 0.0139 memory: 6717 grad_norm: 2.8746 loss: 1.9024 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.9024 2023/04/13 23:27:11 - mmengine - INFO - Epoch(train) [17][1380/1879] lr: 2.0000e-02 eta: 16:04:58 time: 0.3364 data_time: 0.0135 memory: 6717 grad_norm: 2.8478 loss: 1.6111 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6111 2023/04/13 23:27:20 - mmengine - INFO - Epoch(train) [17][1400/1879] lr: 2.0000e-02 eta: 16:05:02 time: 0.4919 data_time: 0.0143 memory: 6717 grad_norm: 2.8916 loss: 1.9861 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9861 2023/04/13 23:27:27 - mmengine - INFO - Epoch(train) [17][1420/1879] lr: 2.0000e-02 eta: 16:04:50 time: 0.3214 data_time: 0.0139 memory: 6717 grad_norm: 2.8236 loss: 1.8895 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8895 2023/04/13 23:27:35 - mmengine - INFO - Epoch(train) [17][1440/1879] lr: 2.0000e-02 eta: 16:04:47 time: 0.4185 data_time: 0.0134 memory: 6717 grad_norm: 2.8280 loss: 1.9174 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.9174 2023/04/13 23:27:41 - mmengine - INFO - Epoch(train) [17][1460/1879] lr: 2.0000e-02 eta: 16:04:34 time: 0.3121 data_time: 0.0160 memory: 6717 grad_norm: 2.8563 loss: 2.0409 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 2.0409 2023/04/13 23:27:50 - mmengine - INFO - Epoch(train) [17][1480/1879] lr: 2.0000e-02 eta: 16:04:30 time: 0.4012 data_time: 0.0126 memory: 6717 grad_norm: 2.9304 loss: 1.9266 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9266 2023/04/13 23:27:55 - mmengine - INFO - Epoch(train) [17][1500/1879] lr: 2.0000e-02 eta: 16:04:13 time: 0.2785 data_time: 0.0149 memory: 6717 grad_norm: 2.7668 loss: 1.9203 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.9203 2023/04/13 23:28:03 - mmengine - INFO - Epoch(train) [17][1520/1879] lr: 2.0000e-02 eta: 16:04:09 time: 0.3990 data_time: 0.0133 memory: 6717 grad_norm: 2.8195 loss: 1.9630 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9630 2023/04/13 23:28:10 - mmengine - INFO - Epoch(train) [17][1540/1879] lr: 2.0000e-02 eta: 16:04:00 time: 0.3505 data_time: 0.0160 memory: 6717 grad_norm: 2.8793 loss: 1.8885 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8885 2023/04/13 23:28:18 - mmengine - INFO - Epoch(train) [17][1560/1879] lr: 2.0000e-02 eta: 16:03:56 time: 0.4089 data_time: 0.0124 memory: 6717 grad_norm: 2.8413 loss: 1.6340 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.6340 2023/04/13 23:28:25 - mmengine - INFO - Epoch(train) [17][1580/1879] lr: 2.0000e-02 eta: 16:03:44 time: 0.3240 data_time: 0.0153 memory: 6717 grad_norm: 2.8499 loss: 2.0717 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0717 2023/04/13 23:28:33 - mmengine - INFO - Epoch(train) [17][1600/1879] lr: 2.0000e-02 eta: 16:03:39 time: 0.3897 data_time: 0.0129 memory: 6717 grad_norm: 2.8319 loss: 1.9211 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.9211 2023/04/13 23:28:40 - mmengine - INFO - Epoch(train) [17][1620/1879] lr: 2.0000e-02 eta: 16:03:30 time: 0.3590 data_time: 0.0138 memory: 6717 grad_norm: 2.8333 loss: 1.9591 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9591 2023/04/13 23:28:47 - mmengine - INFO - Epoch(train) [17][1640/1879] lr: 2.0000e-02 eta: 16:03:21 time: 0.3523 data_time: 0.0215 memory: 6717 grad_norm: 2.7955 loss: 1.9172 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9172 2023/04/13 23:28:54 - mmengine - INFO - Epoch(train) [17][1660/1879] lr: 2.0000e-02 eta: 16:03:15 time: 0.3860 data_time: 0.0119 memory: 6717 grad_norm: 2.8920 loss: 1.8486 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.8486 2023/04/13 23:29:01 - mmengine - INFO - Epoch(train) [17][1680/1879] lr: 2.0000e-02 eta: 16:03:02 time: 0.3167 data_time: 0.0131 memory: 6717 grad_norm: 2.7715 loss: 2.0163 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0163 2023/04/13 23:29:09 - mmengine - INFO - Epoch(train) [17][1700/1879] lr: 2.0000e-02 eta: 16:02:59 time: 0.4128 data_time: 0.0143 memory: 6717 grad_norm: 2.8661 loss: 1.9612 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.9612 2023/04/13 23:29:16 - mmengine - INFO - Epoch(train) [17][1720/1879] lr: 2.0000e-02 eta: 16:02:52 time: 0.3698 data_time: 0.0125 memory: 6717 grad_norm: 2.8452 loss: 1.9987 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.9987 2023/04/13 23:29:24 - mmengine - INFO - Epoch(train) [17][1740/1879] lr: 2.0000e-02 eta: 16:02:44 time: 0.3709 data_time: 0.0154 memory: 6717 grad_norm: 2.7485 loss: 2.0404 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0404 2023/04/13 23:29:32 - mmengine - INFO - Epoch(train) [17][1760/1879] lr: 2.0000e-02 eta: 16:02:40 time: 0.4047 data_time: 0.0142 memory: 6717 grad_norm: 2.8243 loss: 1.8819 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.8819 2023/04/13 23:29:39 - mmengine - INFO - Epoch(train) [17][1780/1879] lr: 2.0000e-02 eta: 16:02:30 time: 0.3369 data_time: 0.0136 memory: 6717 grad_norm: 2.8158 loss: 2.0132 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0132 2023/04/13 23:29:46 - mmengine - INFO - Epoch(train) [17][1800/1879] lr: 2.0000e-02 eta: 16:02:22 time: 0.3705 data_time: 0.0128 memory: 6717 grad_norm: 2.8811 loss: 1.8977 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.8977 2023/04/13 23:29:53 - mmengine - INFO - Epoch(train) [17][1820/1879] lr: 2.0000e-02 eta: 16:02:13 time: 0.3450 data_time: 0.0183 memory: 6717 grad_norm: 2.7252 loss: 1.8402 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8402 2023/04/13 23:30:01 - mmengine - INFO - Epoch(train) [17][1840/1879] lr: 2.0000e-02 eta: 16:02:10 time: 0.4201 data_time: 0.0140 memory: 6717 grad_norm: 2.8413 loss: 1.8739 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8739 2023/04/13 23:30:08 - mmengine - INFO - Epoch(train) [17][1860/1879] lr: 2.0000e-02 eta: 16:01:58 time: 0.3221 data_time: 0.0140 memory: 6717 grad_norm: 2.8575 loss: 1.9384 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9384 2023/04/13 23:30:14 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 23:30:14 - mmengine - INFO - Epoch(train) [17][1879/1879] lr: 2.0000e-02 eta: 16:01:47 time: 0.3134 data_time: 0.0116 memory: 6717 grad_norm: 2.8385 loss: 2.0561 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 2.0561 2023/04/13 23:30:23 - mmengine - INFO - Epoch(val) [17][ 20/155] eta: 0:01:01 time: 0.4568 data_time: 0.4244 memory: 1391 2023/04/13 23:30:29 - mmengine - INFO - Epoch(val) [17][ 40/155] eta: 0:00:43 time: 0.2991 data_time: 0.2663 memory: 1391 2023/04/13 23:30:38 - mmengine - INFO - Epoch(val) [17][ 60/155] eta: 0:00:38 time: 0.4555 data_time: 0.4226 memory: 1391 2023/04/13 23:30:45 - mmengine - INFO - Epoch(val) [17][ 80/155] eta: 0:00:28 time: 0.3165 data_time: 0.2832 memory: 1391 2023/04/13 23:30:54 - mmengine - INFO - Epoch(val) [17][100/155] eta: 0:00:21 time: 0.4568 data_time: 0.4240 memory: 1391 2023/04/13 23:31:00 - mmengine - INFO - Epoch(val) [17][120/155] eta: 0:00:13 time: 0.2954 data_time: 0.2618 memory: 1391 2023/04/13 23:31:09 - mmengine - INFO - Epoch(val) [17][140/155] eta: 0:00:05 time: 0.4829 data_time: 0.4501 memory: 1391 2023/04/13 23:31:16 - mmengine - INFO - Epoch(val) [17][155/155] acc/top1: 0.5743 acc/top5: 0.8178 acc/mean1: 0.5742 data_time: 0.4193 time: 0.4514 2023/04/13 23:31:16 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_16.pth is removed 2023/04/13 23:31:17 - mmengine - INFO - The best checkpoint with 0.5743 acc/top1 at 17 epoch is saved to best_acc_top1_epoch_17.pth. 2023/04/13 23:31:27 - mmengine - INFO - Epoch(train) [18][ 20/1879] lr: 2.0000e-02 eta: 16:01:50 time: 0.4855 data_time: 0.3521 memory: 6717 grad_norm: 2.9753 loss: 1.8542 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8542 2023/04/13 23:31:33 - mmengine - INFO - Epoch(train) [18][ 40/1879] lr: 2.0000e-02 eta: 16:01:40 time: 0.3366 data_time: 0.2066 memory: 6717 grad_norm: 2.8062 loss: 1.7959 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7959 2023/04/13 23:31:41 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 23:31:41 - mmengine - INFO - Epoch(train) [18][ 60/1879] lr: 2.0000e-02 eta: 16:01:36 time: 0.4081 data_time: 0.2625 memory: 6717 grad_norm: 2.8750 loss: 1.7855 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7855 2023/04/13 23:31:48 - mmengine - INFO - Epoch(train) [18][ 80/1879] lr: 2.0000e-02 eta: 16:01:24 time: 0.3249 data_time: 0.1362 memory: 6717 grad_norm: 2.8241 loss: 1.8671 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8671 2023/04/13 23:31:56 - mmengine - INFO - Epoch(train) [18][ 100/1879] lr: 2.0000e-02 eta: 16:01:21 time: 0.4113 data_time: 0.1045 memory: 6717 grad_norm: 2.8262 loss: 1.8229 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.8229 2023/04/13 23:32:03 - mmengine - INFO - Epoch(train) [18][ 120/1879] lr: 2.0000e-02 eta: 16:01:10 time: 0.3362 data_time: 0.0520 memory: 6717 grad_norm: 2.7776 loss: 1.8318 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8318 2023/04/13 23:32:11 - mmengine - INFO - Epoch(train) [18][ 140/1879] lr: 2.0000e-02 eta: 16:01:07 time: 0.4159 data_time: 0.0145 memory: 6717 grad_norm: 2.8469 loss: 2.0769 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 2.0769 2023/04/13 23:32:17 - mmengine - INFO - Epoch(train) [18][ 160/1879] lr: 2.0000e-02 eta: 16:00:53 time: 0.2982 data_time: 0.0137 memory: 6717 grad_norm: 2.8016 loss: 1.9327 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.9327 2023/04/13 23:32:26 - mmengine - INFO - Epoch(train) [18][ 180/1879] lr: 2.0000e-02 eta: 16:00:51 time: 0.4318 data_time: 0.0132 memory: 6717 grad_norm: 2.9039 loss: 1.8877 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8877 2023/04/13 23:32:33 - mmengine - INFO - Epoch(train) [18][ 200/1879] lr: 2.0000e-02 eta: 16:00:40 time: 0.3323 data_time: 0.0124 memory: 6717 grad_norm: 2.8783 loss: 2.0616 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 2.0616 2023/04/13 23:32:41 - mmengine - INFO - Epoch(train) [18][ 220/1879] lr: 2.0000e-02 eta: 16:00:36 time: 0.4006 data_time: 0.0148 memory: 6717 grad_norm: 2.8939 loss: 1.7602 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7602 2023/04/13 23:32:47 - mmengine - INFO - Epoch(train) [18][ 240/1879] lr: 2.0000e-02 eta: 16:00:25 time: 0.3304 data_time: 0.0138 memory: 6717 grad_norm: 2.8846 loss: 1.9237 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9237 2023/04/13 23:32:55 - mmengine - INFO - Epoch(train) [18][ 260/1879] lr: 2.0000e-02 eta: 16:00:21 time: 0.4109 data_time: 0.0142 memory: 6717 grad_norm: 2.9015 loss: 1.8176 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8176 2023/04/13 23:33:01 - mmengine - INFO - Epoch(train) [18][ 280/1879] lr: 2.0000e-02 eta: 16:00:06 time: 0.2906 data_time: 0.0250 memory: 6717 grad_norm: 2.8233 loss: 1.9802 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.9802 2023/04/13 23:33:10 - mmengine - INFO - Epoch(train) [18][ 300/1879] lr: 2.0000e-02 eta: 16:00:05 time: 0.4341 data_time: 0.0321 memory: 6717 grad_norm: 2.8907 loss: 1.7760 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7760 2023/04/13 23:33:16 - mmengine - INFO - Epoch(train) [18][ 320/1879] lr: 2.0000e-02 eta: 15:59:53 time: 0.3237 data_time: 0.0143 memory: 6717 grad_norm: 2.7460 loss: 1.7805 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.7805 2023/04/13 23:33:25 - mmengine - INFO - Epoch(train) [18][ 340/1879] lr: 2.0000e-02 eta: 15:59:50 time: 0.4112 data_time: 0.0126 memory: 6717 grad_norm: 2.8689 loss: 1.9145 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9145 2023/04/13 23:33:31 - mmengine - INFO - Epoch(train) [18][ 360/1879] lr: 2.0000e-02 eta: 15:59:39 time: 0.3322 data_time: 0.0312 memory: 6717 grad_norm: 2.8311 loss: 1.9247 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9247 2023/04/13 23:33:40 - mmengine - INFO - Epoch(train) [18][ 380/1879] lr: 2.0000e-02 eta: 15:59:36 time: 0.4189 data_time: 0.0447 memory: 6717 grad_norm: 2.8664 loss: 1.7571 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7571 2023/04/13 23:33:46 - mmengine - INFO - Epoch(train) [18][ 400/1879] lr: 2.0000e-02 eta: 15:59:22 time: 0.2998 data_time: 0.0214 memory: 6717 grad_norm: 2.8668 loss: 1.6538 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6538 2023/04/13 23:33:55 - mmengine - INFO - Epoch(train) [18][ 420/1879] lr: 2.0000e-02 eta: 15:59:22 time: 0.4543 data_time: 0.0137 memory: 6717 grad_norm: 2.8387 loss: 1.9175 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9175 2023/04/13 23:34:01 - mmengine - INFO - Epoch(train) [18][ 440/1879] lr: 2.0000e-02 eta: 15:59:12 time: 0.3404 data_time: 0.0130 memory: 6717 grad_norm: 2.8119 loss: 1.7704 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7704 2023/04/13 23:34:10 - mmengine - INFO - Epoch(train) [18][ 460/1879] lr: 2.0000e-02 eta: 15:59:09 time: 0.4108 data_time: 0.0148 memory: 6717 grad_norm: 2.8413 loss: 1.8612 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8612 2023/04/13 23:34:16 - mmengine - INFO - Epoch(train) [18][ 480/1879] lr: 2.0000e-02 eta: 15:58:56 time: 0.3144 data_time: 0.0145 memory: 6717 grad_norm: 2.9931 loss: 1.8598 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8598 2023/04/13 23:34:24 - mmengine - INFO - Epoch(train) [18][ 500/1879] lr: 2.0000e-02 eta: 15:58:53 time: 0.4189 data_time: 0.0138 memory: 6717 grad_norm: 2.7891 loss: 1.8437 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8437 2023/04/13 23:34:31 - mmengine - INFO - Epoch(train) [18][ 520/1879] lr: 2.0000e-02 eta: 15:58:43 time: 0.3445 data_time: 0.0124 memory: 6717 grad_norm: 2.8159 loss: 1.8245 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8245 2023/04/13 23:34:40 - mmengine - INFO - Epoch(train) [18][ 540/1879] lr: 2.0000e-02 eta: 15:58:41 time: 0.4211 data_time: 0.0151 memory: 6717 grad_norm: 2.8401 loss: 2.1422 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 2.1422 2023/04/13 23:34:46 - mmengine - INFO - Epoch(train) [18][ 560/1879] lr: 2.0000e-02 eta: 15:58:28 time: 0.3141 data_time: 0.0146 memory: 6717 grad_norm: 2.8424 loss: 1.7575 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7575 2023/04/13 23:34:54 - mmengine - INFO - Epoch(train) [18][ 580/1879] lr: 2.0000e-02 eta: 15:58:25 time: 0.4158 data_time: 0.0125 memory: 6717 grad_norm: 2.7765 loss: 1.6303 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6303 2023/04/13 23:35:01 - mmengine - INFO - Epoch(train) [18][ 600/1879] lr: 2.0000e-02 eta: 15:58:15 time: 0.3374 data_time: 0.0137 memory: 6717 grad_norm: 2.9280 loss: 1.9026 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.9026 2023/04/13 23:35:10 - mmengine - INFO - Epoch(train) [18][ 620/1879] lr: 2.0000e-02 eta: 15:58:13 time: 0.4281 data_time: 0.0135 memory: 6717 grad_norm: 2.9137 loss: 1.7129 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7129 2023/04/13 23:35:16 - mmengine - INFO - Epoch(train) [18][ 640/1879] lr: 2.0000e-02 eta: 15:58:01 time: 0.3294 data_time: 0.0135 memory: 6717 grad_norm: 2.8215 loss: 1.8649 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8649 2023/04/13 23:35:23 - mmengine - INFO - Epoch(train) [18][ 660/1879] lr: 2.0000e-02 eta: 15:57:53 time: 0.3619 data_time: 0.0150 memory: 6717 grad_norm: 2.8532 loss: 1.7448 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7448 2023/04/13 23:35:30 - mmengine - INFO - Epoch(train) [18][ 680/1879] lr: 2.0000e-02 eta: 15:57:40 time: 0.3045 data_time: 0.0128 memory: 6717 grad_norm: 2.8138 loss: 1.8643 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8643 2023/04/13 23:35:39 - mmengine - INFO - Epoch(train) [18][ 700/1879] lr: 2.0000e-02 eta: 15:57:41 time: 0.4609 data_time: 0.0151 memory: 6717 grad_norm: 3.3432 loss: 1.7600 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7600 2023/04/13 23:35:45 - mmengine - INFO - Epoch(train) [18][ 720/1879] lr: 2.0000e-02 eta: 15:57:30 time: 0.3360 data_time: 0.0118 memory: 6717 grad_norm: 2.7981 loss: 1.7628 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7628 2023/04/13 23:35:53 - mmengine - INFO - Epoch(train) [18][ 740/1879] lr: 2.0000e-02 eta: 15:57:25 time: 0.3896 data_time: 0.0152 memory: 6717 grad_norm: 2.8617 loss: 1.7707 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7707 2023/04/13 23:35:59 - mmengine - INFO - Epoch(train) [18][ 760/1879] lr: 2.0000e-02 eta: 15:57:12 time: 0.3101 data_time: 0.0119 memory: 6717 grad_norm: 2.7551 loss: 1.8863 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.8863 2023/04/13 23:36:08 - mmengine - INFO - Epoch(train) [18][ 780/1879] lr: 2.0000e-02 eta: 15:57:09 time: 0.4262 data_time: 0.0316 memory: 6717 grad_norm: 2.8652 loss: 1.9250 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9250 2023/04/13 23:36:14 - mmengine - INFO - Epoch(train) [18][ 800/1879] lr: 2.0000e-02 eta: 15:56:57 time: 0.3193 data_time: 0.0282 memory: 6717 grad_norm: 2.8643 loss: 1.8793 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 1.8793 2023/04/13 23:36:22 - mmengine - INFO - Epoch(train) [18][ 820/1879] lr: 2.0000e-02 eta: 15:56:53 time: 0.3990 data_time: 0.0267 memory: 6717 grad_norm: 2.8107 loss: 1.9094 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.9094 2023/04/13 23:36:29 - mmengine - INFO - Epoch(train) [18][ 840/1879] lr: 2.0000e-02 eta: 15:56:40 time: 0.3151 data_time: 0.0184 memory: 6717 grad_norm: 2.9340 loss: 2.1032 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1032 2023/04/13 23:36:38 - mmengine - INFO - Epoch(train) [18][ 860/1879] lr: 2.0000e-02 eta: 15:56:42 time: 0.4678 data_time: 0.0490 memory: 6717 grad_norm: 2.8220 loss: 1.8282 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.8282 2023/04/13 23:36:44 - mmengine - INFO - Epoch(train) [18][ 880/1879] lr: 2.0000e-02 eta: 15:56:30 time: 0.3231 data_time: 0.0431 memory: 6717 grad_norm: 2.8688 loss: 1.6514 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6514 2023/04/13 23:36:52 - mmengine - INFO - Epoch(train) [18][ 900/1879] lr: 2.0000e-02 eta: 15:56:25 time: 0.3967 data_time: 0.0693 memory: 6717 grad_norm: 2.8532 loss: 1.9403 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9403 2023/04/13 23:36:59 - mmengine - INFO - Epoch(train) [18][ 920/1879] lr: 2.0000e-02 eta: 15:56:12 time: 0.3155 data_time: 0.0218 memory: 6717 grad_norm: 2.8329 loss: 1.9028 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9028 2023/04/13 23:37:08 - mmengine - INFO - Epoch(train) [18][ 940/1879] lr: 2.0000e-02 eta: 15:56:12 time: 0.4415 data_time: 0.0180 memory: 6717 grad_norm: 2.8139 loss: 1.7291 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7291 2023/04/13 23:37:14 - mmengine - INFO - Epoch(train) [18][ 960/1879] lr: 2.0000e-02 eta: 15:56:01 time: 0.3325 data_time: 0.0121 memory: 6717 grad_norm: 2.8123 loss: 1.8003 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8003 2023/04/13 23:37:23 - mmengine - INFO - Epoch(train) [18][ 980/1879] lr: 2.0000e-02 eta: 15:55:59 time: 0.4293 data_time: 0.0154 memory: 6717 grad_norm: 2.8443 loss: 1.8840 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8840 2023/04/13 23:37:29 - mmengine - INFO - Epoch(train) [18][1000/1879] lr: 2.0000e-02 eta: 15:55:46 time: 0.3114 data_time: 0.0123 memory: 6717 grad_norm: 2.8374 loss: 1.8650 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8650 2023/04/13 23:37:37 - mmengine - INFO - Epoch(train) [18][1020/1879] lr: 2.0000e-02 eta: 15:55:43 time: 0.4165 data_time: 0.0154 memory: 6717 grad_norm: 2.8438 loss: 1.6698 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6698 2023/04/13 23:37:44 - mmengine - INFO - Epoch(train) [18][1040/1879] lr: 2.0000e-02 eta: 15:55:32 time: 0.3273 data_time: 0.0128 memory: 6717 grad_norm: 2.8836 loss: 1.9091 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9091 2023/04/13 23:37:51 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 23:37:52 - mmengine - INFO - Epoch(train) [18][1060/1879] lr: 2.0000e-02 eta: 15:55:28 time: 0.4110 data_time: 0.0152 memory: 6717 grad_norm: 2.7751 loss: 1.9325 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9325 2023/04/13 23:37:59 - mmengine - INFO - Epoch(train) [18][1080/1879] lr: 2.0000e-02 eta: 15:55:19 time: 0.3498 data_time: 0.0124 memory: 6717 grad_norm: 2.8673 loss: 1.9283 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9283 2023/04/13 23:38:07 - mmengine - INFO - Epoch(train) [18][1100/1879] lr: 2.0000e-02 eta: 15:55:16 time: 0.4178 data_time: 0.0141 memory: 6717 grad_norm: 2.7754 loss: 1.9073 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9073 2023/04/13 23:38:14 - mmengine - INFO - Epoch(train) [18][1120/1879] lr: 2.0000e-02 eta: 15:55:05 time: 0.3318 data_time: 0.0138 memory: 6717 grad_norm: 2.8152 loss: 1.8872 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8872 2023/04/13 23:38:23 - mmengine - INFO - Epoch(train) [18][1140/1879] lr: 2.0000e-02 eta: 15:55:04 time: 0.4414 data_time: 0.0157 memory: 6717 grad_norm: 2.7964 loss: 1.8135 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.8135 2023/04/13 23:38:29 - mmengine - INFO - Epoch(train) [18][1160/1879] lr: 2.0000e-02 eta: 15:54:52 time: 0.3194 data_time: 0.0126 memory: 6717 grad_norm: 2.7804 loss: 1.6985 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.6985 2023/04/13 23:38:38 - mmengine - INFO - Epoch(train) [18][1180/1879] lr: 2.0000e-02 eta: 15:54:49 time: 0.4139 data_time: 0.0138 memory: 6717 grad_norm: 2.8702 loss: 1.9406 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9406 2023/04/13 23:38:44 - mmengine - INFO - Epoch(train) [18][1200/1879] lr: 2.0000e-02 eta: 15:54:36 time: 0.3135 data_time: 0.0145 memory: 6717 grad_norm: 2.7833 loss: 1.8857 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8857 2023/04/13 23:38:53 - mmengine - INFO - Epoch(train) [18][1220/1879] lr: 2.0000e-02 eta: 15:54:35 time: 0.4431 data_time: 0.0137 memory: 6717 grad_norm: 2.7745 loss: 1.8498 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.8498 2023/04/13 23:38:59 - mmengine - INFO - Epoch(train) [18][1240/1879] lr: 2.0000e-02 eta: 15:54:23 time: 0.3115 data_time: 0.0133 memory: 6717 grad_norm: 2.8305 loss: 1.9623 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9623 2023/04/13 23:39:07 - mmengine - INFO - Epoch(train) [18][1260/1879] lr: 2.0000e-02 eta: 15:54:18 time: 0.4054 data_time: 0.0158 memory: 6717 grad_norm: 2.7798 loss: 1.9988 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9988 2023/04/13 23:39:14 - mmengine - INFO - Epoch(train) [18][1280/1879] lr: 2.0000e-02 eta: 15:54:07 time: 0.3269 data_time: 0.0135 memory: 6717 grad_norm: 2.8116 loss: 1.9953 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9953 2023/04/13 23:39:22 - mmengine - INFO - Epoch(train) [18][1300/1879] lr: 2.0000e-02 eta: 15:54:02 time: 0.3991 data_time: 0.0135 memory: 6717 grad_norm: 2.7458 loss: 1.9411 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9411 2023/04/13 23:39:28 - mmengine - INFO - Epoch(train) [18][1320/1879] lr: 2.0000e-02 eta: 15:53:50 time: 0.3211 data_time: 0.0142 memory: 6717 grad_norm: 2.8105 loss: 1.7810 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7810 2023/04/13 23:39:36 - mmengine - INFO - Epoch(train) [18][1340/1879] lr: 2.0000e-02 eta: 15:53:45 time: 0.3939 data_time: 0.0149 memory: 6717 grad_norm: 2.8019 loss: 1.9708 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9708 2023/04/13 23:39:42 - mmengine - INFO - Epoch(train) [18][1360/1879] lr: 2.0000e-02 eta: 15:53:32 time: 0.3080 data_time: 0.0144 memory: 6717 grad_norm: 2.8819 loss: 1.7845 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7845 2023/04/13 23:39:50 - mmengine - INFO - Epoch(train) [18][1380/1879] lr: 2.0000e-02 eta: 15:53:26 time: 0.3909 data_time: 0.0130 memory: 6717 grad_norm: 2.8351 loss: 1.8972 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8972 2023/04/13 23:39:57 - mmengine - INFO - Epoch(train) [18][1400/1879] lr: 2.0000e-02 eta: 15:53:17 time: 0.3522 data_time: 0.0354 memory: 6717 grad_norm: 2.8707 loss: 1.8426 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8426 2023/04/13 23:40:05 - mmengine - INFO - Epoch(train) [18][1420/1879] lr: 2.0000e-02 eta: 15:53:15 time: 0.4259 data_time: 0.0121 memory: 6717 grad_norm: 2.9341 loss: 1.9587 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 1.9587 2023/04/13 23:40:12 - mmengine - INFO - Epoch(train) [18][1440/1879] lr: 2.0000e-02 eta: 15:53:04 time: 0.3252 data_time: 0.0132 memory: 6717 grad_norm: 2.8504 loss: 1.8624 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.8624 2023/04/13 23:40:20 - mmengine - INFO - Epoch(train) [18][1460/1879] lr: 2.0000e-02 eta: 15:52:58 time: 0.3919 data_time: 0.0144 memory: 6717 grad_norm: 2.8506 loss: 1.8951 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8951 2023/04/13 23:40:27 - mmengine - INFO - Epoch(train) [18][1480/1879] lr: 2.0000e-02 eta: 15:52:50 time: 0.3602 data_time: 0.0124 memory: 6717 grad_norm: 2.8169 loss: 1.8124 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8124 2023/04/13 23:40:35 - mmengine - INFO - Epoch(train) [18][1500/1879] lr: 2.0000e-02 eta: 15:52:47 time: 0.4218 data_time: 0.0131 memory: 6717 grad_norm: 2.7627 loss: 1.7691 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7691 2023/04/13 23:40:42 - mmengine - INFO - Epoch(train) [18][1520/1879] lr: 2.0000e-02 eta: 15:52:35 time: 0.3125 data_time: 0.0142 memory: 6717 grad_norm: 2.8037 loss: 1.8453 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8453 2023/04/13 23:40:49 - mmengine - INFO - Epoch(train) [18][1540/1879] lr: 2.0000e-02 eta: 15:52:28 time: 0.3807 data_time: 0.0135 memory: 6717 grad_norm: 2.8573 loss: 1.6385 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6385 2023/04/13 23:40:56 - mmengine - INFO - Epoch(train) [18][1560/1879] lr: 2.0000e-02 eta: 15:52:18 time: 0.3357 data_time: 0.0137 memory: 6717 grad_norm: 2.8202 loss: 1.8617 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8617 2023/04/13 23:41:04 - mmengine - INFO - Epoch(train) [18][1580/1879] lr: 2.0000e-02 eta: 15:52:14 time: 0.4118 data_time: 0.0136 memory: 6717 grad_norm: 2.7741 loss: 1.8727 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.8727 2023/04/13 23:41:10 - mmengine - INFO - Epoch(train) [18][1600/1879] lr: 2.0000e-02 eta: 15:52:00 time: 0.3023 data_time: 0.0129 memory: 6717 grad_norm: 2.8198 loss: 1.6866 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6866 2023/04/13 23:41:19 - mmengine - INFO - Epoch(train) [18][1620/1879] lr: 2.0000e-02 eta: 15:51:59 time: 0.4407 data_time: 0.0278 memory: 6717 grad_norm: 2.8664 loss: 1.8195 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8195 2023/04/13 23:41:26 - mmengine - INFO - Epoch(train) [18][1640/1879] lr: 2.0000e-02 eta: 15:51:48 time: 0.3227 data_time: 0.0399 memory: 6717 grad_norm: 2.8382 loss: 1.7967 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7967 2023/04/13 23:41:33 - mmengine - INFO - Epoch(train) [18][1660/1879] lr: 2.0000e-02 eta: 15:51:43 time: 0.3945 data_time: 0.0423 memory: 6717 grad_norm: 2.8889 loss: 1.9037 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9037 2023/04/13 23:41:40 - mmengine - INFO - Epoch(train) [18][1680/1879] lr: 2.0000e-02 eta: 15:51:31 time: 0.3287 data_time: 0.0592 memory: 6717 grad_norm: 2.8674 loss: 1.9175 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9175 2023/04/13 23:41:48 - mmengine - INFO - Epoch(train) [18][1700/1879] lr: 2.0000e-02 eta: 15:51:28 time: 0.4154 data_time: 0.0130 memory: 6717 grad_norm: 2.7453 loss: 1.9317 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.9317 2023/04/13 23:41:55 - mmengine - INFO - Epoch(train) [18][1720/1879] lr: 2.0000e-02 eta: 15:51:16 time: 0.3210 data_time: 0.0141 memory: 6717 grad_norm: 2.8219 loss: 1.8044 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8044 2023/04/13 23:42:03 - mmengine - INFO - Epoch(train) [18][1740/1879] lr: 2.0000e-02 eta: 15:51:15 time: 0.4350 data_time: 0.0156 memory: 6717 grad_norm: 2.8688 loss: 1.8051 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8051 2023/04/13 23:42:10 - mmengine - INFO - Epoch(train) [18][1760/1879] lr: 2.0000e-02 eta: 15:51:02 time: 0.3167 data_time: 0.0213 memory: 6717 grad_norm: 2.9284 loss: 2.0089 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 2.0089 2023/04/13 23:42:17 - mmengine - INFO - Epoch(train) [18][1780/1879] lr: 2.0000e-02 eta: 15:50:55 time: 0.3641 data_time: 0.0144 memory: 6717 grad_norm: 2.8118 loss: 1.9039 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9039 2023/04/13 23:42:23 - mmengine - INFO - Epoch(train) [18][1800/1879] lr: 2.0000e-02 eta: 15:50:42 time: 0.3104 data_time: 0.0469 memory: 6717 grad_norm: 2.8724 loss: 1.9408 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9408 2023/04/13 23:42:31 - mmengine - INFO - Epoch(train) [18][1820/1879] lr: 2.0000e-02 eta: 15:50:37 time: 0.4003 data_time: 0.1641 memory: 6717 grad_norm: 2.8024 loss: 1.8296 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8296 2023/04/13 23:42:39 - mmengine - INFO - Epoch(train) [18][1840/1879] lr: 2.0000e-02 eta: 15:50:30 time: 0.3723 data_time: 0.0927 memory: 6717 grad_norm: 2.8224 loss: 1.8775 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.8775 2023/04/13 23:42:46 - mmengine - INFO - Epoch(train) [18][1860/1879] lr: 2.0000e-02 eta: 15:50:22 time: 0.3692 data_time: 0.0410 memory: 6717 grad_norm: 2.8355 loss: 1.9471 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9471 2023/04/13 23:42:53 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 23:42:53 - mmengine - INFO - Epoch(train) [18][1879/1879] lr: 2.0000e-02 eta: 15:50:15 time: 0.3902 data_time: 0.0113 memory: 6717 grad_norm: 2.8729 loss: 1.8148 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.8148 2023/04/13 23:42:53 - mmengine - INFO - Saving checkpoint at 18 epochs 2023/04/13 23:43:03 - mmengine - INFO - Epoch(val) [18][ 20/155] eta: 0:01:02 time: 0.4630 data_time: 0.4297 memory: 1391 2023/04/13 23:43:09 - mmengine - INFO - Epoch(val) [18][ 40/155] eta: 0:00:44 time: 0.3152 data_time: 0.2820 memory: 1391 2023/04/13 23:43:18 - mmengine - INFO - Epoch(val) [18][ 60/155] eta: 0:00:38 time: 0.4282 data_time: 0.3951 memory: 1391 2023/04/13 23:43:24 - mmengine - INFO - Epoch(val) [18][ 80/155] eta: 0:00:28 time: 0.3175 data_time: 0.2847 memory: 1391 2023/04/13 23:43:33 - mmengine - INFO - Epoch(val) [18][100/155] eta: 0:00:21 time: 0.4544 data_time: 0.4217 memory: 1391 2023/04/13 23:43:39 - mmengine - INFO - Epoch(val) [18][120/155] eta: 0:00:13 time: 0.2984 data_time: 0.2654 memory: 1391 2023/04/13 23:43:48 - mmengine - INFO - Epoch(val) [18][140/155] eta: 0:00:05 time: 0.4445 data_time: 0.4113 memory: 1391 2023/04/13 23:43:55 - mmengine - INFO - Epoch(val) [18][155/155] acc/top1: 0.5757 acc/top5: 0.8192 acc/mean1: 0.5757 data_time: 0.3619 time: 0.3942 2023/04/13 23:43:55 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_17.pth is removed 2023/04/13 23:43:55 - mmengine - INFO - The best checkpoint with 0.5757 acc/top1 at 18 epoch is saved to best_acc_top1_epoch_18.pth. 2023/04/13 23:44:05 - mmengine - INFO - Epoch(train) [19][ 20/1879] lr: 2.0000e-02 eta: 15:50:19 time: 0.4977 data_time: 0.3003 memory: 6717 grad_norm: 2.8234 loss: 1.8299 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8299 2023/04/13 23:44:12 - mmengine - INFO - Epoch(train) [19][ 40/1879] lr: 2.0000e-02 eta: 15:50:09 time: 0.3386 data_time: 0.1374 memory: 6717 grad_norm: 2.8205 loss: 1.7336 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7336 2023/04/13 23:44:20 - mmengine - INFO - Epoch(train) [19][ 60/1879] lr: 2.0000e-02 eta: 15:50:05 time: 0.4091 data_time: 0.2528 memory: 6717 grad_norm: 2.7639 loss: 1.8560 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8560 2023/04/13 23:44:26 - mmengine - INFO - Epoch(train) [19][ 80/1879] lr: 2.0000e-02 eta: 15:49:53 time: 0.3163 data_time: 0.1371 memory: 6717 grad_norm: 2.7156 loss: 1.9561 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 1.9561 2023/04/13 23:44:35 - mmengine - INFO - Epoch(train) [19][ 100/1879] lr: 2.0000e-02 eta: 15:49:52 time: 0.4368 data_time: 0.1387 memory: 6717 grad_norm: 2.9478 loss: 1.6935 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6935 2023/04/13 23:44:41 - mmengine - INFO - Epoch(train) [19][ 120/1879] lr: 2.0000e-02 eta: 15:49:39 time: 0.3087 data_time: 0.0470 memory: 6717 grad_norm: 2.7987 loss: 1.9834 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.9834 2023/04/13 23:44:50 - mmengine - INFO - Epoch(train) [19][ 140/1879] lr: 2.0000e-02 eta: 15:49:36 time: 0.4271 data_time: 0.1598 memory: 6717 grad_norm: 2.7705 loss: 1.7133 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7133 2023/04/13 23:44:57 - mmengine - INFO - Epoch(train) [19][ 160/1879] lr: 2.0000e-02 eta: 15:49:27 time: 0.3434 data_time: 0.1239 memory: 6717 grad_norm: 2.8169 loss: 1.6157 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6157 2023/04/13 23:45:05 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 23:45:05 - mmengine - INFO - Epoch(train) [19][ 180/1879] lr: 2.0000e-02 eta: 15:49:25 time: 0.4301 data_time: 0.0167 memory: 6717 grad_norm: 2.9336 loss: 1.9202 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9202 2023/04/13 23:45:12 - mmengine - INFO - Epoch(train) [19][ 200/1879] lr: 2.0000e-02 eta: 15:49:13 time: 0.3241 data_time: 0.0129 memory: 6717 grad_norm: 2.8662 loss: 2.0261 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0261 2023/04/13 23:45:20 - mmengine - INFO - Epoch(train) [19][ 220/1879] lr: 2.0000e-02 eta: 15:49:09 time: 0.4112 data_time: 0.0141 memory: 6717 grad_norm: 2.8582 loss: 1.6878 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6878 2023/04/13 23:45:26 - mmengine - INFO - Epoch(train) [19][ 240/1879] lr: 2.0000e-02 eta: 15:48:55 time: 0.2952 data_time: 0.0248 memory: 6717 grad_norm: 2.7488 loss: 1.7815 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7815 2023/04/13 23:45:34 - mmengine - INFO - Epoch(train) [19][ 260/1879] lr: 2.0000e-02 eta: 15:48:52 time: 0.4155 data_time: 0.0474 memory: 6717 grad_norm: 2.8787 loss: 1.8998 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8998 2023/04/13 23:45:40 - mmengine - INFO - Epoch(train) [19][ 280/1879] lr: 2.0000e-02 eta: 15:48:39 time: 0.3128 data_time: 0.0154 memory: 6717 grad_norm: 2.7540 loss: 1.6168 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6168 2023/04/13 23:45:49 - mmengine - INFO - Epoch(train) [19][ 300/1879] lr: 2.0000e-02 eta: 15:48:36 time: 0.4141 data_time: 0.0942 memory: 6717 grad_norm: 2.8184 loss: 1.8383 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8383 2023/04/13 23:45:55 - mmengine - INFO - Epoch(train) [19][ 320/1879] lr: 2.0000e-02 eta: 15:48:24 time: 0.3198 data_time: 0.1145 memory: 6717 grad_norm: 2.8337 loss: 1.8323 top1_acc: 0.1875 top5_acc: 0.6875 loss_cls: 1.8323 2023/04/13 23:46:04 - mmengine - INFO - Epoch(train) [19][ 340/1879] lr: 2.0000e-02 eta: 15:48:24 time: 0.4486 data_time: 0.2492 memory: 6717 grad_norm: 2.8750 loss: 1.9468 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9468 2023/04/13 23:46:10 - mmengine - INFO - Epoch(train) [19][ 360/1879] lr: 2.0000e-02 eta: 15:48:10 time: 0.2976 data_time: 0.1558 memory: 6717 grad_norm: 2.9032 loss: 1.7486 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7486 2023/04/13 23:46:19 - mmengine - INFO - Epoch(train) [19][ 380/1879] lr: 2.0000e-02 eta: 15:48:08 time: 0.4379 data_time: 0.2941 memory: 6717 grad_norm: 2.8998 loss: 1.6292 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.6292 2023/04/13 23:46:25 - mmengine - INFO - Epoch(train) [19][ 400/1879] lr: 2.0000e-02 eta: 15:47:56 time: 0.3212 data_time: 0.1788 memory: 6717 grad_norm: 2.7754 loss: 1.7468 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7468 2023/04/13 23:46:33 - mmengine - INFO - Epoch(train) [19][ 420/1879] lr: 2.0000e-02 eta: 15:47:50 time: 0.3795 data_time: 0.2395 memory: 6717 grad_norm: 2.7877 loss: 2.1105 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1105 2023/04/13 23:46:39 - mmengine - INFO - Epoch(train) [19][ 440/1879] lr: 2.0000e-02 eta: 15:47:39 time: 0.3280 data_time: 0.1812 memory: 6717 grad_norm: 2.8814 loss: 2.2295 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.2295 2023/04/13 23:46:47 - mmengine - INFO - Epoch(train) [19][ 460/1879] lr: 2.0000e-02 eta: 15:47:33 time: 0.3909 data_time: 0.2073 memory: 6717 grad_norm: 2.7917 loss: 1.9492 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.9492 2023/04/13 23:46:54 - mmengine - INFO - Epoch(train) [19][ 480/1879] lr: 2.0000e-02 eta: 15:47:24 time: 0.3543 data_time: 0.1754 memory: 6717 grad_norm: 2.8242 loss: 2.0183 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0183 2023/04/13 23:47:02 - mmengine - INFO - Epoch(train) [19][ 500/1879] lr: 2.0000e-02 eta: 15:47:19 time: 0.3967 data_time: 0.2379 memory: 6717 grad_norm: 2.8170 loss: 1.8810 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.8810 2023/04/13 23:47:09 - mmengine - INFO - Epoch(train) [19][ 520/1879] lr: 2.0000e-02 eta: 15:47:08 time: 0.3302 data_time: 0.1758 memory: 6717 grad_norm: 2.9742 loss: 2.0001 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.0001 2023/04/13 23:47:17 - mmengine - INFO - Epoch(train) [19][ 540/1879] lr: 2.0000e-02 eta: 15:47:05 time: 0.4123 data_time: 0.2757 memory: 6717 grad_norm: 2.8403 loss: 1.8616 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.8616 2023/04/13 23:47:23 - mmengine - INFO - Epoch(train) [19][ 560/1879] lr: 2.0000e-02 eta: 15:46:52 time: 0.3097 data_time: 0.1549 memory: 6717 grad_norm: 2.7692 loss: 1.9605 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.9605 2023/04/13 23:47:31 - mmengine - INFO - Epoch(train) [19][ 580/1879] lr: 2.0000e-02 eta: 15:46:45 time: 0.3787 data_time: 0.1549 memory: 6717 grad_norm: 2.8764 loss: 1.8677 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8677 2023/04/13 23:47:38 - mmengine - INFO - Epoch(train) [19][ 600/1879] lr: 2.0000e-02 eta: 15:46:36 time: 0.3464 data_time: 0.1448 memory: 6717 grad_norm: 2.7535 loss: 1.7566 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7566 2023/04/13 23:47:46 - mmengine - INFO - Epoch(train) [19][ 620/1879] lr: 2.0000e-02 eta: 15:46:31 time: 0.3967 data_time: 0.2443 memory: 6717 grad_norm: 2.7884 loss: 1.7475 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7475 2023/04/13 23:47:52 - mmengine - INFO - Epoch(train) [19][ 640/1879] lr: 2.0000e-02 eta: 15:46:19 time: 0.3184 data_time: 0.1049 memory: 6717 grad_norm: 2.8423 loss: 1.8241 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8241 2023/04/13 23:47:59 - mmengine - INFO - Epoch(train) [19][ 660/1879] lr: 2.0000e-02 eta: 15:46:10 time: 0.3592 data_time: 0.0261 memory: 6717 grad_norm: 2.8066 loss: 1.7681 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7681 2023/04/13 23:48:07 - mmengine - INFO - Epoch(train) [19][ 680/1879] lr: 2.0000e-02 eta: 15:46:05 time: 0.3877 data_time: 0.0121 memory: 6717 grad_norm: 2.8784 loss: 1.8802 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8802 2023/04/13 23:48:14 - mmengine - INFO - Epoch(train) [19][ 700/1879] lr: 2.0000e-02 eta: 15:45:56 time: 0.3513 data_time: 0.0149 memory: 6717 grad_norm: 2.8010 loss: 1.8087 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8087 2023/04/13 23:48:22 - mmengine - INFO - Epoch(train) [19][ 720/1879] lr: 2.0000e-02 eta: 15:45:52 time: 0.4101 data_time: 0.0125 memory: 6717 grad_norm: 2.9157 loss: 1.6794 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6794 2023/04/13 23:48:29 - mmengine - INFO - Epoch(train) [19][ 740/1879] lr: 2.0000e-02 eta: 15:45:41 time: 0.3347 data_time: 0.0136 memory: 6717 grad_norm: 2.8456 loss: 1.9053 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9053 2023/04/13 23:48:37 - mmengine - INFO - Epoch(train) [19][ 760/1879] lr: 2.0000e-02 eta: 15:45:38 time: 0.4160 data_time: 0.0131 memory: 6717 grad_norm: 2.8326 loss: 1.9714 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9714 2023/04/13 23:48:48 - mmengine - INFO - Epoch(train) [19][ 780/1879] lr: 2.0000e-02 eta: 15:45:44 time: 0.5219 data_time: 0.0148 memory: 6717 grad_norm: 2.8498 loss: 1.8076 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8076 2023/04/13 23:48:54 - mmengine - INFO - Epoch(train) [19][ 800/1879] lr: 2.0000e-02 eta: 15:45:33 time: 0.3354 data_time: 0.0143 memory: 6717 grad_norm: 2.8234 loss: 1.8431 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8431 2023/04/13 23:49:04 - mmengine - INFO - Epoch(train) [19][ 820/1879] lr: 2.0000e-02 eta: 15:45:35 time: 0.4684 data_time: 0.0148 memory: 6717 grad_norm: 2.7546 loss: 1.8731 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8731 2023/04/13 23:49:10 - mmengine - INFO - Epoch(train) [19][ 840/1879] lr: 2.0000e-02 eta: 15:45:24 time: 0.3304 data_time: 0.0142 memory: 6717 grad_norm: 2.8217 loss: 1.7244 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.7244 2023/04/13 23:49:18 - mmengine - INFO - Epoch(train) [19][ 860/1879] lr: 2.0000e-02 eta: 15:45:19 time: 0.4046 data_time: 0.0153 memory: 6717 grad_norm: 2.8428 loss: 1.8165 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8165 2023/04/13 23:49:25 - mmengine - INFO - Epoch(train) [19][ 880/1879] lr: 2.0000e-02 eta: 15:45:09 time: 0.3317 data_time: 0.0149 memory: 6717 grad_norm: 2.7814 loss: 1.7983 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7983 2023/04/13 23:49:33 - mmengine - INFO - Epoch(train) [19][ 900/1879] lr: 2.0000e-02 eta: 15:45:05 time: 0.4144 data_time: 0.0134 memory: 6717 grad_norm: 2.8902 loss: 1.8041 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8041 2023/04/13 23:49:40 - mmengine - INFO - Epoch(train) [19][ 920/1879] lr: 2.0000e-02 eta: 15:44:55 time: 0.3357 data_time: 0.0137 memory: 6717 grad_norm: 2.8188 loss: 1.9160 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9160 2023/04/13 23:49:48 - mmengine - INFO - Epoch(train) [19][ 940/1879] lr: 2.0000e-02 eta: 15:44:52 time: 0.4199 data_time: 0.0140 memory: 6717 grad_norm: 2.7671 loss: 1.7749 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7749 2023/04/13 23:49:54 - mmengine - INFO - Epoch(train) [19][ 960/1879] lr: 2.0000e-02 eta: 15:44:38 time: 0.3015 data_time: 0.0135 memory: 6717 grad_norm: 2.8400 loss: 1.9581 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9581 2023/04/13 23:50:03 - mmengine - INFO - Epoch(train) [19][ 980/1879] lr: 2.0000e-02 eta: 15:44:35 time: 0.4173 data_time: 0.0142 memory: 6717 grad_norm: 2.7666 loss: 1.4164 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4164 2023/04/13 23:50:09 - mmengine - INFO - Epoch(train) [19][1000/1879] lr: 2.0000e-02 eta: 15:44:21 time: 0.2960 data_time: 0.0139 memory: 6717 grad_norm: 2.8344 loss: 1.7537 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7537 2023/04/13 23:50:17 - mmengine - INFO - Epoch(train) [19][1020/1879] lr: 2.0000e-02 eta: 15:44:17 time: 0.4093 data_time: 0.0186 memory: 6717 grad_norm: 2.8836 loss: 1.8928 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.8928 2023/04/13 23:50:23 - mmengine - INFO - Epoch(train) [19][1040/1879] lr: 2.0000e-02 eta: 15:44:05 time: 0.3122 data_time: 0.0315 memory: 6717 grad_norm: 2.8143 loss: 1.8204 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8204 2023/04/13 23:50:32 - mmengine - INFO - Epoch(train) [19][1060/1879] lr: 2.0000e-02 eta: 15:44:05 time: 0.4619 data_time: 0.0288 memory: 6717 grad_norm: 2.7912 loss: 1.9469 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.9469 2023/04/13 23:50:40 - mmengine - INFO - Epoch(train) [19][1080/1879] lr: 2.0000e-02 eta: 15:43:57 time: 0.3557 data_time: 0.0115 memory: 6717 grad_norm: 2.8965 loss: 2.1087 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.1087 2023/04/13 23:50:48 - mmengine - INFO - Epoch(train) [19][1100/1879] lr: 2.0000e-02 eta: 15:43:52 time: 0.3987 data_time: 0.0128 memory: 6717 grad_norm: 2.7590 loss: 1.7699 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7699 2023/04/13 23:50:54 - mmengine - INFO - Epoch(train) [19][1120/1879] lr: 2.0000e-02 eta: 15:43:39 time: 0.3090 data_time: 0.0149 memory: 6717 grad_norm: 2.8137 loss: 1.8351 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8351 2023/04/13 23:51:02 - mmengine - INFO - Epoch(train) [19][1140/1879] lr: 2.0000e-02 eta: 15:43:35 time: 0.4147 data_time: 0.0131 memory: 6717 grad_norm: 2.9047 loss: 1.8044 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8044 2023/04/13 23:51:08 - mmengine - INFO - Epoch(train) [19][1160/1879] lr: 2.0000e-02 eta: 15:43:22 time: 0.3059 data_time: 0.0143 memory: 6717 grad_norm: 2.8812 loss: 1.6578 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.6578 2023/04/13 23:51:14 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 23:51:16 - mmengine - INFO - Epoch(train) [19][1180/1879] lr: 2.0000e-02 eta: 15:43:17 time: 0.3956 data_time: 0.0137 memory: 6717 grad_norm: 2.7253 loss: 1.6662 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6662 2023/04/13 23:51:23 - mmengine - INFO - Epoch(train) [19][1200/1879] lr: 2.0000e-02 eta: 15:43:07 time: 0.3360 data_time: 0.0144 memory: 6717 grad_norm: 2.8253 loss: 1.7832 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7832 2023/04/13 23:51:31 - mmengine - INFO - Epoch(train) [19][1220/1879] lr: 2.0000e-02 eta: 15:43:02 time: 0.3984 data_time: 0.0135 memory: 6717 grad_norm: 2.8073 loss: 1.9910 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9910 2023/04/13 23:51:38 - mmengine - INFO - Epoch(train) [19][1240/1879] lr: 2.0000e-02 eta: 15:42:53 time: 0.3557 data_time: 0.0153 memory: 6717 grad_norm: 2.8394 loss: 1.9989 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9989 2023/04/13 23:51:46 - mmengine - INFO - Epoch(train) [19][1260/1879] lr: 2.0000e-02 eta: 15:42:48 time: 0.3949 data_time: 0.0130 memory: 6717 grad_norm: 2.8054 loss: 1.8240 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8240 2023/04/13 23:51:53 - mmengine - INFO - Epoch(train) [19][1280/1879] lr: 2.0000e-02 eta: 15:42:39 time: 0.3482 data_time: 0.0133 memory: 6717 grad_norm: 2.8167 loss: 2.0886 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.0886 2023/04/13 23:52:01 - mmengine - INFO - Epoch(train) [19][1300/1879] lr: 2.0000e-02 eta: 15:42:34 time: 0.4061 data_time: 0.0149 memory: 6717 grad_norm: 2.7790 loss: 1.6611 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6611 2023/04/13 23:52:08 - mmengine - INFO - Epoch(train) [19][1320/1879] lr: 2.0000e-02 eta: 15:42:24 time: 0.3396 data_time: 0.0145 memory: 6717 grad_norm: 2.8897 loss: 1.7890 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7890 2023/04/13 23:52:16 - mmengine - INFO - Epoch(train) [19][1340/1879] lr: 2.0000e-02 eta: 15:42:20 time: 0.4069 data_time: 0.0135 memory: 6717 grad_norm: 2.8528 loss: 1.6675 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6675 2023/04/13 23:52:23 - mmengine - INFO - Epoch(train) [19][1360/1879] lr: 2.0000e-02 eta: 15:42:10 time: 0.3408 data_time: 0.0153 memory: 6717 grad_norm: 2.8486 loss: 1.7297 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7297 2023/04/13 23:52:30 - mmengine - INFO - Epoch(train) [19][1380/1879] lr: 2.0000e-02 eta: 15:42:04 time: 0.3848 data_time: 0.0127 memory: 6717 grad_norm: 2.7624 loss: 1.6811 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6811 2023/04/13 23:52:37 - mmengine - INFO - Epoch(train) [19][1400/1879] lr: 2.0000e-02 eta: 15:41:53 time: 0.3332 data_time: 0.0157 memory: 6717 grad_norm: 2.8030 loss: 1.8225 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8225 2023/04/13 23:52:45 - mmengine - INFO - Epoch(train) [19][1420/1879] lr: 2.0000e-02 eta: 15:41:51 time: 0.4225 data_time: 0.0125 memory: 6717 grad_norm: 2.8323 loss: 2.1493 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 2.1493 2023/04/13 23:52:52 - mmengine - INFO - Epoch(train) [19][1440/1879] lr: 2.0000e-02 eta: 15:41:39 time: 0.3241 data_time: 0.0143 memory: 6717 grad_norm: 2.8188 loss: 1.9439 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9439 2023/04/13 23:53:00 - mmengine - INFO - Epoch(train) [19][1460/1879] lr: 2.0000e-02 eta: 15:41:35 time: 0.4041 data_time: 0.0134 memory: 6717 grad_norm: 2.8402 loss: 2.0591 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0591 2023/04/13 23:53:06 - mmengine - INFO - Epoch(train) [19][1480/1879] lr: 2.0000e-02 eta: 15:41:22 time: 0.3153 data_time: 0.0136 memory: 6717 grad_norm: 2.8275 loss: 1.9361 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9361 2023/04/13 23:53:14 - mmengine - INFO - Epoch(train) [19][1500/1879] lr: 2.0000e-02 eta: 15:41:15 time: 0.3633 data_time: 0.0143 memory: 6717 grad_norm: 2.7688 loss: 1.8959 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8959 2023/04/13 23:53:19 - mmengine - INFO - Epoch(train) [19][1520/1879] lr: 2.0000e-02 eta: 15:41:01 time: 0.2995 data_time: 0.0152 memory: 6717 grad_norm: 2.8010 loss: 1.7924 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7924 2023/04/13 23:53:27 - mmengine - INFO - Epoch(train) [19][1540/1879] lr: 2.0000e-02 eta: 15:40:56 time: 0.3945 data_time: 0.0140 memory: 6717 grad_norm: 2.8512 loss: 1.8126 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8126 2023/04/13 23:53:34 - mmengine - INFO - Epoch(train) [19][1560/1879] lr: 2.0000e-02 eta: 15:40:46 time: 0.3489 data_time: 0.0139 memory: 6717 grad_norm: 2.8533 loss: 1.8808 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8808 2023/04/13 23:53:42 - mmengine - INFO - Epoch(train) [19][1580/1879] lr: 2.0000e-02 eta: 15:40:40 time: 0.3804 data_time: 0.0161 memory: 6717 grad_norm: 2.7905 loss: 1.7644 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7644 2023/04/13 23:53:49 - mmengine - INFO - Epoch(train) [19][1600/1879] lr: 2.0000e-02 eta: 15:40:31 time: 0.3519 data_time: 0.0296 memory: 6717 grad_norm: 2.8227 loss: 1.7240 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7240 2023/04/13 23:53:58 - mmengine - INFO - Epoch(train) [19][1620/1879] lr: 2.0000e-02 eta: 15:40:28 time: 0.4259 data_time: 0.0133 memory: 6717 grad_norm: 2.8288 loss: 1.8588 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8588 2023/04/13 23:54:04 - mmengine - INFO - Epoch(train) [19][1640/1879] lr: 2.0000e-02 eta: 15:40:17 time: 0.3240 data_time: 0.0138 memory: 6717 grad_norm: 2.7419 loss: 1.8778 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8778 2023/04/13 23:54:12 - mmengine - INFO - Epoch(train) [19][1660/1879] lr: 2.0000e-02 eta: 15:40:13 time: 0.4066 data_time: 0.0139 memory: 6717 grad_norm: 2.7956 loss: 1.9301 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9301 2023/04/13 23:54:18 - mmengine - INFO - Epoch(train) [19][1680/1879] lr: 2.0000e-02 eta: 15:40:01 time: 0.3159 data_time: 0.0144 memory: 6717 grad_norm: 2.8031 loss: 1.9415 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9415 2023/04/13 23:54:27 - mmengine - INFO - Epoch(train) [19][1700/1879] lr: 2.0000e-02 eta: 15:39:56 time: 0.4058 data_time: 0.0127 memory: 6717 grad_norm: 2.8900 loss: 1.8024 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8024 2023/04/13 23:54:33 - mmengine - INFO - Epoch(train) [19][1720/1879] lr: 2.0000e-02 eta: 15:39:47 time: 0.3439 data_time: 0.0146 memory: 6717 grad_norm: 2.8910 loss: 1.7542 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.7542 2023/04/13 23:54:42 - mmengine - INFO - Epoch(train) [19][1740/1879] lr: 2.0000e-02 eta: 15:39:44 time: 0.4203 data_time: 0.0158 memory: 6717 grad_norm: 2.8424 loss: 2.0138 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0138 2023/04/13 23:54:48 - mmengine - INFO - Epoch(train) [19][1760/1879] lr: 2.0000e-02 eta: 15:39:31 time: 0.3097 data_time: 0.0149 memory: 6717 grad_norm: 2.7396 loss: 1.8648 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8648 2023/04/13 23:54:56 - mmengine - INFO - Epoch(train) [19][1780/1879] lr: 2.0000e-02 eta: 15:39:26 time: 0.3999 data_time: 0.0118 memory: 6717 grad_norm: 2.8043 loss: 1.8262 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8262 2023/04/13 23:55:03 - mmengine - INFO - Epoch(train) [19][1800/1879] lr: 2.0000e-02 eta: 15:39:15 time: 0.3284 data_time: 0.0144 memory: 6717 grad_norm: 2.7716 loss: 1.9460 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9460 2023/04/13 23:55:11 - mmengine - INFO - Epoch(train) [19][1820/1879] lr: 2.0000e-02 eta: 15:39:11 time: 0.4131 data_time: 0.0137 memory: 6717 grad_norm: 2.7935 loss: 1.7795 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7795 2023/04/13 23:55:18 - mmengine - INFO - Epoch(train) [19][1840/1879] lr: 2.0000e-02 eta: 15:39:03 time: 0.3518 data_time: 0.0141 memory: 6717 grad_norm: 2.8249 loss: 1.9087 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9087 2023/04/13 23:55:25 - mmengine - INFO - Epoch(train) [19][1860/1879] lr: 2.0000e-02 eta: 15:38:54 time: 0.3612 data_time: 0.0142 memory: 6717 grad_norm: 2.6891 loss: 1.7176 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7176 2023/04/13 23:55:31 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 23:55:31 - mmengine - INFO - Epoch(train) [19][1879/1879] lr: 2.0000e-02 eta: 15:38:44 time: 0.3432 data_time: 0.0125 memory: 6717 grad_norm: 2.9105 loss: 1.7788 top1_acc: 0.0000 top5_acc: 0.4286 loss_cls: 1.7788 2023/04/13 23:55:42 - mmengine - INFO - Epoch(val) [19][ 20/155] eta: 0:01:13 time: 0.5420 data_time: 0.5085 memory: 1391 2023/04/13 23:55:51 - mmengine - INFO - Epoch(val) [19][ 40/155] eta: 0:00:54 time: 0.4126 data_time: 0.3795 memory: 1391 2023/04/13 23:55:56 - mmengine - INFO - Epoch(val) [19][ 60/155] eta: 0:00:39 time: 0.2939 data_time: 0.2618 memory: 1391 2023/04/13 23:56:04 - mmengine - INFO - Epoch(val) [19][ 80/155] eta: 0:00:30 time: 0.3878 data_time: 0.3553 memory: 1391 2023/04/13 23:56:11 - mmengine - INFO - Epoch(val) [19][100/155] eta: 0:00:21 time: 0.3259 data_time: 0.2933 memory: 1391 2023/04/13 23:56:19 - mmengine - INFO - Epoch(val) [19][120/155] eta: 0:00:13 time: 0.4025 data_time: 0.3697 memory: 1391 2023/04/13 23:56:26 - mmengine - INFO - Epoch(val) [19][140/155] eta: 0:00:05 time: 0.3359 data_time: 0.3033 memory: 1391 2023/04/13 23:56:37 - mmengine - INFO - Epoch(val) [19][155/155] acc/top1: 0.5848 acc/top5: 0.8206 acc/mean1: 0.5847 data_time: 0.2679 time: 0.3006 2023/04/13 23:56:37 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_18.pth is removed 2023/04/13 23:56:38 - mmengine - INFO - The best checkpoint with 0.5848 acc/top1 at 19 epoch is saved to best_acc_top1_epoch_19.pth. 2023/04/13 23:56:47 - mmengine - INFO - Epoch(train) [20][ 20/1879] lr: 2.0000e-02 eta: 15:38:45 time: 0.4633 data_time: 0.3226 memory: 6717 grad_norm: 2.7835 loss: 1.6777 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.6777 2023/04/13 23:56:54 - mmengine - INFO - Epoch(train) [20][ 40/1879] lr: 2.0000e-02 eta: 15:38:34 time: 0.3279 data_time: 0.1324 memory: 6717 grad_norm: 2.8704 loss: 1.7903 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7903 2023/04/13 23:57:02 - mmengine - INFO - Epoch(train) [20][ 60/1879] lr: 2.0000e-02 eta: 15:38:30 time: 0.4133 data_time: 0.0385 memory: 6717 grad_norm: 2.8482 loss: 1.7599 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7599 2023/04/13 23:57:08 - mmengine - INFO - Epoch(train) [20][ 80/1879] lr: 2.0000e-02 eta: 15:38:18 time: 0.3138 data_time: 0.0224 memory: 6717 grad_norm: 2.8553 loss: 1.8350 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8350 2023/04/13 23:57:17 - mmengine - INFO - Epoch(train) [20][ 100/1879] lr: 2.0000e-02 eta: 15:38:16 time: 0.4316 data_time: 0.0156 memory: 6717 grad_norm: 2.7990 loss: 1.8898 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8898 2023/04/13 23:57:24 - mmengine - INFO - Epoch(train) [20][ 120/1879] lr: 2.0000e-02 eta: 15:38:06 time: 0.3403 data_time: 0.0130 memory: 6717 grad_norm: 2.8151 loss: 1.6167 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6167 2023/04/13 23:57:31 - mmengine - INFO - Epoch(train) [20][ 140/1879] lr: 2.0000e-02 eta: 15:38:00 time: 0.3879 data_time: 0.0160 memory: 6717 grad_norm: 2.8397 loss: 1.9648 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9648 2023/04/13 23:57:38 - mmengine - INFO - Epoch(train) [20][ 160/1879] lr: 2.0000e-02 eta: 15:37:51 time: 0.3474 data_time: 0.0126 memory: 6717 grad_norm: 2.7726 loss: 1.7231 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7231 2023/04/13 23:57:46 - mmengine - INFO - Epoch(train) [20][ 180/1879] lr: 2.0000e-02 eta: 15:37:46 time: 0.3984 data_time: 0.0166 memory: 6717 grad_norm: 2.8077 loss: 1.7762 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7762 2023/04/13 23:57:52 - mmengine - INFO - Epoch(train) [20][ 200/1879] lr: 2.0000e-02 eta: 15:37:33 time: 0.3065 data_time: 0.0155 memory: 6717 grad_norm: 2.8610 loss: 1.8763 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8763 2023/04/13 23:58:01 - mmengine - INFO - Epoch(train) [20][ 220/1879] lr: 2.0000e-02 eta: 15:37:30 time: 0.4291 data_time: 0.0158 memory: 6717 grad_norm: 2.8574 loss: 1.8896 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.8896 2023/04/13 23:58:08 - mmengine - INFO - Epoch(train) [20][ 240/1879] lr: 2.0000e-02 eta: 15:37:21 time: 0.3464 data_time: 0.0122 memory: 6717 grad_norm: 2.7769 loss: 1.7492 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7492 2023/04/13 23:58:16 - mmengine - INFO - Epoch(train) [20][ 260/1879] lr: 2.0000e-02 eta: 15:37:16 time: 0.4005 data_time: 0.0146 memory: 6717 grad_norm: 2.8848 loss: 1.9893 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9893 2023/04/13 23:58:23 - mmengine - INFO - Epoch(train) [20][ 280/1879] lr: 2.0000e-02 eta: 15:37:07 time: 0.3524 data_time: 0.0132 memory: 6717 grad_norm: 2.7926 loss: 1.6018 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6018 2023/04/13 23:58:31 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/13 23:58:31 - mmengine - INFO - Epoch(train) [20][ 300/1879] lr: 2.0000e-02 eta: 15:37:01 time: 0.3844 data_time: 0.0166 memory: 6717 grad_norm: 2.9157 loss: 1.8125 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8125 2023/04/13 23:58:38 - mmengine - INFO - Epoch(train) [20][ 320/1879] lr: 2.0000e-02 eta: 15:36:54 time: 0.3770 data_time: 0.0139 memory: 6717 grad_norm: 2.7412 loss: 1.7794 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7794 2023/04/13 23:58:46 - mmengine - INFO - Epoch(train) [20][ 340/1879] lr: 2.0000e-02 eta: 15:36:47 time: 0.3701 data_time: 0.0141 memory: 6717 grad_norm: 2.8475 loss: 1.6888 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6888 2023/04/13 23:58:52 - mmengine - INFO - Epoch(train) [20][ 360/1879] lr: 2.0000e-02 eta: 15:36:35 time: 0.3227 data_time: 0.0137 memory: 6717 grad_norm: 2.8473 loss: 1.7428 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7428 2023/04/13 23:59:00 - mmengine - INFO - Epoch(train) [20][ 380/1879] lr: 2.0000e-02 eta: 15:36:30 time: 0.3994 data_time: 0.0131 memory: 6717 grad_norm: 2.9095 loss: 1.7664 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7664 2023/04/13 23:59:07 - mmengine - INFO - Epoch(train) [20][ 400/1879] lr: 2.0000e-02 eta: 15:36:20 time: 0.3319 data_time: 0.0142 memory: 6717 grad_norm: 2.9059 loss: 1.6298 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6298 2023/04/13 23:59:15 - mmengine - INFO - Epoch(train) [20][ 420/1879] lr: 2.0000e-02 eta: 15:36:17 time: 0.4228 data_time: 0.0136 memory: 6717 grad_norm: 2.8582 loss: 1.9394 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.9394 2023/04/13 23:59:21 - mmengine - INFO - Epoch(train) [20][ 440/1879] lr: 2.0000e-02 eta: 15:36:05 time: 0.3113 data_time: 0.0156 memory: 6717 grad_norm: 2.8406 loss: 1.8678 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.8678 2023/04/13 23:59:29 - mmengine - INFO - Epoch(train) [20][ 460/1879] lr: 2.0000e-02 eta: 15:35:58 time: 0.3776 data_time: 0.0136 memory: 6717 grad_norm: 2.7825 loss: 1.9592 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9592 2023/04/13 23:59:36 - mmengine - INFO - Epoch(train) [20][ 480/1879] lr: 2.0000e-02 eta: 15:35:47 time: 0.3288 data_time: 0.0139 memory: 6717 grad_norm: 2.8424 loss: 1.7078 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7078 2023/04/13 23:59:45 - mmengine - INFO - Epoch(train) [20][ 500/1879] lr: 2.0000e-02 eta: 15:35:46 time: 0.4509 data_time: 0.0147 memory: 6717 grad_norm: 2.7789 loss: 1.6768 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6768 2023/04/13 23:59:51 - mmengine - INFO - Epoch(train) [20][ 520/1879] lr: 2.0000e-02 eta: 15:35:34 time: 0.3133 data_time: 0.0135 memory: 6717 grad_norm: 2.8349 loss: 1.7717 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7717 2023/04/13 23:59:59 - mmengine - INFO - Epoch(train) [20][ 540/1879] lr: 2.0000e-02 eta: 15:35:28 time: 0.3913 data_time: 0.0134 memory: 6717 grad_norm: 2.8543 loss: 1.8899 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 1.8899 2023/04/14 00:00:05 - mmengine - INFO - Epoch(train) [20][ 560/1879] lr: 2.0000e-02 eta: 15:35:18 time: 0.3336 data_time: 0.0152 memory: 6717 grad_norm: 2.7528 loss: 1.8379 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.8379 2023/04/14 00:00:13 - mmengine - INFO - Epoch(train) [20][ 580/1879] lr: 2.0000e-02 eta: 15:35:13 time: 0.3993 data_time: 0.0146 memory: 6717 grad_norm: 2.7506 loss: 1.8060 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.8060 2023/04/14 00:00:20 - mmengine - INFO - Epoch(train) [20][ 600/1879] lr: 2.0000e-02 eta: 15:35:03 time: 0.3371 data_time: 0.0138 memory: 6717 grad_norm: 2.8411 loss: 1.7980 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7980 2023/04/14 00:00:29 - mmengine - INFO - Epoch(train) [20][ 620/1879] lr: 2.0000e-02 eta: 15:35:00 time: 0.4226 data_time: 0.0156 memory: 6717 grad_norm: 2.8185 loss: 1.7570 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7570 2023/04/14 00:00:35 - mmengine - INFO - Epoch(train) [20][ 640/1879] lr: 2.0000e-02 eta: 15:34:48 time: 0.3118 data_time: 0.0141 memory: 6717 grad_norm: 2.8387 loss: 1.9462 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9462 2023/04/14 00:00:43 - mmengine - INFO - Epoch(train) [20][ 660/1879] lr: 2.0000e-02 eta: 15:34:44 time: 0.4148 data_time: 0.0148 memory: 6717 grad_norm: 2.8183 loss: 1.8105 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8105 2023/04/14 00:00:50 - mmengine - INFO - Epoch(train) [20][ 680/1879] lr: 2.0000e-02 eta: 15:34:32 time: 0.3201 data_time: 0.0142 memory: 6717 grad_norm: 2.8219 loss: 1.6927 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6927 2023/04/14 00:00:59 - mmengine - INFO - Epoch(train) [20][ 700/1879] lr: 2.0000e-02 eta: 15:34:32 time: 0.4496 data_time: 0.0135 memory: 6717 grad_norm: 2.8115 loss: 1.8224 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8224 2023/04/14 00:01:06 - mmengine - INFO - Epoch(train) [20][ 720/1879] lr: 2.0000e-02 eta: 15:34:23 time: 0.3531 data_time: 0.0139 memory: 6717 grad_norm: 2.8103 loss: 1.7438 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7438 2023/04/14 00:01:14 - mmengine - INFO - Epoch(train) [20][ 740/1879] lr: 2.0000e-02 eta: 15:34:19 time: 0.4151 data_time: 0.0138 memory: 6717 grad_norm: 2.8133 loss: 1.7748 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7748 2023/04/14 00:01:20 - mmengine - INFO - Epoch(train) [20][ 760/1879] lr: 2.0000e-02 eta: 15:34:07 time: 0.3095 data_time: 0.0139 memory: 6717 grad_norm: 2.8495 loss: 1.7330 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7330 2023/04/14 00:01:28 - mmengine - INFO - Epoch(train) [20][ 780/1879] lr: 2.0000e-02 eta: 15:34:03 time: 0.4103 data_time: 0.0148 memory: 6717 grad_norm: 2.7883 loss: 1.7486 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7486 2023/04/14 00:01:35 - mmengine - INFO - Epoch(train) [20][ 800/1879] lr: 2.0000e-02 eta: 15:33:53 time: 0.3403 data_time: 0.0148 memory: 6717 grad_norm: 2.8759 loss: 1.7742 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7742 2023/04/14 00:01:43 - mmengine - INFO - Epoch(train) [20][ 820/1879] lr: 2.0000e-02 eta: 15:33:49 time: 0.4145 data_time: 0.0158 memory: 6717 grad_norm: 2.7563 loss: 1.8399 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8399 2023/04/14 00:01:50 - mmengine - INFO - Epoch(train) [20][ 840/1879] lr: 2.0000e-02 eta: 15:33:37 time: 0.3172 data_time: 0.0124 memory: 6717 grad_norm: 2.8988 loss: 1.9552 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.9552 2023/04/14 00:01:58 - mmengine - INFO - Epoch(train) [20][ 860/1879] lr: 2.0000e-02 eta: 15:33:33 time: 0.4121 data_time: 0.0159 memory: 6717 grad_norm: 2.7707 loss: 1.8996 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.8996 2023/04/14 00:02:04 - mmengine - INFO - Epoch(train) [20][ 880/1879] lr: 2.0000e-02 eta: 15:33:21 time: 0.3076 data_time: 0.0121 memory: 6717 grad_norm: 2.8098 loss: 1.7229 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.7229 2023/04/14 00:02:12 - mmengine - INFO - Epoch(train) [20][ 900/1879] lr: 2.0000e-02 eta: 15:33:15 time: 0.3956 data_time: 0.0222 memory: 6717 grad_norm: 2.8125 loss: 1.5317 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5317 2023/04/14 00:02:18 - mmengine - INFO - Epoch(train) [20][ 920/1879] lr: 2.0000e-02 eta: 15:33:04 time: 0.3173 data_time: 0.0159 memory: 6717 grad_norm: 2.9196 loss: 1.8581 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8581 2023/04/14 00:02:26 - mmengine - INFO - Epoch(train) [20][ 940/1879] lr: 2.0000e-02 eta: 15:32:59 time: 0.3995 data_time: 0.0148 memory: 6717 grad_norm: 2.8613 loss: 1.7080 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.7080 2023/04/14 00:02:33 - mmengine - INFO - Epoch(train) [20][ 960/1879] lr: 2.0000e-02 eta: 15:32:50 time: 0.3492 data_time: 0.0137 memory: 6717 grad_norm: 2.8869 loss: 1.7613 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 1.7613 2023/04/14 00:02:41 - mmengine - INFO - Epoch(train) [20][ 980/1879] lr: 2.0000e-02 eta: 15:32:44 time: 0.3907 data_time: 0.0137 memory: 6717 grad_norm: 2.8158 loss: 1.8290 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8290 2023/04/14 00:02:48 - mmengine - INFO - Epoch(train) [20][1000/1879] lr: 2.0000e-02 eta: 15:32:35 time: 0.3483 data_time: 0.0300 memory: 6717 grad_norm: 2.7974 loss: 1.7474 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7474 2023/04/14 00:02:55 - mmengine - INFO - Epoch(train) [20][1020/1879] lr: 2.0000e-02 eta: 15:32:25 time: 0.3482 data_time: 0.0168 memory: 6717 grad_norm: 2.8043 loss: 1.7597 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7597 2023/04/14 00:03:03 - mmengine - INFO - Epoch(train) [20][1040/1879] lr: 2.0000e-02 eta: 15:32:21 time: 0.4091 data_time: 0.0844 memory: 6717 grad_norm: 2.7795 loss: 1.9214 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9214 2023/04/14 00:03:10 - mmengine - INFO - Epoch(train) [20][1060/1879] lr: 2.0000e-02 eta: 15:32:09 time: 0.3152 data_time: 0.1024 memory: 6717 grad_norm: 2.7914 loss: 1.9010 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9010 2023/04/14 00:03:18 - mmengine - INFO - Epoch(train) [20][1080/1879] lr: 2.0000e-02 eta: 15:32:08 time: 0.4386 data_time: 0.1121 memory: 6717 grad_norm: 2.7829 loss: 1.9091 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9091 2023/04/14 00:03:25 - mmengine - INFO - Epoch(train) [20][1100/1879] lr: 2.0000e-02 eta: 15:31:56 time: 0.3157 data_time: 0.0137 memory: 6717 grad_norm: 2.8378 loss: 1.7084 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7084 2023/04/14 00:03:32 - mmengine - INFO - Epoch(train) [20][1120/1879] lr: 2.0000e-02 eta: 15:31:50 time: 0.3853 data_time: 0.0140 memory: 6717 grad_norm: 2.8313 loss: 1.6288 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.6288 2023/04/14 00:03:40 - mmengine - INFO - Epoch(train) [20][1140/1879] lr: 2.0000e-02 eta: 15:31:42 time: 0.3620 data_time: 0.0670 memory: 6717 grad_norm: 2.8579 loss: 1.6812 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6812 2023/04/14 00:03:48 - mmengine - INFO - Epoch(train) [20][1160/1879] lr: 2.0000e-02 eta: 15:31:37 time: 0.4050 data_time: 0.0126 memory: 6717 grad_norm: 2.7810 loss: 1.9458 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9458 2023/04/14 00:03:55 - mmengine - INFO - Epoch(train) [20][1180/1879] lr: 2.0000e-02 eta: 15:31:27 time: 0.3389 data_time: 0.0142 memory: 6717 grad_norm: 2.8296 loss: 1.7236 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7236 2023/04/14 00:04:02 - mmengine - INFO - Epoch(train) [20][1200/1879] lr: 2.0000e-02 eta: 15:31:22 time: 0.3935 data_time: 0.0179 memory: 6717 grad_norm: 2.7716 loss: 1.9065 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9065 2023/04/14 00:04:09 - mmengine - INFO - Epoch(train) [20][1220/1879] lr: 2.0000e-02 eta: 15:31:12 time: 0.3443 data_time: 0.1329 memory: 6717 grad_norm: 2.8261 loss: 1.7685 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.7685 2023/04/14 00:04:16 - mmengine - INFO - Epoch(train) [20][1240/1879] lr: 2.0000e-02 eta: 15:31:03 time: 0.3534 data_time: 0.1495 memory: 6717 grad_norm: 2.7761 loss: 1.5252 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5252 2023/04/14 00:04:24 - mmengine - INFO - Epoch(train) [20][1260/1879] lr: 2.0000e-02 eta: 15:30:58 time: 0.3955 data_time: 0.1379 memory: 6717 grad_norm: 2.7913 loss: 1.9843 top1_acc: 0.1875 top5_acc: 0.8125 loss_cls: 1.9843 2023/04/14 00:04:31 - mmengine - INFO - Epoch(train) [20][1280/1879] lr: 2.0000e-02 eta: 15:30:50 time: 0.3593 data_time: 0.0121 memory: 6717 grad_norm: 2.7823 loss: 2.1196 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 2.1196 2023/04/14 00:04:39 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 00:04:39 - mmengine - INFO - Epoch(train) [20][1300/1879] lr: 2.0000e-02 eta: 15:30:43 time: 0.3772 data_time: 0.0153 memory: 6717 grad_norm: 2.7990 loss: 1.7139 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7139 2023/04/14 00:04:46 - mmengine - INFO - Epoch(train) [20][1320/1879] lr: 2.0000e-02 eta: 15:30:35 time: 0.3651 data_time: 0.0130 memory: 6717 grad_norm: 2.8809 loss: 1.7551 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7551 2023/04/14 00:04:53 - mmengine - INFO - Epoch(train) [20][1340/1879] lr: 2.0000e-02 eta: 15:30:26 time: 0.3515 data_time: 0.0140 memory: 6717 grad_norm: 2.8091 loss: 1.8800 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8800 2023/04/14 00:05:01 - mmengine - INFO - Epoch(train) [20][1360/1879] lr: 2.0000e-02 eta: 15:30:20 time: 0.3866 data_time: 0.0153 memory: 6717 grad_norm: 2.7973 loss: 1.9592 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9592 2023/04/14 00:05:08 - mmengine - INFO - Epoch(train) [20][1380/1879] lr: 2.0000e-02 eta: 15:30:13 time: 0.3710 data_time: 0.0373 memory: 6717 grad_norm: 2.8045 loss: 1.8312 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8312 2023/04/14 00:05:15 - mmengine - INFO - Epoch(train) [20][1400/1879] lr: 2.0000e-02 eta: 15:30:02 time: 0.3263 data_time: 0.0534 memory: 6717 grad_norm: 2.8160 loss: 1.8314 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8314 2023/04/14 00:05:23 - mmengine - INFO - Epoch(train) [20][1420/1879] lr: 2.0000e-02 eta: 15:29:58 time: 0.4128 data_time: 0.0471 memory: 6717 grad_norm: 2.8403 loss: 1.8179 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8179 2023/04/14 00:05:30 - mmengine - INFO - Epoch(train) [20][1440/1879] lr: 2.0000e-02 eta: 15:29:48 time: 0.3399 data_time: 0.0130 memory: 6717 grad_norm: 2.9628 loss: 1.8836 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8836 2023/04/14 00:05:38 - mmengine - INFO - Epoch(train) [20][1460/1879] lr: 2.0000e-02 eta: 15:29:44 time: 0.4094 data_time: 0.0200 memory: 6717 grad_norm: 2.8018 loss: 2.0375 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0375 2023/04/14 00:05:45 - mmengine - INFO - Epoch(train) [20][1480/1879] lr: 2.0000e-02 eta: 15:29:35 time: 0.3573 data_time: 0.0232 memory: 6717 grad_norm: 2.7937 loss: 1.6653 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6653 2023/04/14 00:05:53 - mmengine - INFO - Epoch(train) [20][1500/1879] lr: 2.0000e-02 eta: 15:29:30 time: 0.3965 data_time: 0.0138 memory: 6717 grad_norm: 2.8201 loss: 1.9389 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9389 2023/04/14 00:06:00 - mmengine - INFO - Epoch(train) [20][1520/1879] lr: 2.0000e-02 eta: 15:29:21 time: 0.3504 data_time: 0.0135 memory: 6717 grad_norm: 2.7302 loss: 1.6526 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6526 2023/04/14 00:06:08 - mmengine - INFO - Epoch(train) [20][1540/1879] lr: 2.0000e-02 eta: 15:29:15 time: 0.3837 data_time: 0.0132 memory: 6717 grad_norm: 2.7313 loss: 1.6842 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6842 2023/04/14 00:06:15 - mmengine - INFO - Epoch(train) [20][1560/1879] lr: 2.0000e-02 eta: 15:29:06 time: 0.3520 data_time: 0.0135 memory: 6717 grad_norm: 2.7930 loss: 1.8467 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8467 2023/04/14 00:06:23 - mmengine - INFO - Epoch(train) [20][1580/1879] lr: 2.0000e-02 eta: 15:28:59 time: 0.3819 data_time: 0.0131 memory: 6717 grad_norm: 2.8321 loss: 2.0011 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0011 2023/04/14 00:06:30 - mmengine - INFO - Epoch(train) [20][1600/1879] lr: 2.0000e-02 eta: 15:28:52 time: 0.3693 data_time: 0.0143 memory: 6717 grad_norm: 2.7949 loss: 1.8517 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8517 2023/04/14 00:06:38 - mmengine - INFO - Epoch(train) [20][1620/1879] lr: 2.0000e-02 eta: 15:28:46 time: 0.3928 data_time: 0.0138 memory: 6717 grad_norm: 2.7879 loss: 2.0312 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 2.0312 2023/04/14 00:06:45 - mmengine - INFO - Epoch(train) [20][1640/1879] lr: 2.0000e-02 eta: 15:28:38 time: 0.3546 data_time: 0.0130 memory: 6717 grad_norm: 2.7728 loss: 1.9547 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.9547 2023/04/14 00:06:53 - mmengine - INFO - Epoch(train) [20][1660/1879] lr: 2.0000e-02 eta: 15:28:34 time: 0.4152 data_time: 0.0143 memory: 6717 grad_norm: 2.8336 loss: 1.8007 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8007 2023/04/14 00:07:00 - mmengine - INFO - Epoch(train) [20][1680/1879] lr: 2.0000e-02 eta: 15:28:23 time: 0.3298 data_time: 0.0141 memory: 6717 grad_norm: 2.7510 loss: 1.6894 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6894 2023/04/14 00:07:08 - mmengine - INFO - Epoch(train) [20][1700/1879] lr: 2.0000e-02 eta: 15:28:17 time: 0.3871 data_time: 0.0136 memory: 6717 grad_norm: 2.8079 loss: 1.8979 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8979 2023/04/14 00:07:14 - mmengine - INFO - Epoch(train) [20][1720/1879] lr: 2.0000e-02 eta: 15:28:07 time: 0.3284 data_time: 0.0140 memory: 6717 grad_norm: 2.8213 loss: 1.8412 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8412 2023/04/14 00:07:23 - mmengine - INFO - Epoch(train) [20][1740/1879] lr: 2.0000e-02 eta: 15:28:03 time: 0.4143 data_time: 0.0134 memory: 6717 grad_norm: 2.7896 loss: 1.5970 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5970 2023/04/14 00:07:29 - mmengine - INFO - Epoch(train) [20][1760/1879] lr: 2.0000e-02 eta: 15:27:53 time: 0.3461 data_time: 0.0140 memory: 6717 grad_norm: 2.7452 loss: 1.8769 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8769 2023/04/14 00:07:38 - mmengine - INFO - Epoch(train) [20][1780/1879] lr: 2.0000e-02 eta: 15:27:49 time: 0.4125 data_time: 0.0144 memory: 6717 grad_norm: 2.7804 loss: 1.7605 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7605 2023/04/14 00:07:44 - mmengine - INFO - Epoch(train) [20][1800/1879] lr: 2.0000e-02 eta: 15:27:38 time: 0.3166 data_time: 0.0142 memory: 6717 grad_norm: 2.9239 loss: 1.5842 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.5842 2023/04/14 00:07:52 - mmengine - INFO - Epoch(train) [20][1820/1879] lr: 2.0000e-02 eta: 15:27:33 time: 0.4038 data_time: 0.0154 memory: 6717 grad_norm: 2.7953 loss: 1.7417 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7417 2023/04/14 00:07:59 - mmengine - INFO - Epoch(train) [20][1840/1879] lr: 2.0000e-02 eta: 15:27:23 time: 0.3374 data_time: 0.0131 memory: 6717 grad_norm: 2.7893 loss: 1.6801 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6801 2023/04/14 00:08:06 - mmengine - INFO - Epoch(train) [20][1860/1879] lr: 2.0000e-02 eta: 15:27:16 time: 0.3712 data_time: 0.0152 memory: 6717 grad_norm: 2.8383 loss: 1.9082 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9082 2023/04/14 00:08:12 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 00:08:12 - mmengine - INFO - Epoch(train) [20][1879/1879] lr: 2.0000e-02 eta: 15:27:05 time: 0.3167 data_time: 0.0133 memory: 6717 grad_norm: 2.9185 loss: 1.8353 top1_acc: 0.4286 top5_acc: 0.8571 loss_cls: 1.8353 2023/04/14 00:08:22 - mmengine - INFO - Epoch(val) [20][ 20/155] eta: 0:01:02 time: 0.4643 data_time: 0.4319 memory: 1391 2023/04/14 00:08:28 - mmengine - INFO - Epoch(val) [20][ 40/155] eta: 0:00:44 time: 0.3171 data_time: 0.2840 memory: 1391 2023/04/14 00:08:37 - mmengine - INFO - Epoch(val) [20][ 60/155] eta: 0:00:38 time: 0.4235 data_time: 0.3904 memory: 1391 2023/04/14 00:08:43 - mmengine - INFO - Epoch(val) [20][ 80/155] eta: 0:00:28 time: 0.3245 data_time: 0.2920 memory: 1391 2023/04/14 00:08:51 - mmengine - INFO - Epoch(val) [20][100/155] eta: 0:00:21 time: 0.4193 data_time: 0.3865 memory: 1391 2023/04/14 00:08:58 - mmengine - INFO - Epoch(val) [20][120/155] eta: 0:00:13 time: 0.3360 data_time: 0.3033 memory: 1391 2023/04/14 00:09:08 - mmengine - INFO - Epoch(val) [20][140/155] eta: 0:00:05 time: 0.4842 data_time: 0.4518 memory: 1391 2023/04/14 00:09:15 - mmengine - INFO - Epoch(val) [20][155/155] acc/top1: 0.5799 acc/top5: 0.8197 acc/mean1: 0.5799 data_time: 0.4183 time: 0.4501 2023/04/14 00:09:25 - mmengine - INFO - Epoch(train) [21][ 20/1879] lr: 2.0000e-02 eta: 15:27:07 time: 0.4839 data_time: 0.2917 memory: 6717 grad_norm: 2.8991 loss: 1.7091 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7091 2023/04/14 00:09:31 - mmengine - INFO - Epoch(train) [21][ 40/1879] lr: 2.0000e-02 eta: 15:26:56 time: 0.3262 data_time: 0.1036 memory: 6717 grad_norm: 2.8668 loss: 1.8303 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8303 2023/04/14 00:09:40 - mmengine - INFO - Epoch(train) [21][ 60/1879] lr: 2.0000e-02 eta: 15:26:54 time: 0.4350 data_time: 0.1973 memory: 6717 grad_norm: 2.8161 loss: 1.7806 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7806 2023/04/14 00:09:46 - mmengine - INFO - Epoch(train) [21][ 80/1879] lr: 2.0000e-02 eta: 15:26:41 time: 0.3001 data_time: 0.0583 memory: 6717 grad_norm: 2.8345 loss: 1.5042 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5042 2023/04/14 00:09:54 - mmengine - INFO - Epoch(train) [21][ 100/1879] lr: 2.0000e-02 eta: 15:26:36 time: 0.4007 data_time: 0.1092 memory: 6717 grad_norm: 2.8368 loss: 1.9313 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9313 2023/04/14 00:10:01 - mmengine - INFO - Epoch(train) [21][ 120/1879] lr: 2.0000e-02 eta: 15:26:26 time: 0.3406 data_time: 0.1381 memory: 6717 grad_norm: 2.8336 loss: 1.6948 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6948 2023/04/14 00:10:08 - mmengine - INFO - Epoch(train) [21][ 140/1879] lr: 2.0000e-02 eta: 15:26:19 time: 0.3735 data_time: 0.1333 memory: 6717 grad_norm: 2.7926 loss: 1.6671 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6671 2023/04/14 00:10:16 - mmengine - INFO - Epoch(train) [21][ 160/1879] lr: 2.0000e-02 eta: 15:26:12 time: 0.3782 data_time: 0.0949 memory: 6717 grad_norm: 2.7979 loss: 1.6963 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6963 2023/04/14 00:10:23 - mmengine - INFO - Epoch(train) [21][ 180/1879] lr: 2.0000e-02 eta: 15:26:06 time: 0.3858 data_time: 0.1704 memory: 6717 grad_norm: 2.7357 loss: 1.6997 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6997 2023/04/14 00:10:32 - mmengine - INFO - Epoch(train) [21][ 200/1879] lr: 2.0000e-02 eta: 15:26:02 time: 0.4089 data_time: 0.2547 memory: 6717 grad_norm: 2.8852 loss: 1.7345 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7345 2023/04/14 00:10:38 - mmengine - INFO - Epoch(train) [21][ 220/1879] lr: 2.0000e-02 eta: 15:25:51 time: 0.3227 data_time: 0.1456 memory: 6717 grad_norm: 2.8971 loss: 1.8180 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8180 2023/04/14 00:10:46 - mmengine - INFO - Epoch(train) [21][ 240/1879] lr: 2.0000e-02 eta: 15:25:44 time: 0.3843 data_time: 0.2347 memory: 6717 grad_norm: 2.8647 loss: 1.7607 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7607 2023/04/14 00:10:53 - mmengine - INFO - Epoch(train) [21][ 260/1879] lr: 2.0000e-02 eta: 15:25:36 time: 0.3634 data_time: 0.1187 memory: 6717 grad_norm: 2.8410 loss: 1.7728 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7728 2023/04/14 00:11:00 - mmengine - INFO - Epoch(train) [21][ 280/1879] lr: 2.0000e-02 eta: 15:25:27 time: 0.3398 data_time: 0.0480 memory: 6717 grad_norm: 2.8564 loss: 1.7678 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7678 2023/04/14 00:11:08 - mmengine - INFO - Epoch(train) [21][ 300/1879] lr: 2.0000e-02 eta: 15:25:24 time: 0.4287 data_time: 0.0146 memory: 6717 grad_norm: 2.7961 loss: 1.7128 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7128 2023/04/14 00:11:15 - mmengine - INFO - Epoch(train) [21][ 320/1879] lr: 2.0000e-02 eta: 15:25:12 time: 0.3168 data_time: 0.0137 memory: 6717 grad_norm: 2.8481 loss: 1.8443 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8443 2023/04/14 00:11:23 - mmengine - INFO - Epoch(train) [21][ 340/1879] lr: 2.0000e-02 eta: 15:25:09 time: 0.4288 data_time: 0.0144 memory: 6717 grad_norm: 2.9368 loss: 1.7582 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7582 2023/04/14 00:11:30 - mmengine - INFO - Epoch(train) [21][ 360/1879] lr: 2.0000e-02 eta: 15:24:57 time: 0.3080 data_time: 0.0137 memory: 6717 grad_norm: 2.7874 loss: 1.7489 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7489 2023/04/14 00:11:38 - mmengine - INFO - Epoch(train) [21][ 380/1879] lr: 2.0000e-02 eta: 15:24:54 time: 0.4193 data_time: 0.0149 memory: 6717 grad_norm: 2.8295 loss: 1.7790 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7790 2023/04/14 00:11:44 - mmengine - INFO - Epoch(train) [21][ 400/1879] lr: 2.0000e-02 eta: 15:24:43 time: 0.3274 data_time: 0.0137 memory: 6717 grad_norm: 2.8676 loss: 1.5857 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5857 2023/04/14 00:11:53 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 00:11:53 - mmengine - INFO - Epoch(train) [21][ 420/1879] lr: 2.0000e-02 eta: 15:24:40 time: 0.4268 data_time: 0.0144 memory: 6717 grad_norm: 2.7637 loss: 1.7442 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7442 2023/04/14 00:12:00 - mmengine - INFO - Epoch(train) [21][ 440/1879] lr: 2.0000e-02 eta: 15:24:30 time: 0.3392 data_time: 0.0156 memory: 6717 grad_norm: 2.8193 loss: 1.6295 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6295 2023/04/14 00:12:07 - mmengine - INFO - Epoch(train) [21][ 460/1879] lr: 2.0000e-02 eta: 15:24:24 time: 0.3853 data_time: 0.0157 memory: 6717 grad_norm: 2.8411 loss: 1.6262 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6262 2023/04/14 00:12:15 - mmengine - INFO - Epoch(train) [21][ 480/1879] lr: 2.0000e-02 eta: 15:24:16 time: 0.3665 data_time: 0.0141 memory: 6717 grad_norm: 2.8368 loss: 1.7110 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.7110 2023/04/14 00:12:23 - mmengine - INFO - Epoch(train) [21][ 500/1879] lr: 2.0000e-02 eta: 15:24:12 time: 0.4171 data_time: 0.0127 memory: 6717 grad_norm: 2.8929 loss: 1.7617 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7617 2023/04/14 00:12:29 - mmengine - INFO - Epoch(train) [21][ 520/1879] lr: 2.0000e-02 eta: 15:24:00 time: 0.3064 data_time: 0.0146 memory: 6717 grad_norm: 2.8352 loss: 1.6623 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6623 2023/04/14 00:12:38 - mmengine - INFO - Epoch(train) [21][ 540/1879] lr: 2.0000e-02 eta: 15:23:57 time: 0.4258 data_time: 0.0138 memory: 6717 grad_norm: 2.8057 loss: 1.7781 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7781 2023/04/14 00:12:44 - mmengine - INFO - Epoch(train) [21][ 560/1879] lr: 2.0000e-02 eta: 15:23:46 time: 0.3185 data_time: 0.0152 memory: 6717 grad_norm: 2.7958 loss: 2.0092 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0092 2023/04/14 00:12:52 - mmengine - INFO - Epoch(train) [21][ 580/1879] lr: 2.0000e-02 eta: 15:23:42 time: 0.4150 data_time: 0.0153 memory: 6717 grad_norm: 2.8001 loss: 1.8719 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8719 2023/04/14 00:12:59 - mmengine - INFO - Epoch(train) [21][ 600/1879] lr: 2.0000e-02 eta: 15:23:30 time: 0.3145 data_time: 0.0127 memory: 6717 grad_norm: 2.7387 loss: 1.9882 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9882 2023/04/14 00:13:07 - mmengine - INFO - Epoch(train) [21][ 620/1879] lr: 2.0000e-02 eta: 15:23:25 time: 0.4010 data_time: 0.0153 memory: 6717 grad_norm: 2.8063 loss: 1.7017 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7017 2023/04/14 00:13:13 - mmengine - INFO - Epoch(train) [21][ 640/1879] lr: 2.0000e-02 eta: 15:23:15 time: 0.3318 data_time: 0.0121 memory: 6717 grad_norm: 2.7409 loss: 1.6593 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6593 2023/04/14 00:13:22 - mmengine - INFO - Epoch(train) [21][ 660/1879] lr: 2.0000e-02 eta: 15:23:11 time: 0.4222 data_time: 0.0169 memory: 6717 grad_norm: 2.8315 loss: 1.7953 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7953 2023/04/14 00:13:28 - mmengine - INFO - Epoch(train) [21][ 680/1879] lr: 2.0000e-02 eta: 15:23:00 time: 0.3252 data_time: 0.0123 memory: 6717 grad_norm: 2.8538 loss: 1.5844 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5844 2023/04/14 00:13:37 - mmengine - INFO - Epoch(train) [21][ 700/1879] lr: 2.0000e-02 eta: 15:22:59 time: 0.4458 data_time: 0.0156 memory: 6717 grad_norm: 2.8257 loss: 1.8592 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.8592 2023/04/14 00:13:44 - mmengine - INFO - Epoch(train) [21][ 720/1879] lr: 2.0000e-02 eta: 15:22:47 time: 0.3127 data_time: 0.0131 memory: 6717 grad_norm: 2.7355 loss: 1.8521 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8521 2023/04/14 00:13:51 - mmengine - INFO - Epoch(train) [21][ 740/1879] lr: 2.0000e-02 eta: 15:22:41 time: 0.3929 data_time: 0.0147 memory: 6717 grad_norm: 2.6874 loss: 1.5823 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5823 2023/04/14 00:13:58 - mmengine - INFO - Epoch(train) [21][ 760/1879] lr: 2.0000e-02 eta: 15:22:29 time: 0.3098 data_time: 0.0147 memory: 6717 grad_norm: 2.7547 loss: 1.7366 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7366 2023/04/14 00:14:05 - mmengine - INFO - Epoch(train) [21][ 780/1879] lr: 2.0000e-02 eta: 15:22:24 time: 0.3946 data_time: 0.0170 memory: 6717 grad_norm: 2.8948 loss: 1.5678 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5678 2023/04/14 00:14:13 - mmengine - INFO - Epoch(train) [21][ 800/1879] lr: 2.0000e-02 eta: 15:22:16 time: 0.3615 data_time: 0.0128 memory: 6717 grad_norm: 2.8441 loss: 1.8459 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8459 2023/04/14 00:14:20 - mmengine - INFO - Epoch(train) [21][ 820/1879] lr: 2.0000e-02 eta: 15:22:09 time: 0.3788 data_time: 0.0144 memory: 6717 grad_norm: 2.8537 loss: 1.8999 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8999 2023/04/14 00:14:27 - mmengine - INFO - Epoch(train) [21][ 840/1879] lr: 2.0000e-02 eta: 15:21:58 time: 0.3302 data_time: 0.0141 memory: 6717 grad_norm: 2.7932 loss: 1.5460 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5460 2023/04/14 00:14:35 - mmengine - INFO - Epoch(train) [21][ 860/1879] lr: 2.0000e-02 eta: 15:21:54 time: 0.4142 data_time: 0.0155 memory: 6717 grad_norm: 2.8309 loss: 1.8457 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8457 2023/04/14 00:14:42 - mmengine - INFO - Epoch(train) [21][ 880/1879] lr: 2.0000e-02 eta: 15:21:44 time: 0.3347 data_time: 0.0139 memory: 6717 grad_norm: 2.8446 loss: 1.9558 top1_acc: 0.2500 top5_acc: 0.8125 loss_cls: 1.9558 2023/04/14 00:14:50 - mmengine - INFO - Epoch(train) [21][ 900/1879] lr: 2.0000e-02 eta: 15:21:39 time: 0.3918 data_time: 0.0164 memory: 6717 grad_norm: 2.7972 loss: 1.9352 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9352 2023/04/14 00:14:57 - mmengine - INFO - Epoch(train) [21][ 920/1879] lr: 2.0000e-02 eta: 15:21:29 time: 0.3463 data_time: 0.0122 memory: 6717 grad_norm: 2.7651 loss: 1.8775 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8775 2023/04/14 00:15:05 - mmengine - INFO - Epoch(train) [21][ 940/1879] lr: 2.0000e-02 eta: 15:21:25 time: 0.4069 data_time: 0.0154 memory: 6717 grad_norm: 2.8740 loss: 1.9752 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9752 2023/04/14 00:15:12 - mmengine - INFO - Epoch(train) [21][ 960/1879] lr: 2.0000e-02 eta: 15:21:17 time: 0.3640 data_time: 0.0123 memory: 6717 grad_norm: 2.8006 loss: 1.8065 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.8065 2023/04/14 00:15:21 - mmengine - INFO - Epoch(train) [21][ 980/1879] lr: 2.0000e-02 eta: 15:21:14 time: 0.4273 data_time: 0.0154 memory: 6717 grad_norm: 2.7540 loss: 1.9508 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9508 2023/04/14 00:15:27 - mmengine - INFO - Epoch(train) [21][1000/1879] lr: 2.0000e-02 eta: 15:21:02 time: 0.3162 data_time: 0.0128 memory: 6717 grad_norm: 2.7711 loss: 1.7669 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.7669 2023/04/14 00:15:35 - mmengine - INFO - Epoch(train) [21][1020/1879] lr: 2.0000e-02 eta: 15:20:59 time: 0.4237 data_time: 0.0149 memory: 6717 grad_norm: 2.8010 loss: 1.8752 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8752 2023/04/14 00:15:42 - mmengine - INFO - Epoch(train) [21][1040/1879] lr: 2.0000e-02 eta: 15:20:48 time: 0.3204 data_time: 0.0130 memory: 6717 grad_norm: 2.7774 loss: 1.7667 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7667 2023/04/14 00:15:50 - mmengine - INFO - Epoch(train) [21][1060/1879] lr: 2.0000e-02 eta: 15:20:43 time: 0.4032 data_time: 0.0150 memory: 6717 grad_norm: 2.7157 loss: 1.7136 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7136 2023/04/14 00:15:56 - mmengine - INFO - Epoch(train) [21][1080/1879] lr: 2.0000e-02 eta: 15:20:30 time: 0.3010 data_time: 0.0128 memory: 6717 grad_norm: 2.7864 loss: 1.6906 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6906 2023/04/14 00:16:04 - mmengine - INFO - Epoch(train) [21][1100/1879] lr: 2.0000e-02 eta: 15:20:26 time: 0.4090 data_time: 0.0142 memory: 6717 grad_norm: 2.8092 loss: 1.7366 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.7366 2023/04/14 00:16:11 - mmengine - INFO - Epoch(train) [21][1120/1879] lr: 2.0000e-02 eta: 15:20:17 time: 0.3483 data_time: 0.0141 memory: 6717 grad_norm: 2.8735 loss: 2.0065 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 2.0065 2023/04/14 00:16:19 - mmengine - INFO - Epoch(train) [21][1140/1879] lr: 2.0000e-02 eta: 15:20:12 time: 0.4021 data_time: 0.0136 memory: 6717 grad_norm: 2.7660 loss: 1.7558 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7558 2023/04/14 00:16:26 - mmengine - INFO - Epoch(train) [21][1160/1879] lr: 2.0000e-02 eta: 15:20:03 time: 0.3566 data_time: 0.0148 memory: 6717 grad_norm: 2.8901 loss: 1.7708 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7708 2023/04/14 00:16:35 - mmengine - INFO - Epoch(train) [21][1180/1879] lr: 2.0000e-02 eta: 15:20:00 time: 0.4245 data_time: 0.0148 memory: 6717 grad_norm: 2.8735 loss: 1.6697 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6697 2023/04/14 00:16:41 - mmengine - INFO - Epoch(train) [21][1200/1879] lr: 2.0000e-02 eta: 15:19:49 time: 0.3154 data_time: 0.0131 memory: 6717 grad_norm: 2.7960 loss: 1.8217 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8217 2023/04/14 00:16:49 - mmengine - INFO - Epoch(train) [21][1220/1879] lr: 2.0000e-02 eta: 15:19:44 time: 0.4097 data_time: 0.0147 memory: 6717 grad_norm: 2.8558 loss: 1.8019 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8019 2023/04/14 00:16:56 - mmengine - INFO - Epoch(train) [21][1240/1879] lr: 2.0000e-02 eta: 15:19:33 time: 0.3234 data_time: 0.0133 memory: 6717 grad_norm: 2.8373 loss: 1.9072 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.9072 2023/04/14 00:17:04 - mmengine - INFO - Epoch(train) [21][1260/1879] lr: 2.0000e-02 eta: 15:19:31 time: 0.4364 data_time: 0.0140 memory: 6717 grad_norm: 2.7589 loss: 1.6637 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6637 2023/04/14 00:17:11 - mmengine - INFO - Epoch(train) [21][1280/1879] lr: 2.0000e-02 eta: 15:19:18 time: 0.3049 data_time: 0.0158 memory: 6717 grad_norm: 2.8353 loss: 1.7346 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7346 2023/04/14 00:17:19 - mmengine - INFO - Epoch(train) [21][1300/1879] lr: 2.0000e-02 eta: 15:19:15 time: 0.4170 data_time: 0.0155 memory: 6717 grad_norm: 2.8384 loss: 1.8438 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8438 2023/04/14 00:17:25 - mmengine - INFO - Epoch(train) [21][1320/1879] lr: 2.0000e-02 eta: 15:19:03 time: 0.3177 data_time: 0.0147 memory: 6717 grad_norm: 2.7774 loss: 1.7655 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7655 2023/04/14 00:17:33 - mmengine - INFO - Epoch(train) [21][1340/1879] lr: 2.0000e-02 eta: 15:18:58 time: 0.3979 data_time: 0.0156 memory: 6717 grad_norm: 2.8094 loss: 1.7917 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7917 2023/04/14 00:17:40 - mmengine - INFO - Epoch(train) [21][1360/1879] lr: 2.0000e-02 eta: 15:18:46 time: 0.3159 data_time: 0.0138 memory: 6717 grad_norm: 2.8504 loss: 1.9483 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9483 2023/04/14 00:17:48 - mmengine - INFO - Epoch(train) [21][1380/1879] lr: 2.0000e-02 eta: 15:18:43 time: 0.4156 data_time: 0.0162 memory: 6717 grad_norm: 2.7448 loss: 1.7552 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7552 2023/04/14 00:17:55 - mmengine - INFO - Epoch(train) [21][1400/1879] lr: 2.0000e-02 eta: 15:18:33 time: 0.3443 data_time: 0.0135 memory: 6717 grad_norm: 2.7378 loss: 1.6906 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6906 2023/04/14 00:18:03 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 00:18:03 - mmengine - INFO - Epoch(train) [21][1420/1879] lr: 2.0000e-02 eta: 15:18:28 time: 0.4026 data_time: 0.0135 memory: 6717 grad_norm: 2.7895 loss: 1.9556 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9556 2023/04/14 00:18:09 - mmengine - INFO - Epoch(train) [21][1440/1879] lr: 2.0000e-02 eta: 15:18:16 time: 0.3084 data_time: 0.0149 memory: 6717 grad_norm: 2.8041 loss: 1.8228 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.8228 2023/04/14 00:18:17 - mmengine - INFO - Epoch(train) [21][1460/1879] lr: 2.0000e-02 eta: 15:18:13 time: 0.4263 data_time: 0.0145 memory: 6717 grad_norm: 2.8321 loss: 1.8248 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8248 2023/04/14 00:18:23 - mmengine - INFO - Epoch(train) [21][1480/1879] lr: 2.0000e-02 eta: 15:18:00 time: 0.2937 data_time: 0.0132 memory: 6717 grad_norm: 2.7704 loss: 1.8337 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8337 2023/04/14 00:18:32 - mmengine - INFO - Epoch(train) [21][1500/1879] lr: 2.0000e-02 eta: 15:17:56 time: 0.4202 data_time: 0.0154 memory: 6717 grad_norm: 2.8197 loss: 1.7443 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7443 2023/04/14 00:18:38 - mmengine - INFO - Epoch(train) [21][1520/1879] lr: 2.0000e-02 eta: 15:17:45 time: 0.3233 data_time: 0.0136 memory: 6717 grad_norm: 2.7493 loss: 1.8666 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.8666 2023/04/14 00:18:46 - mmengine - INFO - Epoch(train) [21][1540/1879] lr: 2.0000e-02 eta: 15:17:40 time: 0.4050 data_time: 0.0143 memory: 6717 grad_norm: 2.7634 loss: 1.7786 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7786 2023/04/14 00:18:53 - mmengine - INFO - Epoch(train) [21][1560/1879] lr: 2.0000e-02 eta: 15:17:30 time: 0.3233 data_time: 0.0142 memory: 6717 grad_norm: 2.8305 loss: 1.6926 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6926 2023/04/14 00:19:01 - mmengine - INFO - Epoch(train) [21][1580/1879] lr: 2.0000e-02 eta: 15:17:24 time: 0.3898 data_time: 0.0155 memory: 6717 grad_norm: 2.7935 loss: 1.6420 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6420 2023/04/14 00:19:07 - mmengine - INFO - Epoch(train) [21][1600/1879] lr: 2.0000e-02 eta: 15:17:13 time: 0.3338 data_time: 0.0141 memory: 6717 grad_norm: 2.7185 loss: 1.7749 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.7749 2023/04/14 00:19:16 - mmengine - INFO - Epoch(train) [21][1620/1879] lr: 2.0000e-02 eta: 15:17:09 time: 0.4143 data_time: 0.0137 memory: 6717 grad_norm: 2.8677 loss: 1.7238 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.7238 2023/04/14 00:19:22 - mmengine - INFO - Epoch(train) [21][1640/1879] lr: 2.0000e-02 eta: 15:16:59 time: 0.3290 data_time: 0.0137 memory: 6717 grad_norm: 2.8203 loss: 1.7551 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7551 2023/04/14 00:19:30 - mmengine - INFO - Epoch(train) [21][1660/1879] lr: 2.0000e-02 eta: 15:16:53 time: 0.3894 data_time: 0.0143 memory: 6717 grad_norm: 2.8634 loss: 1.7259 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7259 2023/04/14 00:19:36 - mmengine - INFO - Epoch(train) [21][1680/1879] lr: 2.0000e-02 eta: 15:16:42 time: 0.3264 data_time: 0.0140 memory: 6717 grad_norm: 2.8465 loss: 1.8352 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8352 2023/04/14 00:19:46 - mmengine - INFO - Epoch(train) [21][1700/1879] lr: 2.0000e-02 eta: 15:16:43 time: 0.4726 data_time: 0.0132 memory: 6717 grad_norm: 2.7967 loss: 1.9157 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9157 2023/04/14 00:19:53 - mmengine - INFO - Epoch(train) [21][1720/1879] lr: 2.0000e-02 eta: 15:16:32 time: 0.3325 data_time: 0.0140 memory: 6717 grad_norm: 2.8136 loss: 1.7104 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7104 2023/04/14 00:20:00 - mmengine - INFO - Epoch(train) [21][1740/1879] lr: 2.0000e-02 eta: 15:16:26 time: 0.3838 data_time: 0.0149 memory: 6717 grad_norm: 2.8011 loss: 1.8648 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8648 2023/04/14 00:20:06 - mmengine - INFO - Epoch(train) [21][1760/1879] lr: 2.0000e-02 eta: 15:16:13 time: 0.3010 data_time: 0.0144 memory: 6717 grad_norm: 2.7779 loss: 1.5942 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5942 2023/04/14 00:20:14 - mmengine - INFO - Epoch(train) [21][1780/1879] lr: 2.0000e-02 eta: 15:16:08 time: 0.4030 data_time: 0.0129 memory: 6717 grad_norm: 2.8081 loss: 2.0051 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0051 2023/04/14 00:20:21 - mmengine - INFO - Epoch(train) [21][1800/1879] lr: 2.0000e-02 eta: 15:15:58 time: 0.3270 data_time: 0.0140 memory: 6717 grad_norm: 2.7801 loss: 1.8195 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.8195 2023/04/14 00:20:29 - mmengine - INFO - Epoch(train) [21][1820/1879] lr: 2.0000e-02 eta: 15:15:53 time: 0.4010 data_time: 0.0139 memory: 6717 grad_norm: 2.7815 loss: 1.8862 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8862 2023/04/14 00:20:36 - mmengine - INFO - Epoch(train) [21][1840/1879] lr: 2.0000e-02 eta: 15:15:43 time: 0.3348 data_time: 0.0147 memory: 6717 grad_norm: 2.7635 loss: 1.9864 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9864 2023/04/14 00:20:44 - mmengine - INFO - Epoch(train) [21][1860/1879] lr: 2.0000e-02 eta: 15:15:39 time: 0.4172 data_time: 0.0130 memory: 6717 grad_norm: 2.8579 loss: 2.0395 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0395 2023/04/14 00:20:50 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 00:20:50 - mmengine - INFO - Epoch(train) [21][1879/1879] lr: 2.0000e-02 eta: 15:15:28 time: 0.3056 data_time: 0.0136 memory: 6717 grad_norm: 3.2467 loss: 1.9209 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.9209 2023/04/14 00:20:50 - mmengine - INFO - Saving checkpoint at 21 epochs 2023/04/14 00:20:59 - mmengine - INFO - Epoch(val) [21][ 20/155] eta: 0:00:58 time: 0.4357 data_time: 0.4023 memory: 1391 2023/04/14 00:21:06 - mmengine - INFO - Epoch(val) [21][ 40/155] eta: 0:00:44 time: 0.3410 data_time: 0.3080 memory: 1391 2023/04/14 00:21:13 - mmengine - INFO - Epoch(val) [21][ 60/155] eta: 0:00:36 time: 0.3701 data_time: 0.3320 memory: 1391 2023/04/14 00:21:21 - mmengine - INFO - Epoch(val) [21][ 80/155] eta: 0:00:28 time: 0.3758 data_time: 0.3424 memory: 1391 2023/04/14 00:21:29 - mmengine - INFO - Epoch(val) [21][100/155] eta: 0:00:21 time: 0.4254 data_time: 0.3910 memory: 1391 2023/04/14 00:21:36 - mmengine - INFO - Epoch(val) [21][120/155] eta: 0:00:13 time: 0.3107 data_time: 0.2771 memory: 1391 2023/04/14 00:21:43 - mmengine - INFO - Epoch(val) [21][140/155] eta: 0:00:05 time: 0.3785 data_time: 0.3448 memory: 1391 2023/04/14 00:21:51 - mmengine - INFO - Epoch(val) [21][155/155] acc/top1: 0.5854 acc/top5: 0.8231 acc/mean1: 0.5853 data_time: 0.3104 time: 0.3438 2023/04/14 00:21:51 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_19.pth is removed 2023/04/14 00:21:52 - mmengine - INFO - The best checkpoint with 0.5854 acc/top1 at 21 epoch is saved to best_acc_top1_epoch_21.pth. 2023/04/14 00:22:01 - mmengine - INFO - Epoch(train) [22][ 20/1879] lr: 2.0000e-02 eta: 15:15:27 time: 0.4525 data_time: 0.2691 memory: 6717 grad_norm: 2.9140 loss: 1.9002 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9002 2023/04/14 00:22:08 - mmengine - INFO - Epoch(train) [22][ 40/1879] lr: 2.0000e-02 eta: 15:15:16 time: 0.3344 data_time: 0.0683 memory: 6717 grad_norm: 2.8671 loss: 1.7831 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7831 2023/04/14 00:22:16 - mmengine - INFO - Epoch(train) [22][ 60/1879] lr: 2.0000e-02 eta: 15:15:13 time: 0.4249 data_time: 0.1410 memory: 6717 grad_norm: 2.8175 loss: 1.6856 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.6856 2023/04/14 00:22:23 - mmengine - INFO - Epoch(train) [22][ 80/1879] lr: 2.0000e-02 eta: 15:15:03 time: 0.3282 data_time: 0.0355 memory: 6717 grad_norm: 2.8547 loss: 1.6483 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6483 2023/04/14 00:22:31 - mmengine - INFO - Epoch(train) [22][ 100/1879] lr: 2.0000e-02 eta: 15:15:00 time: 0.4366 data_time: 0.0410 memory: 6717 grad_norm: 2.7788 loss: 1.6545 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6545 2023/04/14 00:22:37 - mmengine - INFO - Epoch(train) [22][ 120/1879] lr: 2.0000e-02 eta: 15:14:48 time: 0.3034 data_time: 0.0121 memory: 6717 grad_norm: 2.9057 loss: 1.7912 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7912 2023/04/14 00:22:45 - mmengine - INFO - Epoch(train) [22][ 140/1879] lr: 2.0000e-02 eta: 15:14:43 time: 0.3986 data_time: 0.0217 memory: 6717 grad_norm: 2.8285 loss: 1.6966 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6966 2023/04/14 00:22:52 - mmengine - INFO - Epoch(train) [22][ 160/1879] lr: 2.0000e-02 eta: 15:14:31 time: 0.3164 data_time: 0.0223 memory: 6717 grad_norm: 2.7735 loss: 1.6808 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6808 2023/04/14 00:23:00 - mmengine - INFO - Epoch(train) [22][ 180/1879] lr: 2.0000e-02 eta: 15:14:29 time: 0.4347 data_time: 0.0156 memory: 6717 grad_norm: 2.7334 loss: 1.6926 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6926 2023/04/14 00:23:08 - mmengine - INFO - Epoch(train) [22][ 200/1879] lr: 2.0000e-02 eta: 15:14:20 time: 0.3596 data_time: 0.0534 memory: 6717 grad_norm: 2.7394 loss: 1.9471 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9471 2023/04/14 00:23:15 - mmengine - INFO - Epoch(train) [22][ 220/1879] lr: 2.0000e-02 eta: 15:14:12 time: 0.3519 data_time: 0.0782 memory: 6717 grad_norm: 2.8410 loss: 1.6926 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6926 2023/04/14 00:23:22 - mmengine - INFO - Epoch(train) [22][ 240/1879] lr: 2.0000e-02 eta: 15:14:05 time: 0.3746 data_time: 0.0902 memory: 6717 grad_norm: 2.7165 loss: 1.5942 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5942 2023/04/14 00:23:30 - mmengine - INFO - Epoch(train) [22][ 260/1879] lr: 2.0000e-02 eta: 15:13:58 time: 0.3807 data_time: 0.1405 memory: 6717 grad_norm: 2.8184 loss: 1.8907 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 1.8907 2023/04/14 00:23:38 - mmengine - INFO - Epoch(train) [22][ 280/1879] lr: 2.0000e-02 eta: 15:13:54 time: 0.4134 data_time: 0.2191 memory: 6717 grad_norm: 2.7889 loss: 1.7129 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7129 2023/04/14 00:23:45 - mmengine - INFO - Epoch(train) [22][ 300/1879] lr: 2.0000e-02 eta: 15:13:43 time: 0.3289 data_time: 0.1814 memory: 6717 grad_norm: 2.8069 loss: 1.6227 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6227 2023/04/14 00:23:53 - mmengine - INFO - Epoch(train) [22][ 320/1879] lr: 2.0000e-02 eta: 15:13:39 time: 0.4134 data_time: 0.2760 memory: 6717 grad_norm: 2.8273 loss: 1.8621 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8621 2023/04/14 00:24:00 - mmengine - INFO - Epoch(train) [22][ 340/1879] lr: 2.0000e-02 eta: 15:13:29 time: 0.3323 data_time: 0.1885 memory: 6717 grad_norm: 2.7537 loss: 1.7755 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7755 2023/04/14 00:24:07 - mmengine - INFO - Epoch(train) [22][ 360/1879] lr: 2.0000e-02 eta: 15:13:22 time: 0.3777 data_time: 0.2345 memory: 6717 grad_norm: 2.8301 loss: 1.7253 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7253 2023/04/14 00:24:14 - mmengine - INFO - Epoch(train) [22][ 380/1879] lr: 2.0000e-02 eta: 15:13:13 time: 0.3495 data_time: 0.2079 memory: 6717 grad_norm: 2.8227 loss: 1.8361 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8361 2023/04/14 00:24:22 - mmengine - INFO - Epoch(train) [22][ 400/1879] lr: 2.0000e-02 eta: 15:13:07 time: 0.3920 data_time: 0.2495 memory: 6717 grad_norm: 2.8771 loss: 2.0465 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 2.0465 2023/04/14 00:24:28 - mmengine - INFO - Epoch(train) [22][ 420/1879] lr: 2.0000e-02 eta: 15:12:56 time: 0.3160 data_time: 0.1296 memory: 6717 grad_norm: 2.7793 loss: 1.7048 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7048 2023/04/14 00:24:36 - mmengine - INFO - Epoch(train) [22][ 440/1879] lr: 2.0000e-02 eta: 15:12:51 time: 0.4067 data_time: 0.1586 memory: 6717 grad_norm: 2.8834 loss: 1.8460 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.8460 2023/04/14 00:24:44 - mmengine - INFO - Epoch(train) [22][ 460/1879] lr: 2.0000e-02 eta: 15:12:45 time: 0.3803 data_time: 0.0958 memory: 6717 grad_norm: 2.7128 loss: 1.8872 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8872 2023/04/14 00:24:51 - mmengine - INFO - Epoch(train) [22][ 480/1879] lr: 2.0000e-02 eta: 15:12:36 time: 0.3526 data_time: 0.0152 memory: 6717 grad_norm: 2.7691 loss: 1.8553 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8553 2023/04/14 00:24:58 - mmengine - INFO - Epoch(train) [22][ 500/1879] lr: 2.0000e-02 eta: 15:12:28 time: 0.3583 data_time: 0.0451 memory: 6717 grad_norm: 2.7674 loss: 1.8027 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8027 2023/04/14 00:25:06 - mmengine - INFO - Epoch(train) [22][ 520/1879] lr: 2.0000e-02 eta: 15:12:21 time: 0.3771 data_time: 0.0997 memory: 6717 grad_norm: 2.7960 loss: 1.8234 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.8234 2023/04/14 00:25:14 - mmengine - INFO - Epoch(train) [22][ 540/1879] lr: 2.0000e-02 eta: 15:12:16 time: 0.4031 data_time: 0.0225 memory: 6717 grad_norm: 2.7774 loss: 1.8837 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.8837 2023/04/14 00:25:14 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 00:25:20 - mmengine - INFO - Epoch(train) [22][ 560/1879] lr: 2.0000e-02 eta: 15:12:04 time: 0.3081 data_time: 0.0154 memory: 6717 grad_norm: 2.7955 loss: 1.8113 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8113 2023/04/14 00:25:28 - mmengine - INFO - Epoch(train) [22][ 580/1879] lr: 2.0000e-02 eta: 15:11:57 time: 0.3839 data_time: 0.0149 memory: 6717 grad_norm: 2.7617 loss: 1.9380 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.9380 2023/04/14 00:25:35 - mmengine - INFO - Epoch(train) [22][ 600/1879] lr: 2.0000e-02 eta: 15:11:49 time: 0.3576 data_time: 0.0537 memory: 6717 grad_norm: 2.7882 loss: 1.7917 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7917 2023/04/14 00:25:43 - mmengine - INFO - Epoch(train) [22][ 620/1879] lr: 2.0000e-02 eta: 15:11:43 time: 0.3880 data_time: 0.0134 memory: 6717 grad_norm: 2.8015 loss: 1.7770 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7770 2023/04/14 00:25:50 - mmengine - INFO - Epoch(train) [22][ 640/1879] lr: 2.0000e-02 eta: 15:11:34 time: 0.3508 data_time: 0.0229 memory: 6717 grad_norm: 2.7991 loss: 1.7949 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7949 2023/04/14 00:25:58 - mmengine - INFO - Epoch(train) [22][ 660/1879] lr: 2.0000e-02 eta: 15:11:30 time: 0.4070 data_time: 0.0133 memory: 6717 grad_norm: 2.8924 loss: 1.6513 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6513 2023/04/14 00:26:05 - mmengine - INFO - Epoch(train) [22][ 680/1879] lr: 2.0000e-02 eta: 15:11:20 time: 0.3420 data_time: 0.0151 memory: 6717 grad_norm: 2.7074 loss: 1.5222 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5222 2023/04/14 00:26:12 - mmengine - INFO - Epoch(train) [22][ 700/1879] lr: 2.0000e-02 eta: 15:11:13 time: 0.3690 data_time: 0.0137 memory: 6717 grad_norm: 2.7764 loss: 1.8710 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8710 2023/04/14 00:26:19 - mmengine - INFO - Epoch(train) [22][ 720/1879] lr: 2.0000e-02 eta: 15:11:03 time: 0.3416 data_time: 0.0152 memory: 6717 grad_norm: 2.7534 loss: 1.8309 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8309 2023/04/14 00:26:27 - mmengine - INFO - Epoch(train) [22][ 740/1879] lr: 2.0000e-02 eta: 15:10:58 time: 0.3992 data_time: 0.0144 memory: 6717 grad_norm: 2.8036 loss: 1.6869 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6869 2023/04/14 00:26:33 - mmengine - INFO - Epoch(train) [22][ 760/1879] lr: 2.0000e-02 eta: 15:10:47 time: 0.3196 data_time: 0.0153 memory: 6717 grad_norm: 2.8194 loss: 1.9398 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.9398 2023/04/14 00:26:41 - mmengine - INFO - Epoch(train) [22][ 780/1879] lr: 2.0000e-02 eta: 15:10:41 time: 0.3951 data_time: 0.0215 memory: 6717 grad_norm: 2.7882 loss: 1.6844 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6844 2023/04/14 00:26:48 - mmengine - INFO - Epoch(train) [22][ 800/1879] lr: 2.0000e-02 eta: 15:10:32 time: 0.3425 data_time: 0.0739 memory: 6717 grad_norm: 2.8020 loss: 1.6133 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6133 2023/04/14 00:26:55 - mmengine - INFO - Epoch(train) [22][ 820/1879] lr: 2.0000e-02 eta: 15:10:25 time: 0.3738 data_time: 0.1050 memory: 6717 grad_norm: 2.8805 loss: 1.7892 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.7892 2023/04/14 00:27:03 - mmengine - INFO - Epoch(train) [22][ 840/1879] lr: 2.0000e-02 eta: 15:10:17 time: 0.3685 data_time: 0.2220 memory: 6717 grad_norm: 2.7533 loss: 1.8506 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.8506 2023/04/14 00:27:10 - mmengine - INFO - Epoch(train) [22][ 860/1879] lr: 2.0000e-02 eta: 15:10:10 time: 0.3788 data_time: 0.1079 memory: 6717 grad_norm: 2.8258 loss: 1.7819 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.7819 2023/04/14 00:27:17 - mmengine - INFO - Epoch(train) [22][ 880/1879] lr: 2.0000e-02 eta: 15:10:02 time: 0.3520 data_time: 0.1093 memory: 6717 grad_norm: 2.8223 loss: 1.6229 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6229 2023/04/14 00:27:25 - mmengine - INFO - Epoch(train) [22][ 900/1879] lr: 2.0000e-02 eta: 15:09:53 time: 0.3557 data_time: 0.1551 memory: 6717 grad_norm: 2.8637 loss: 1.9903 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9903 2023/04/14 00:27:33 - mmengine - INFO - Epoch(train) [22][ 920/1879] lr: 2.0000e-02 eta: 15:09:49 time: 0.4134 data_time: 0.1797 memory: 6717 grad_norm: 2.7800 loss: 1.7516 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7516 2023/04/14 00:27:40 - mmengine - INFO - Epoch(train) [22][ 940/1879] lr: 2.0000e-02 eta: 15:09:41 time: 0.3551 data_time: 0.0694 memory: 6717 grad_norm: 2.7689 loss: 1.6190 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6190 2023/04/14 00:27:48 - mmengine - INFO - Epoch(train) [22][ 960/1879] lr: 2.0000e-02 eta: 15:09:34 time: 0.3879 data_time: 0.0772 memory: 6717 grad_norm: 2.8794 loss: 1.9166 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9166 2023/04/14 00:27:55 - mmengine - INFO - Epoch(train) [22][ 980/1879] lr: 2.0000e-02 eta: 15:09:25 time: 0.3415 data_time: 0.0702 memory: 6717 grad_norm: 2.7624 loss: 1.6128 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6128 2023/04/14 00:28:03 - mmengine - INFO - Epoch(train) [22][1000/1879] lr: 2.0000e-02 eta: 15:09:21 time: 0.4183 data_time: 0.0984 memory: 6717 grad_norm: 2.8280 loss: 2.0259 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.0259 2023/04/14 00:28:09 - mmengine - INFO - Epoch(train) [22][1020/1879] lr: 2.0000e-02 eta: 15:09:10 time: 0.3243 data_time: 0.0579 memory: 6717 grad_norm: 2.7353 loss: 1.7665 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.7665 2023/04/14 00:28:18 - mmengine - INFO - Epoch(train) [22][1040/1879] lr: 2.0000e-02 eta: 15:09:06 time: 0.4088 data_time: 0.1393 memory: 6717 grad_norm: 2.7912 loss: 1.6136 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6136 2023/04/14 00:28:24 - mmengine - INFO - Epoch(train) [22][1060/1879] lr: 2.0000e-02 eta: 15:08:56 time: 0.3346 data_time: 0.1382 memory: 6717 grad_norm: 2.7686 loss: 1.8862 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8862 2023/04/14 00:28:32 - mmengine - INFO - Epoch(train) [22][1080/1879] lr: 2.0000e-02 eta: 15:08:50 time: 0.3861 data_time: 0.1416 memory: 6717 grad_norm: 2.7617 loss: 1.6961 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6961 2023/04/14 00:28:38 - mmengine - INFO - Epoch(train) [22][1100/1879] lr: 2.0000e-02 eta: 15:08:39 time: 0.3202 data_time: 0.0630 memory: 6717 grad_norm: 2.8106 loss: 1.7006 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7006 2023/04/14 00:28:47 - mmengine - INFO - Epoch(train) [22][1120/1879] lr: 2.0000e-02 eta: 15:08:36 time: 0.4360 data_time: 0.1037 memory: 6717 grad_norm: 2.7436 loss: 1.7603 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7603 2023/04/14 00:28:54 - mmengine - INFO - Epoch(train) [22][1140/1879] lr: 2.0000e-02 eta: 15:08:25 time: 0.3284 data_time: 0.0516 memory: 6717 grad_norm: 2.7804 loss: 1.8398 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8398 2023/04/14 00:29:02 - mmengine - INFO - Epoch(train) [22][1160/1879] lr: 2.0000e-02 eta: 15:08:21 time: 0.4038 data_time: 0.0399 memory: 6717 grad_norm: 2.8187 loss: 1.8841 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.8841 2023/04/14 00:29:09 - mmengine - INFO - Epoch(train) [22][1180/1879] lr: 2.0000e-02 eta: 15:08:11 time: 0.3402 data_time: 0.0139 memory: 6717 grad_norm: 2.7511 loss: 1.8198 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8198 2023/04/14 00:29:17 - mmengine - INFO - Epoch(train) [22][1200/1879] lr: 2.0000e-02 eta: 15:08:06 time: 0.4017 data_time: 0.0156 memory: 6717 grad_norm: 2.7833 loss: 1.8158 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8158 2023/04/14 00:29:23 - mmengine - INFO - Epoch(train) [22][1220/1879] lr: 2.0000e-02 eta: 15:07:54 time: 0.3071 data_time: 0.0134 memory: 6717 grad_norm: 2.8749 loss: 1.6920 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6920 2023/04/14 00:29:31 - mmengine - INFO - Epoch(train) [22][1240/1879] lr: 2.0000e-02 eta: 15:07:49 time: 0.4044 data_time: 0.0159 memory: 6717 grad_norm: 2.7769 loss: 1.7798 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7798 2023/04/14 00:29:37 - mmengine - INFO - Epoch(train) [22][1260/1879] lr: 2.0000e-02 eta: 15:07:39 time: 0.3320 data_time: 0.0127 memory: 6717 grad_norm: 2.8606 loss: 1.8640 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.8640 2023/04/14 00:29:46 - mmengine - INFO - Epoch(train) [22][1280/1879] lr: 2.0000e-02 eta: 15:07:35 time: 0.4216 data_time: 0.0157 memory: 6717 grad_norm: 2.7281 loss: 1.6578 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6578 2023/04/14 00:29:52 - mmengine - INFO - Epoch(train) [22][1300/1879] lr: 2.0000e-02 eta: 15:07:25 time: 0.3255 data_time: 0.0131 memory: 6717 grad_norm: 2.8428 loss: 1.7921 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7921 2023/04/14 00:30:00 - mmengine - INFO - Epoch(train) [22][1320/1879] lr: 2.0000e-02 eta: 15:07:19 time: 0.3965 data_time: 0.0159 memory: 6717 grad_norm: 2.8120 loss: 1.6721 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6721 2023/04/14 00:30:07 - mmengine - INFO - Epoch(train) [22][1340/1879] lr: 2.0000e-02 eta: 15:07:10 time: 0.3405 data_time: 0.0126 memory: 6717 grad_norm: 2.7501 loss: 1.7344 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7344 2023/04/14 00:30:15 - mmengine - INFO - Epoch(train) [22][1360/1879] lr: 2.0000e-02 eta: 15:07:05 time: 0.4050 data_time: 0.0148 memory: 6717 grad_norm: 3.2084 loss: 1.7918 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7918 2023/04/14 00:30:22 - mmengine - INFO - Epoch(train) [22][1380/1879] lr: 2.0000e-02 eta: 15:06:53 time: 0.3139 data_time: 0.0138 memory: 6717 grad_norm: 2.8064 loss: 1.8022 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8022 2023/04/14 00:30:31 - mmengine - INFO - Epoch(train) [22][1400/1879] lr: 2.0000e-02 eta: 15:06:53 time: 0.4711 data_time: 0.0158 memory: 6717 grad_norm: 2.8427 loss: 1.7766 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.7766 2023/04/14 00:30:37 - mmengine - INFO - Epoch(train) [22][1420/1879] lr: 2.0000e-02 eta: 15:06:42 time: 0.3166 data_time: 0.0137 memory: 6717 grad_norm: 2.7502 loss: 1.8021 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8021 2023/04/14 00:30:45 - mmengine - INFO - Epoch(train) [22][1440/1879] lr: 2.0000e-02 eta: 15:06:37 time: 0.4006 data_time: 0.0149 memory: 6717 grad_norm: 2.7782 loss: 1.9815 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9815 2023/04/14 00:30:52 - mmengine - INFO - Epoch(train) [22][1460/1879] lr: 2.0000e-02 eta: 15:06:28 time: 0.3494 data_time: 0.0154 memory: 6717 grad_norm: 2.9929 loss: 1.9246 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.9246 2023/04/14 00:31:00 - mmengine - INFO - Epoch(train) [22][1480/1879] lr: 2.0000e-02 eta: 15:06:21 time: 0.3753 data_time: 0.0138 memory: 6717 grad_norm: 2.8166 loss: 1.8114 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8114 2023/04/14 00:31:06 - mmengine - INFO - Epoch(train) [22][1500/1879] lr: 2.0000e-02 eta: 15:06:09 time: 0.3169 data_time: 0.0142 memory: 6717 grad_norm: 2.7710 loss: 1.9321 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9321 2023/04/14 00:31:15 - mmengine - INFO - Epoch(train) [22][1520/1879] lr: 2.0000e-02 eta: 15:06:06 time: 0.4251 data_time: 0.0147 memory: 6717 grad_norm: 2.8453 loss: 1.6712 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6712 2023/04/14 00:31:21 - mmengine - INFO - Epoch(train) [22][1540/1879] lr: 2.0000e-02 eta: 15:05:56 time: 0.3344 data_time: 0.0132 memory: 6717 grad_norm: 2.7956 loss: 1.7897 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7897 2023/04/14 00:31:22 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 00:31:29 - mmengine - INFO - Epoch(train) [22][1560/1879] lr: 2.0000e-02 eta: 15:05:49 time: 0.3699 data_time: 0.0146 memory: 6717 grad_norm: 2.8242 loss: 1.7352 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.7352 2023/04/14 00:31:35 - mmengine - INFO - Epoch(train) [22][1580/1879] lr: 2.0000e-02 eta: 15:05:37 time: 0.3123 data_time: 0.0136 memory: 6717 grad_norm: 2.8792 loss: 2.0241 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 2.0241 2023/04/14 00:31:44 - mmengine - INFO - Epoch(train) [22][1600/1879] lr: 2.0000e-02 eta: 15:05:35 time: 0.4378 data_time: 0.0160 memory: 6717 grad_norm: 2.8330 loss: 1.7371 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7371 2023/04/14 00:31:51 - mmengine - INFO - Epoch(train) [22][1620/1879] lr: 2.0000e-02 eta: 15:05:26 time: 0.3473 data_time: 0.0131 memory: 6717 grad_norm: 2.7500 loss: 1.6849 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6849 2023/04/14 00:31:59 - mmengine - INFO - Epoch(train) [22][1640/1879] lr: 2.0000e-02 eta: 15:05:21 time: 0.4122 data_time: 0.0140 memory: 6717 grad_norm: 2.7567 loss: 1.6910 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.6910 2023/04/14 00:32:05 - mmengine - INFO - Epoch(train) [22][1660/1879] lr: 2.0000e-02 eta: 15:05:10 time: 0.3161 data_time: 0.0136 memory: 6717 grad_norm: 2.8369 loss: 1.7804 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7804 2023/04/14 00:32:13 - mmengine - INFO - Epoch(train) [22][1680/1879] lr: 2.0000e-02 eta: 15:05:05 time: 0.4009 data_time: 0.0136 memory: 6717 grad_norm: 2.7721 loss: 1.7784 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7784 2023/04/14 00:32:21 - mmengine - INFO - Epoch(train) [22][1700/1879] lr: 2.0000e-02 eta: 15:04:57 time: 0.3626 data_time: 0.0144 memory: 6717 grad_norm: 2.8551 loss: 1.7268 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7268 2023/04/14 00:32:28 - mmengine - INFO - Epoch(train) [22][1720/1879] lr: 2.0000e-02 eta: 15:04:51 time: 0.3940 data_time: 0.0143 memory: 6717 grad_norm: 2.8477 loss: 1.7807 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7807 2023/04/14 00:32:35 - mmengine - INFO - Epoch(train) [22][1740/1879] lr: 2.0000e-02 eta: 15:04:41 time: 0.3322 data_time: 0.0149 memory: 6717 grad_norm: 2.7194 loss: 1.8097 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8097 2023/04/14 00:32:44 - mmengine - INFO - Epoch(train) [22][1760/1879] lr: 2.0000e-02 eta: 15:04:38 time: 0.4256 data_time: 0.0139 memory: 6717 grad_norm: 2.7323 loss: 1.6610 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.6610 2023/04/14 00:32:50 - mmengine - INFO - Epoch(train) [22][1780/1879] lr: 2.0000e-02 eta: 15:04:28 time: 0.3342 data_time: 0.0135 memory: 6717 grad_norm: 2.7146 loss: 1.9974 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.9974 2023/04/14 00:32:58 - mmengine - INFO - Epoch(train) [22][1800/1879] lr: 2.0000e-02 eta: 15:04:22 time: 0.3905 data_time: 0.0132 memory: 6717 grad_norm: 2.7556 loss: 1.7413 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.7413 2023/04/14 00:33:05 - mmengine - INFO - Epoch(train) [22][1820/1879] lr: 2.0000e-02 eta: 15:04:11 time: 0.3236 data_time: 0.0151 memory: 6717 grad_norm: 2.8245 loss: 1.7005 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.7005 2023/04/14 00:33:13 - mmengine - INFO - Epoch(train) [22][1840/1879] lr: 2.0000e-02 eta: 15:04:07 time: 0.4198 data_time: 0.0139 memory: 6717 grad_norm: 2.7459 loss: 1.8861 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8861 2023/04/14 00:33:19 - mmengine - INFO - Epoch(train) [22][1860/1879] lr: 2.0000e-02 eta: 15:03:56 time: 0.3222 data_time: 0.0143 memory: 6717 grad_norm: 2.7837 loss: 1.8546 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8546 2023/04/14 00:33:25 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 00:33:25 - mmengine - INFO - Epoch(train) [22][1879/1879] lr: 2.0000e-02 eta: 15:03:44 time: 0.2773 data_time: 0.0126 memory: 6717 grad_norm: 3.8172 loss: 1.9265 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 1.9265 2023/04/14 00:33:34 - mmengine - INFO - Epoch(val) [22][ 20/155] eta: 0:01:00 time: 0.4489 data_time: 0.4159 memory: 1391 2023/04/14 00:33:40 - mmengine - INFO - Epoch(val) [22][ 40/155] eta: 0:00:44 time: 0.3201 data_time: 0.2870 memory: 1391 2023/04/14 00:33:49 - mmengine - INFO - Epoch(val) [22][ 60/155] eta: 0:00:38 time: 0.4415 data_time: 0.4080 memory: 1391 2023/04/14 00:33:55 - mmengine - INFO - Epoch(val) [22][ 80/155] eta: 0:00:28 time: 0.3148 data_time: 0.2815 memory: 1391 2023/04/14 00:34:04 - mmengine - INFO - Epoch(val) [22][100/155] eta: 0:00:21 time: 0.4546 data_time: 0.4215 memory: 1391 2023/04/14 00:34:10 - mmengine - INFO - Epoch(val) [22][120/155] eta: 0:00:13 time: 0.2948 data_time: 0.2613 memory: 1391 2023/04/14 00:34:20 - mmengine - INFO - Epoch(val) [22][140/155] eta: 0:00:05 time: 0.4830 data_time: 0.4500 memory: 1391 2023/04/14 00:34:27 - mmengine - INFO - Epoch(val) [22][155/155] acc/top1: 0.5896 acc/top5: 0.8253 acc/mean1: 0.5897 data_time: 0.4192 time: 0.4509 2023/04/14 00:34:27 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_21.pth is removed 2023/04/14 00:34:28 - mmengine - INFO - The best checkpoint with 0.5896 acc/top1 at 22 epoch is saved to best_acc_top1_epoch_22.pth. 2023/04/14 00:34:37 - mmengine - INFO - Epoch(train) [23][ 20/1879] lr: 2.0000e-02 eta: 15:03:42 time: 0.4555 data_time: 0.3052 memory: 6717 grad_norm: 2.9025 loss: 1.8206 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.8206 2023/04/14 00:34:43 - mmengine - INFO - Epoch(train) [23][ 40/1879] lr: 2.0000e-02 eta: 15:03:32 time: 0.3257 data_time: 0.1795 memory: 6717 grad_norm: 2.8262 loss: 1.8076 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8076 2023/04/14 00:34:52 - mmengine - INFO - Epoch(train) [23][ 60/1879] lr: 2.0000e-02 eta: 15:03:30 time: 0.4568 data_time: 0.2218 memory: 6717 grad_norm: 2.8062 loss: 1.7995 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7995 2023/04/14 00:34:59 - mmengine - INFO - Epoch(train) [23][ 80/1879] lr: 2.0000e-02 eta: 15:03:20 time: 0.3222 data_time: 0.0989 memory: 6717 grad_norm: 2.8031 loss: 1.7677 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7677 2023/04/14 00:35:07 - mmengine - INFO - Epoch(train) [23][ 100/1879] lr: 2.0000e-02 eta: 15:03:15 time: 0.4050 data_time: 0.1099 memory: 6717 grad_norm: 2.8809 loss: 1.9846 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9846 2023/04/14 00:35:13 - mmengine - INFO - Epoch(train) [23][ 120/1879] lr: 2.0000e-02 eta: 15:03:03 time: 0.3149 data_time: 0.0770 memory: 6717 grad_norm: 2.7348 loss: 1.9132 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.9132 2023/04/14 00:35:21 - mmengine - INFO - Epoch(train) [23][ 140/1879] lr: 2.0000e-02 eta: 15:02:58 time: 0.3933 data_time: 0.0147 memory: 6717 grad_norm: 2.7864 loss: 1.6095 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6095 2023/04/14 00:35:28 - mmengine - INFO - Epoch(train) [23][ 160/1879] lr: 2.0000e-02 eta: 15:02:48 time: 0.3377 data_time: 0.0303 memory: 6717 grad_norm: 2.8882 loss: 2.0164 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 2.0164 2023/04/14 00:35:36 - mmengine - INFO - Epoch(train) [23][ 180/1879] lr: 2.0000e-02 eta: 15:02:43 time: 0.4064 data_time: 0.0138 memory: 6717 grad_norm: 2.8169 loss: 1.7476 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7476 2023/04/14 00:35:43 - mmengine - INFO - Epoch(train) [23][ 200/1879] lr: 2.0000e-02 eta: 15:02:35 time: 0.3660 data_time: 0.0141 memory: 6717 grad_norm: 2.8156 loss: 1.7736 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7736 2023/04/14 00:35:51 - mmengine - INFO - Epoch(train) [23][ 220/1879] lr: 2.0000e-02 eta: 15:02:29 time: 0.3770 data_time: 0.0145 memory: 6717 grad_norm: 2.7892 loss: 1.5428 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5428 2023/04/14 00:35:58 - mmengine - INFO - Epoch(train) [23][ 240/1879] lr: 2.0000e-02 eta: 15:02:19 time: 0.3364 data_time: 0.0289 memory: 6717 grad_norm: 2.7590 loss: 1.6534 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6534 2023/04/14 00:36:05 - mmengine - INFO - Epoch(train) [23][ 260/1879] lr: 2.0000e-02 eta: 15:02:13 time: 0.3900 data_time: 0.0243 memory: 6717 grad_norm: 2.8257 loss: 1.9052 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.9052 2023/04/14 00:36:13 - mmengine - INFO - Epoch(train) [23][ 280/1879] lr: 2.0000e-02 eta: 15:02:05 time: 0.3638 data_time: 0.0150 memory: 6717 grad_norm: 2.8756 loss: 1.8409 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8409 2023/04/14 00:36:20 - mmengine - INFO - Epoch(train) [23][ 300/1879] lr: 2.0000e-02 eta: 15:01:56 time: 0.3546 data_time: 0.0397 memory: 6717 grad_norm: 2.8358 loss: 2.0162 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0162 2023/04/14 00:36:27 - mmengine - INFO - Epoch(train) [23][ 320/1879] lr: 2.0000e-02 eta: 15:01:50 time: 0.3855 data_time: 0.1025 memory: 6717 grad_norm: 2.7883 loss: 1.7777 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.7777 2023/04/14 00:36:34 - mmengine - INFO - Epoch(train) [23][ 340/1879] lr: 2.0000e-02 eta: 15:01:40 time: 0.3348 data_time: 0.0664 memory: 6717 grad_norm: 2.7715 loss: 2.1052 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 2.1052 2023/04/14 00:36:42 - mmengine - INFO - Epoch(train) [23][ 360/1879] lr: 2.0000e-02 eta: 15:01:35 time: 0.4030 data_time: 0.0901 memory: 6717 grad_norm: 2.7791 loss: 1.8139 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8139 2023/04/14 00:36:49 - mmengine - INFO - Epoch(train) [23][ 380/1879] lr: 2.0000e-02 eta: 15:01:24 time: 0.3177 data_time: 0.0435 memory: 6717 grad_norm: 2.7530 loss: 1.8228 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.8228 2023/04/14 00:36:57 - mmengine - INFO - Epoch(train) [23][ 400/1879] lr: 2.0000e-02 eta: 15:01:21 time: 0.4239 data_time: 0.0445 memory: 6717 grad_norm: 2.8947 loss: 2.0696 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 2.0696 2023/04/14 00:37:04 - mmengine - INFO - Epoch(train) [23][ 420/1879] lr: 2.0000e-02 eta: 15:01:10 time: 0.3298 data_time: 0.0338 memory: 6717 grad_norm: 2.8020 loss: 1.6253 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6253 2023/04/14 00:37:12 - mmengine - INFO - Epoch(train) [23][ 440/1879] lr: 2.0000e-02 eta: 15:01:05 time: 0.3931 data_time: 0.0450 memory: 6717 grad_norm: 2.7689 loss: 1.6434 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6434 2023/04/14 00:37:19 - mmengine - INFO - Epoch(train) [23][ 460/1879] lr: 2.0000e-02 eta: 15:00:56 time: 0.3509 data_time: 0.0368 memory: 6717 grad_norm: 2.8139 loss: 1.8001 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8001 2023/04/14 00:37:26 - mmengine - INFO - Epoch(train) [23][ 480/1879] lr: 2.0000e-02 eta: 15:00:49 time: 0.3821 data_time: 0.0553 memory: 6717 grad_norm: 2.7485 loss: 2.0834 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 2.0834 2023/04/14 00:37:33 - mmengine - INFO - Epoch(train) [23][ 500/1879] lr: 2.0000e-02 eta: 15:00:40 time: 0.3413 data_time: 0.0447 memory: 6717 grad_norm: 2.7662 loss: 1.7248 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7248 2023/04/14 00:37:41 - mmengine - INFO - Epoch(train) [23][ 520/1879] lr: 2.0000e-02 eta: 15:00:36 time: 0.4172 data_time: 0.0789 memory: 6717 grad_norm: 2.8496 loss: 1.9153 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.9153 2023/04/14 00:37:48 - mmengine - INFO - Epoch(train) [23][ 540/1879] lr: 2.0000e-02 eta: 15:00:25 time: 0.3198 data_time: 0.0256 memory: 6717 grad_norm: 2.9820 loss: 1.6239 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6239 2023/04/14 00:37:56 - mmengine - INFO - Epoch(train) [23][ 560/1879] lr: 2.0000e-02 eta: 15:00:21 time: 0.4260 data_time: 0.0153 memory: 6717 grad_norm: 2.7601 loss: 1.8980 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8980 2023/04/14 00:38:03 - mmengine - INFO - Epoch(train) [23][ 580/1879] lr: 2.0000e-02 eta: 15:00:11 time: 0.3339 data_time: 0.0123 memory: 6717 grad_norm: 2.7911 loss: 1.8159 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8159 2023/04/14 00:38:11 - mmengine - INFO - Epoch(train) [23][ 600/1879] lr: 2.0000e-02 eta: 15:00:05 time: 0.3857 data_time: 0.0138 memory: 6717 grad_norm: 2.8275 loss: 2.0054 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0054 2023/04/14 00:38:18 - mmengine - INFO - Epoch(train) [23][ 620/1879] lr: 2.0000e-02 eta: 14:59:56 time: 0.3521 data_time: 0.0126 memory: 6717 grad_norm: 2.8470 loss: 1.6964 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6964 2023/04/14 00:38:25 - mmengine - INFO - Epoch(train) [23][ 640/1879] lr: 2.0000e-02 eta: 14:59:49 time: 0.3751 data_time: 0.0159 memory: 6717 grad_norm: 2.8148 loss: 1.7095 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7095 2023/04/14 00:38:32 - mmengine - INFO - Epoch(train) [23][ 660/1879] lr: 2.0000e-02 eta: 14:59:40 time: 0.3478 data_time: 0.0144 memory: 6717 grad_norm: 2.7400 loss: 1.8848 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 1.8848 2023/04/14 00:38:32 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 00:38:40 - mmengine - INFO - Epoch(train) [23][ 680/1879] lr: 2.0000e-02 eta: 14:59:33 time: 0.3733 data_time: 0.0148 memory: 6717 grad_norm: 2.8048 loss: 1.7088 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7088 2023/04/14 00:38:47 - mmengine - INFO - Epoch(train) [23][ 700/1879] lr: 2.0000e-02 eta: 14:59:25 time: 0.3535 data_time: 0.0120 memory: 6717 grad_norm: 2.7825 loss: 1.5671 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5671 2023/04/14 00:38:55 - mmengine - INFO - Epoch(train) [23][ 720/1879] lr: 2.0000e-02 eta: 14:59:20 time: 0.4036 data_time: 0.0147 memory: 6717 grad_norm: 2.8675 loss: 1.6933 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.6933 2023/04/14 00:39:02 - mmengine - INFO - Epoch(train) [23][ 740/1879] lr: 2.0000e-02 eta: 14:59:12 time: 0.3661 data_time: 0.0132 memory: 6717 grad_norm: 2.8007 loss: 1.5769 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5769 2023/04/14 00:39:09 - mmengine - INFO - Epoch(train) [23][ 760/1879] lr: 2.0000e-02 eta: 14:59:04 time: 0.3541 data_time: 0.0167 memory: 6717 grad_norm: 2.8298 loss: 1.6352 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6352 2023/04/14 00:39:17 - mmengine - INFO - Epoch(train) [23][ 780/1879] lr: 2.0000e-02 eta: 14:58:59 time: 0.4071 data_time: 0.0120 memory: 6717 grad_norm: 2.7657 loss: 1.8254 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8254 2023/04/14 00:39:24 - mmengine - INFO - Epoch(train) [23][ 800/1879] lr: 2.0000e-02 eta: 14:58:48 time: 0.3168 data_time: 0.0149 memory: 6717 grad_norm: 2.8429 loss: 1.7400 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7400 2023/04/14 00:39:32 - mmengine - INFO - Epoch(train) [23][ 820/1879] lr: 2.0000e-02 eta: 14:58:43 time: 0.4100 data_time: 0.0142 memory: 6717 grad_norm: 2.7488 loss: 1.8688 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8688 2023/04/14 00:39:38 - mmengine - INFO - Epoch(train) [23][ 840/1879] lr: 2.0000e-02 eta: 14:58:31 time: 0.3061 data_time: 0.0149 memory: 6717 grad_norm: 2.7506 loss: 1.8642 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8642 2023/04/14 00:39:46 - mmengine - INFO - Epoch(train) [23][ 860/1879] lr: 2.0000e-02 eta: 14:58:26 time: 0.3979 data_time: 0.0135 memory: 6717 grad_norm: 2.7805 loss: 2.0286 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0286 2023/04/14 00:39:53 - mmengine - INFO - Epoch(train) [23][ 880/1879] lr: 2.0000e-02 eta: 14:58:16 time: 0.3392 data_time: 0.0155 memory: 6717 grad_norm: 2.7928 loss: 1.6467 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6467 2023/04/14 00:40:01 - mmengine - INFO - Epoch(train) [23][ 900/1879] lr: 2.0000e-02 eta: 14:58:13 time: 0.4243 data_time: 0.0123 memory: 6717 grad_norm: 2.8385 loss: 1.9590 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9590 2023/04/14 00:40:09 - mmengine - INFO - Epoch(train) [23][ 920/1879] lr: 2.0000e-02 eta: 14:58:05 time: 0.3636 data_time: 0.0146 memory: 6717 grad_norm: 2.7721 loss: 1.7770 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7770 2023/04/14 00:40:15 - mmengine - INFO - Epoch(train) [23][ 940/1879] lr: 2.0000e-02 eta: 14:57:54 time: 0.3294 data_time: 0.0145 memory: 6717 grad_norm: 2.7609 loss: 1.8166 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8166 2023/04/14 00:40:23 - mmengine - INFO - Epoch(train) [23][ 960/1879] lr: 2.0000e-02 eta: 14:57:48 time: 0.3759 data_time: 0.0155 memory: 6717 grad_norm: 2.8577 loss: 1.8280 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8280 2023/04/14 00:40:30 - mmengine - INFO - Epoch(train) [23][ 980/1879] lr: 2.0000e-02 eta: 14:57:42 time: 0.3931 data_time: 0.0139 memory: 6717 grad_norm: 2.7636 loss: 1.9636 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9636 2023/04/14 00:40:37 - mmengine - INFO - Epoch(train) [23][1000/1879] lr: 2.0000e-02 eta: 14:57:31 time: 0.3177 data_time: 0.0148 memory: 6717 grad_norm: 2.7383 loss: 1.7839 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.7839 2023/04/14 00:40:46 - mmengine - INFO - Epoch(train) [23][1020/1879] lr: 2.0000e-02 eta: 14:57:29 time: 0.4512 data_time: 0.0132 memory: 6717 grad_norm: 2.7546 loss: 1.8296 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 1.8296 2023/04/14 00:40:52 - mmengine - INFO - Epoch(train) [23][1040/1879] lr: 2.0000e-02 eta: 14:57:19 time: 0.3289 data_time: 0.0153 memory: 6717 grad_norm: 2.7569 loss: 1.8736 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8736 2023/04/14 00:41:01 - mmengine - INFO - Epoch(train) [23][1060/1879] lr: 2.0000e-02 eta: 14:57:14 time: 0.4114 data_time: 0.0130 memory: 6717 grad_norm: 2.7801 loss: 1.7749 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7749 2023/04/14 00:41:07 - mmengine - INFO - Epoch(train) [23][1080/1879] lr: 2.0000e-02 eta: 14:57:03 time: 0.3152 data_time: 0.0166 memory: 6717 grad_norm: 2.6958 loss: 1.6371 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6371 2023/04/14 00:41:14 - mmengine - INFO - Epoch(train) [23][1100/1879] lr: 2.0000e-02 eta: 14:56:55 time: 0.3631 data_time: 0.0144 memory: 6717 grad_norm: 2.8257 loss: 1.9697 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.9697 2023/04/14 00:41:22 - mmengine - INFO - Epoch(train) [23][1120/1879] lr: 2.0000e-02 eta: 14:56:49 time: 0.3851 data_time: 0.0149 memory: 6717 grad_norm: 2.8266 loss: 1.5294 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5294 2023/04/14 00:41:29 - mmengine - INFO - Epoch(train) [23][1140/1879] lr: 2.0000e-02 eta: 14:56:40 time: 0.3504 data_time: 0.0130 memory: 6717 grad_norm: 2.8310 loss: 1.6436 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.6436 2023/04/14 00:41:37 - mmengine - INFO - Epoch(train) [23][1160/1879] lr: 2.0000e-02 eta: 14:56:35 time: 0.4015 data_time: 0.0151 memory: 6717 grad_norm: 2.8272 loss: 1.6659 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6659 2023/04/14 00:41:44 - mmengine - INFO - Epoch(train) [23][1180/1879] lr: 2.0000e-02 eta: 14:56:26 time: 0.3509 data_time: 0.0129 memory: 6717 grad_norm: 2.7280 loss: 1.6903 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6903 2023/04/14 00:41:52 - mmengine - INFO - Epoch(train) [23][1200/1879] lr: 2.0000e-02 eta: 14:56:21 time: 0.3977 data_time: 0.0152 memory: 6717 grad_norm: 2.7598 loss: 1.8013 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8013 2023/04/14 00:41:58 - mmengine - INFO - Epoch(train) [23][1220/1879] lr: 2.0000e-02 eta: 14:56:09 time: 0.3114 data_time: 0.0165 memory: 6717 grad_norm: 2.7361 loss: 1.8424 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8424 2023/04/14 00:42:07 - mmengine - INFO - Epoch(train) [23][1240/1879] lr: 2.0000e-02 eta: 14:56:06 time: 0.4271 data_time: 0.0468 memory: 6717 grad_norm: 2.7182 loss: 2.0124 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 2.0124 2023/04/14 00:42:13 - mmengine - INFO - Epoch(train) [23][1260/1879] lr: 2.0000e-02 eta: 14:55:54 time: 0.3106 data_time: 0.0129 memory: 6717 grad_norm: 2.8149 loss: 1.7991 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7991 2023/04/14 00:42:20 - mmengine - INFO - Epoch(train) [23][1280/1879] lr: 2.0000e-02 eta: 14:55:47 time: 0.3697 data_time: 0.1249 memory: 6717 grad_norm: 2.6956 loss: 1.9574 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.9574 2023/04/14 00:42:27 - mmengine - INFO - Epoch(train) [23][1300/1879] lr: 2.0000e-02 eta: 14:55:37 time: 0.3304 data_time: 0.1177 memory: 6717 grad_norm: 2.7585 loss: 1.8170 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8170 2023/04/14 00:42:35 - mmengine - INFO - Epoch(train) [23][1320/1879] lr: 2.0000e-02 eta: 14:55:32 time: 0.4093 data_time: 0.2106 memory: 6717 grad_norm: 2.7342 loss: 1.8192 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8192 2023/04/14 00:42:42 - mmengine - INFO - Epoch(train) [23][1340/1879] lr: 2.0000e-02 eta: 14:55:23 time: 0.3520 data_time: 0.1193 memory: 6717 grad_norm: 2.8366 loss: 1.8121 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8121 2023/04/14 00:42:50 - mmengine - INFO - Epoch(train) [23][1360/1879] lr: 2.0000e-02 eta: 14:55:19 time: 0.4121 data_time: 0.1849 memory: 6717 grad_norm: 2.7289 loss: 1.7993 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7993 2023/04/14 00:42:57 - mmengine - INFO - Epoch(train) [23][1380/1879] lr: 2.0000e-02 eta: 14:55:09 time: 0.3419 data_time: 0.1918 memory: 6717 grad_norm: 2.8724 loss: 1.9665 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.9665 2023/04/14 00:43:06 - mmengine - INFO - Epoch(train) [23][1400/1879] lr: 2.0000e-02 eta: 14:55:06 time: 0.4224 data_time: 0.2797 memory: 6717 grad_norm: 2.7335 loss: 1.7058 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7058 2023/04/14 00:43:12 - mmengine - INFO - Epoch(train) [23][1420/1879] lr: 2.0000e-02 eta: 14:54:55 time: 0.3147 data_time: 0.1749 memory: 6717 grad_norm: 2.8012 loss: 1.7436 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7436 2023/04/14 00:43:20 - mmengine - INFO - Epoch(train) [23][1440/1879] lr: 2.0000e-02 eta: 14:54:49 time: 0.4009 data_time: 0.2485 memory: 6717 grad_norm: 2.8174 loss: 1.9354 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9354 2023/04/14 00:43:26 - mmengine - INFO - Epoch(train) [23][1460/1879] lr: 2.0000e-02 eta: 14:54:38 time: 0.3211 data_time: 0.1566 memory: 6717 grad_norm: 2.7716 loss: 2.0193 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 2.0193 2023/04/14 00:43:35 - mmengine - INFO - Epoch(train) [23][1480/1879] lr: 2.0000e-02 eta: 14:54:35 time: 0.4244 data_time: 0.1291 memory: 6717 grad_norm: 2.7940 loss: 1.8255 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8255 2023/04/14 00:43:42 - mmengine - INFO - Epoch(train) [23][1500/1879] lr: 2.0000e-02 eta: 14:54:25 time: 0.3286 data_time: 0.0739 memory: 6717 grad_norm: 2.7247 loss: 1.7918 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 1.7918 2023/04/14 00:43:50 - mmengine - INFO - Epoch(train) [23][1520/1879] lr: 2.0000e-02 eta: 14:54:20 time: 0.4145 data_time: 0.2338 memory: 6717 grad_norm: 2.8484 loss: 1.9943 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9943 2023/04/14 00:43:56 - mmengine - INFO - Epoch(train) [23][1540/1879] lr: 2.0000e-02 eta: 14:54:09 time: 0.3128 data_time: 0.1558 memory: 6717 grad_norm: 2.8082 loss: 1.7578 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7578 2023/04/14 00:44:05 - mmengine - INFO - Epoch(train) [23][1560/1879] lr: 2.0000e-02 eta: 14:54:05 time: 0.4267 data_time: 0.1777 memory: 6717 grad_norm: 2.8286 loss: 1.6522 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6522 2023/04/14 00:44:11 - mmengine - INFO - Epoch(train) [23][1580/1879] lr: 2.0000e-02 eta: 14:53:54 time: 0.3120 data_time: 0.0754 memory: 6717 grad_norm: 2.7600 loss: 1.8601 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8601 2023/04/14 00:44:19 - mmengine - INFO - Epoch(train) [23][1600/1879] lr: 2.0000e-02 eta: 14:53:48 time: 0.3890 data_time: 0.1369 memory: 6717 grad_norm: 2.7722 loss: 1.8837 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.8837 2023/04/14 00:44:26 - mmengine - INFO - Epoch(train) [23][1620/1879] lr: 2.0000e-02 eta: 14:53:40 time: 0.3584 data_time: 0.0610 memory: 6717 grad_norm: 2.8698 loss: 1.8880 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8880 2023/04/14 00:44:33 - mmengine - INFO - Epoch(train) [23][1640/1879] lr: 2.0000e-02 eta: 14:53:31 time: 0.3498 data_time: 0.0214 memory: 6717 grad_norm: 2.7579 loss: 1.9757 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9757 2023/04/14 00:44:40 - mmengine - INFO - Epoch(train) [23][1660/1879] lr: 2.0000e-02 eta: 14:53:24 time: 0.3793 data_time: 0.0126 memory: 6717 grad_norm: 2.8340 loss: 1.7278 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7278 2023/04/14 00:44:41 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 00:44:47 - mmengine - INFO - Epoch(train) [23][1680/1879] lr: 2.0000e-02 eta: 14:53:15 time: 0.3452 data_time: 0.0268 memory: 6717 grad_norm: 2.7447 loss: 1.8837 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8837 2023/04/14 00:44:55 - mmengine - INFO - Epoch(train) [23][1700/1879] lr: 2.0000e-02 eta: 14:53:10 time: 0.4053 data_time: 0.0406 memory: 6717 grad_norm: 2.8465 loss: 1.6569 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6569 2023/04/14 00:45:02 - mmengine - INFO - Epoch(train) [23][1720/1879] lr: 2.0000e-02 eta: 14:53:01 time: 0.3447 data_time: 0.0349 memory: 6717 grad_norm: 2.7474 loss: 1.5982 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5982 2023/04/14 00:45:10 - mmengine - INFO - Epoch(train) [23][1740/1879] lr: 2.0000e-02 eta: 14:52:56 time: 0.3983 data_time: 0.0139 memory: 6717 grad_norm: 2.7728 loss: 1.6635 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6635 2023/04/14 00:45:18 - mmengine - INFO - Epoch(train) [23][1760/1879] lr: 2.0000e-02 eta: 14:52:48 time: 0.3724 data_time: 0.0161 memory: 6717 grad_norm: 2.7563 loss: 1.6355 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6355 2023/04/14 00:45:25 - mmengine - INFO - Epoch(train) [23][1780/1879] lr: 2.0000e-02 eta: 14:52:42 time: 0.3893 data_time: 0.0125 memory: 6717 grad_norm: 2.8177 loss: 1.8644 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.8644 2023/04/14 00:45:32 - mmengine - INFO - Epoch(train) [23][1800/1879] lr: 2.0000e-02 eta: 14:52:32 time: 0.3277 data_time: 0.0150 memory: 6717 grad_norm: 2.8195 loss: 1.7262 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7262 2023/04/14 00:45:40 - mmengine - INFO - Epoch(train) [23][1820/1879] lr: 2.0000e-02 eta: 14:52:28 time: 0.4184 data_time: 0.0127 memory: 6717 grad_norm: 2.7058 loss: 1.8465 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.8465 2023/04/14 00:45:47 - mmengine - INFO - Epoch(train) [23][1840/1879] lr: 2.0000e-02 eta: 14:52:18 time: 0.3271 data_time: 0.0148 memory: 6717 grad_norm: 2.8324 loss: 1.9649 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9649 2023/04/14 00:45:55 - mmengine - INFO - Epoch(train) [23][1860/1879] lr: 2.0000e-02 eta: 14:52:11 time: 0.3804 data_time: 0.0156 memory: 6717 grad_norm: 2.7442 loss: 1.6679 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.6679 2023/04/14 00:46:00 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 00:46:00 - mmengine - INFO - Epoch(train) [23][1879/1879] lr: 2.0000e-02 eta: 14:52:00 time: 0.2973 data_time: 0.0126 memory: 6717 grad_norm: 2.7711 loss: 1.8749 top1_acc: 0.4286 top5_acc: 0.5714 loss_cls: 1.8749 2023/04/14 00:46:10 - mmengine - INFO - Epoch(val) [23][ 20/155] eta: 0:01:01 time: 0.4591 data_time: 0.4263 memory: 1391 2023/04/14 00:46:16 - mmengine - INFO - Epoch(val) [23][ 40/155] eta: 0:00:44 time: 0.3080 data_time: 0.2752 memory: 1391 2023/04/14 00:46:24 - mmengine - INFO - Epoch(val) [23][ 60/155] eta: 0:00:37 time: 0.4306 data_time: 0.3969 memory: 1391 2023/04/14 00:46:31 - mmengine - INFO - Epoch(val) [23][ 80/155] eta: 0:00:28 time: 0.3231 data_time: 0.2904 memory: 1391 2023/04/14 00:46:39 - mmengine - INFO - Epoch(val) [23][100/155] eta: 0:00:21 time: 0.4167 data_time: 0.3835 memory: 1391 2023/04/14 00:46:46 - mmengine - INFO - Epoch(val) [23][120/155] eta: 0:00:13 time: 0.3387 data_time: 0.3057 memory: 1391 2023/04/14 00:46:56 - mmengine - INFO - Epoch(val) [23][140/155] eta: 0:00:05 time: 0.4840 data_time: 0.4513 memory: 1391 2023/04/14 00:47:03 - mmengine - INFO - Epoch(val) [23][155/155] acc/top1: 0.5871 acc/top5: 0.8253 acc/mean1: 0.5871 data_time: 0.4194 time: 0.4516 2023/04/14 00:47:13 - mmengine - INFO - Epoch(train) [24][ 20/1879] lr: 2.0000e-02 eta: 14:52:00 time: 0.4925 data_time: 0.3202 memory: 6717 grad_norm: 2.8068 loss: 1.7320 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7320 2023/04/14 00:47:19 - mmengine - INFO - Epoch(train) [24][ 40/1879] lr: 2.0000e-02 eta: 14:51:51 time: 0.3335 data_time: 0.0905 memory: 6717 grad_norm: 2.8357 loss: 1.8127 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.8127 2023/04/14 00:47:28 - mmengine - INFO - Epoch(train) [24][ 60/1879] lr: 2.0000e-02 eta: 14:51:47 time: 0.4291 data_time: 0.0814 memory: 6717 grad_norm: 2.8139 loss: 1.6894 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6894 2023/04/14 00:47:34 - mmengine - INFO - Epoch(train) [24][ 80/1879] lr: 2.0000e-02 eta: 14:51:37 time: 0.3215 data_time: 0.0133 memory: 6717 grad_norm: 2.7510 loss: 1.5732 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.5732 2023/04/14 00:47:42 - mmengine - INFO - Epoch(train) [24][ 100/1879] lr: 2.0000e-02 eta: 14:51:30 time: 0.3880 data_time: 0.0360 memory: 6717 grad_norm: 2.8078 loss: 1.7160 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7160 2023/04/14 00:47:48 - mmengine - INFO - Epoch(train) [24][ 120/1879] lr: 2.0000e-02 eta: 14:51:20 time: 0.3211 data_time: 0.0379 memory: 6717 grad_norm: 2.8369 loss: 1.6863 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6863 2023/04/14 00:47:58 - mmengine - INFO - Epoch(train) [24][ 140/1879] lr: 2.0000e-02 eta: 14:51:18 time: 0.4550 data_time: 0.0491 memory: 6717 grad_norm: 2.7806 loss: 1.4977 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4977 2023/04/14 00:48:04 - mmengine - INFO - Epoch(train) [24][ 160/1879] lr: 2.0000e-02 eta: 14:51:06 time: 0.3046 data_time: 0.0201 memory: 6717 grad_norm: 2.8210 loss: 1.7026 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.7026 2023/04/14 00:48:12 - mmengine - INFO - Epoch(train) [24][ 180/1879] lr: 2.0000e-02 eta: 14:51:02 time: 0.4207 data_time: 0.0676 memory: 6717 grad_norm: 2.7815 loss: 1.6101 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.6101 2023/04/14 00:48:19 - mmengine - INFO - Epoch(train) [24][ 200/1879] lr: 2.0000e-02 eta: 14:50:54 time: 0.3570 data_time: 0.0140 memory: 6717 grad_norm: 2.8503 loss: 1.7103 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.7103 2023/04/14 00:48:28 - mmengine - INFO - Epoch(train) [24][ 220/1879] lr: 2.0000e-02 eta: 14:50:50 time: 0.4219 data_time: 0.0202 memory: 6717 grad_norm: 2.7549 loss: 1.6433 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6433 2023/04/14 00:48:34 - mmengine - INFO - Epoch(train) [24][ 240/1879] lr: 2.0000e-02 eta: 14:50:39 time: 0.3202 data_time: 0.0136 memory: 6717 grad_norm: 2.7845 loss: 1.7124 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7124 2023/04/14 00:48:42 - mmengine - INFO - Epoch(train) [24][ 260/1879] lr: 2.0000e-02 eta: 14:50:32 time: 0.3777 data_time: 0.0148 memory: 6717 grad_norm: 2.7118 loss: 1.6797 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6797 2023/04/14 00:48:48 - mmengine - INFO - Epoch(train) [24][ 280/1879] lr: 2.0000e-02 eta: 14:50:21 time: 0.3165 data_time: 0.0140 memory: 6717 grad_norm: 2.8746 loss: 1.6869 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6869 2023/04/14 00:48:56 - mmengine - INFO - Epoch(train) [24][ 300/1879] lr: 2.0000e-02 eta: 14:50:15 time: 0.3842 data_time: 0.0153 memory: 6717 grad_norm: 2.7643 loss: 1.6512 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6512 2023/04/14 00:49:02 - mmengine - INFO - Epoch(train) [24][ 320/1879] lr: 2.0000e-02 eta: 14:50:04 time: 0.3168 data_time: 0.0643 memory: 6717 grad_norm: 2.8232 loss: 1.8164 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.8164 2023/04/14 00:49:10 - mmengine - INFO - Epoch(train) [24][ 340/1879] lr: 2.0000e-02 eta: 14:50:00 time: 0.4157 data_time: 0.0325 memory: 6717 grad_norm: 2.8307 loss: 1.8045 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8045 2023/04/14 00:49:17 - mmengine - INFO - Epoch(train) [24][ 360/1879] lr: 2.0000e-02 eta: 14:49:52 time: 0.3600 data_time: 0.0134 memory: 6717 grad_norm: 2.7169 loss: 1.7787 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7787 2023/04/14 00:49:26 - mmengine - INFO - Epoch(train) [24][ 380/1879] lr: 2.0000e-02 eta: 14:49:47 time: 0.4086 data_time: 0.0143 memory: 6717 grad_norm: 2.8312 loss: 1.7509 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7509 2023/04/14 00:49:33 - mmengine - INFO - Epoch(train) [24][ 400/1879] lr: 2.0000e-02 eta: 14:49:38 time: 0.3520 data_time: 0.0136 memory: 6717 grad_norm: 2.7688 loss: 1.7306 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.7306 2023/04/14 00:49:40 - mmengine - INFO - Epoch(train) [24][ 420/1879] lr: 2.0000e-02 eta: 14:49:31 time: 0.3772 data_time: 0.0151 memory: 6717 grad_norm: 2.8982 loss: 1.6847 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6847 2023/04/14 00:49:47 - mmengine - INFO - Epoch(train) [24][ 440/1879] lr: 2.0000e-02 eta: 14:49:21 time: 0.3313 data_time: 0.0139 memory: 6717 grad_norm: 2.7475 loss: 1.8259 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8259 2023/04/14 00:49:55 - mmengine - INFO - Epoch(train) [24][ 460/1879] lr: 2.0000e-02 eta: 14:49:15 time: 0.3915 data_time: 0.0142 memory: 6717 grad_norm: 2.8705 loss: 1.6441 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.6441 2023/04/14 00:50:01 - mmengine - INFO - Epoch(train) [24][ 480/1879] lr: 2.0000e-02 eta: 14:49:04 time: 0.3113 data_time: 0.0136 memory: 6717 grad_norm: 2.7338 loss: 1.8551 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8551 2023/04/14 00:50:10 - mmengine - INFO - Epoch(train) [24][ 500/1879] lr: 2.0000e-02 eta: 14:49:01 time: 0.4373 data_time: 0.0805 memory: 6717 grad_norm: 2.7347 loss: 1.7232 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7232 2023/04/14 00:50:16 - mmengine - INFO - Epoch(train) [24][ 520/1879] lr: 2.0000e-02 eta: 14:48:50 time: 0.3127 data_time: 0.0514 memory: 6717 grad_norm: 2.7293 loss: 1.8325 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8325 2023/04/14 00:50:24 - mmengine - INFO - Epoch(train) [24][ 540/1879] lr: 2.0000e-02 eta: 14:48:46 time: 0.4174 data_time: 0.2600 memory: 6717 grad_norm: 2.7912 loss: 1.9227 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9227 2023/04/14 00:50:31 - mmengine - INFO - Epoch(train) [24][ 560/1879] lr: 2.0000e-02 eta: 14:48:37 time: 0.3450 data_time: 0.1658 memory: 6717 grad_norm: 2.8084 loss: 1.7679 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7679 2023/04/14 00:50:39 - mmengine - INFO - Epoch(train) [24][ 580/1879] lr: 2.0000e-02 eta: 14:48:31 time: 0.3941 data_time: 0.1740 memory: 6717 grad_norm: 2.8068 loss: 1.7932 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7932 2023/04/14 00:50:46 - mmengine - INFO - Epoch(train) [24][ 600/1879] lr: 2.0000e-02 eta: 14:48:21 time: 0.3316 data_time: 0.1813 memory: 6717 grad_norm: 2.7799 loss: 1.8061 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8061 2023/04/14 00:50:54 - mmengine - INFO - Epoch(train) [24][ 620/1879] lr: 2.0000e-02 eta: 14:48:16 time: 0.4065 data_time: 0.2637 memory: 6717 grad_norm: 2.7479 loss: 1.6749 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6749 2023/04/14 00:51:00 - mmengine - INFO - Epoch(train) [24][ 640/1879] lr: 2.0000e-02 eta: 14:48:06 time: 0.3279 data_time: 0.1792 memory: 6717 grad_norm: 2.7595 loss: 1.6767 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6767 2023/04/14 00:51:09 - mmengine - INFO - Epoch(train) [24][ 660/1879] lr: 2.0000e-02 eta: 14:48:02 time: 0.4217 data_time: 0.2802 memory: 6717 grad_norm: 2.7435 loss: 1.4832 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4832 2023/04/14 00:51:15 - mmengine - INFO - Epoch(train) [24][ 680/1879] lr: 2.0000e-02 eta: 14:47:52 time: 0.3301 data_time: 0.1923 memory: 6717 grad_norm: 2.7639 loss: 1.7743 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7743 2023/04/14 00:51:23 - mmengine - INFO - Epoch(train) [24][ 700/1879] lr: 2.0000e-02 eta: 14:47:46 time: 0.3946 data_time: 0.2371 memory: 6717 grad_norm: 2.7617 loss: 1.8209 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8209 2023/04/14 00:51:29 - mmengine - INFO - Epoch(train) [24][ 720/1879] lr: 2.0000e-02 eta: 14:47:34 time: 0.2987 data_time: 0.1591 memory: 6717 grad_norm: 2.7973 loss: 1.9558 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9558 2023/04/14 00:51:38 - mmengine - INFO - Epoch(train) [24][ 740/1879] lr: 2.0000e-02 eta: 14:47:31 time: 0.4360 data_time: 0.1774 memory: 6717 grad_norm: 2.8404 loss: 1.6699 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6699 2023/04/14 00:51:44 - mmengine - INFO - Epoch(train) [24][ 760/1879] lr: 2.0000e-02 eta: 14:47:20 time: 0.3113 data_time: 0.1254 memory: 6717 grad_norm: 2.7671 loss: 1.7154 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7154 2023/04/14 00:51:52 - mmengine - INFO - Epoch(train) [24][ 780/1879] lr: 2.0000e-02 eta: 14:47:14 time: 0.3924 data_time: 0.1550 memory: 6717 grad_norm: 2.8221 loss: 1.7064 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7064 2023/04/14 00:51:53 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 00:51:59 - mmengine - INFO - Epoch(train) [24][ 800/1879] lr: 2.0000e-02 eta: 14:47:05 time: 0.3542 data_time: 0.1143 memory: 6717 grad_norm: 2.7934 loss: 1.6565 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6565 2023/04/14 00:52:07 - mmengine - INFO - Epoch(train) [24][ 820/1879] lr: 2.0000e-02 eta: 14:46:59 time: 0.3869 data_time: 0.0277 memory: 6717 grad_norm: 2.9159 loss: 1.7909 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7909 2023/04/14 00:52:14 - mmengine - INFO - Epoch(train) [24][ 840/1879] lr: 2.0000e-02 eta: 14:46:50 time: 0.3417 data_time: 0.0566 memory: 6717 grad_norm: 2.8075 loss: 1.8366 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8366 2023/04/14 00:52:21 - mmengine - INFO - Epoch(train) [24][ 860/1879] lr: 2.0000e-02 eta: 14:46:42 time: 0.3719 data_time: 0.1351 memory: 6717 grad_norm: 2.7827 loss: 1.5657 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5657 2023/04/14 00:52:28 - mmengine - INFO - Epoch(train) [24][ 880/1879] lr: 2.0000e-02 eta: 14:46:34 time: 0.3530 data_time: 0.1795 memory: 6717 grad_norm: 2.6931 loss: 1.7231 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7231 2023/04/14 00:52:36 - mmengine - INFO - Epoch(train) [24][ 900/1879] lr: 2.0000e-02 eta: 14:46:28 time: 0.3962 data_time: 0.1518 memory: 6717 grad_norm: 2.8123 loss: 1.7431 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7431 2023/04/14 00:52:43 - mmengine - INFO - Epoch(train) [24][ 920/1879] lr: 2.0000e-02 eta: 14:46:18 time: 0.3223 data_time: 0.0667 memory: 6717 grad_norm: 2.7628 loss: 1.5333 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5333 2023/04/14 00:52:50 - mmengine - INFO - Epoch(train) [24][ 940/1879] lr: 2.0000e-02 eta: 14:46:12 time: 0.3891 data_time: 0.0733 memory: 6717 grad_norm: 2.8200 loss: 1.7630 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7630 2023/04/14 00:52:57 - mmengine - INFO - Epoch(train) [24][ 960/1879] lr: 2.0000e-02 eta: 14:46:03 time: 0.3500 data_time: 0.0824 memory: 6717 grad_norm: 2.7116 loss: 1.9823 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9823 2023/04/14 00:53:05 - mmengine - INFO - Epoch(train) [24][ 980/1879] lr: 2.0000e-02 eta: 14:45:56 time: 0.3703 data_time: 0.0344 memory: 6717 grad_norm: 2.7503 loss: 1.8394 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8394 2023/04/14 00:53:12 - mmengine - INFO - Epoch(train) [24][1000/1879] lr: 2.0000e-02 eta: 14:45:47 time: 0.3476 data_time: 0.0585 memory: 6717 grad_norm: 2.8319 loss: 1.7830 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7830 2023/04/14 00:53:20 - mmengine - INFO - Epoch(train) [24][1020/1879] lr: 2.0000e-02 eta: 14:45:41 time: 0.3935 data_time: 0.0393 memory: 6717 grad_norm: 2.8360 loss: 1.7176 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7176 2023/04/14 00:53:26 - mmengine - INFO - Epoch(train) [24][1040/1879] lr: 2.0000e-02 eta: 14:45:30 time: 0.3191 data_time: 0.0209 memory: 6717 grad_norm: 2.7591 loss: 1.8522 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.8522 2023/04/14 00:53:34 - mmengine - INFO - Epoch(train) [24][1060/1879] lr: 2.0000e-02 eta: 14:45:26 time: 0.4171 data_time: 0.0976 memory: 6717 grad_norm: 2.7290 loss: 1.7944 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7944 2023/04/14 00:53:41 - mmengine - INFO - Epoch(train) [24][1080/1879] lr: 2.0000e-02 eta: 14:45:17 time: 0.3463 data_time: 0.1639 memory: 6717 grad_norm: 2.8049 loss: 1.9712 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.9712 2023/04/14 00:53:49 - mmengine - INFO - Epoch(train) [24][1100/1879] lr: 2.0000e-02 eta: 14:45:11 time: 0.3989 data_time: 0.0750 memory: 6717 grad_norm: 2.7321 loss: 1.8360 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8360 2023/04/14 00:53:56 - mmengine - INFO - Epoch(train) [24][1120/1879] lr: 2.0000e-02 eta: 14:45:02 time: 0.3380 data_time: 0.0149 memory: 6717 grad_norm: 2.8199 loss: 1.8012 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8012 2023/04/14 00:54:03 - mmengine - INFO - Epoch(train) [24][1140/1879] lr: 2.0000e-02 eta: 14:44:54 time: 0.3685 data_time: 0.0139 memory: 6717 grad_norm: 2.7334 loss: 1.9269 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9269 2023/04/14 00:54:11 - mmengine - INFO - Epoch(train) [24][1160/1879] lr: 2.0000e-02 eta: 14:44:47 time: 0.3659 data_time: 0.0365 memory: 6717 grad_norm: 2.7370 loss: 1.7418 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7418 2023/04/14 00:54:19 - mmengine - INFO - Epoch(train) [24][1180/1879] lr: 2.0000e-02 eta: 14:44:42 time: 0.4137 data_time: 0.0158 memory: 6717 grad_norm: 2.8400 loss: 1.6209 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.6209 2023/04/14 00:54:25 - mmengine - INFO - Epoch(train) [24][1200/1879] lr: 2.0000e-02 eta: 14:44:31 time: 0.3060 data_time: 0.0144 memory: 6717 grad_norm: 2.7180 loss: 1.8750 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8750 2023/04/14 00:54:33 - mmengine - INFO - Epoch(train) [24][1220/1879] lr: 2.0000e-02 eta: 14:44:25 time: 0.4024 data_time: 0.0252 memory: 6717 grad_norm: 2.7517 loss: 1.8095 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8095 2023/04/14 00:54:40 - mmengine - INFO - Epoch(train) [24][1240/1879] lr: 2.0000e-02 eta: 14:44:16 time: 0.3442 data_time: 0.0505 memory: 6717 grad_norm: 2.7309 loss: 1.8018 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8018 2023/04/14 00:54:49 - mmengine - INFO - Epoch(train) [24][1260/1879] lr: 2.0000e-02 eta: 14:44:13 time: 0.4299 data_time: 0.1201 memory: 6717 grad_norm: 2.8294 loss: 1.9658 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9658 2023/04/14 00:54:55 - mmengine - INFO - Epoch(train) [24][1280/1879] lr: 2.0000e-02 eta: 14:44:02 time: 0.3226 data_time: 0.1572 memory: 6717 grad_norm: 2.7226 loss: 1.7290 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7290 2023/04/14 00:55:04 - mmengine - INFO - Epoch(train) [24][1300/1879] lr: 2.0000e-02 eta: 14:43:59 time: 0.4264 data_time: 0.2213 memory: 6717 grad_norm: 2.7905 loss: 1.7081 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7081 2023/04/14 00:55:10 - mmengine - INFO - Epoch(train) [24][1320/1879] lr: 2.0000e-02 eta: 14:43:47 time: 0.3126 data_time: 0.1724 memory: 6717 grad_norm: 2.7535 loss: 1.7210 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7210 2023/04/14 00:55:18 - mmengine - INFO - Epoch(train) [24][1340/1879] lr: 2.0000e-02 eta: 14:43:41 time: 0.3876 data_time: 0.1922 memory: 6717 grad_norm: 2.8335 loss: 1.8240 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8240 2023/04/14 00:55:24 - mmengine - INFO - Epoch(train) [24][1360/1879] lr: 2.0000e-02 eta: 14:43:31 time: 0.3205 data_time: 0.1072 memory: 6717 grad_norm: 2.7551 loss: 2.0171 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 2.0171 2023/04/14 00:55:33 - mmengine - INFO - Epoch(train) [24][1380/1879] lr: 2.0000e-02 eta: 14:43:28 time: 0.4499 data_time: 0.1124 memory: 6717 grad_norm: 2.8314 loss: 1.7780 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7780 2023/04/14 00:55:39 - mmengine - INFO - Epoch(train) [24][1400/1879] lr: 2.0000e-02 eta: 14:43:18 time: 0.3229 data_time: 0.0198 memory: 6717 grad_norm: 2.7260 loss: 1.7218 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7218 2023/04/14 00:55:48 - mmengine - INFO - Epoch(train) [24][1420/1879] lr: 2.0000e-02 eta: 14:43:14 time: 0.4239 data_time: 0.0134 memory: 6717 grad_norm: 2.7248 loss: 1.9876 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9876 2023/04/14 00:55:54 - mmengine - INFO - Epoch(train) [24][1440/1879] lr: 2.0000e-02 eta: 14:43:04 time: 0.3225 data_time: 0.0145 memory: 6717 grad_norm: 2.7685 loss: 1.6965 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6965 2023/04/14 00:56:02 - mmengine - INFO - Epoch(train) [24][1460/1879] lr: 2.0000e-02 eta: 14:42:58 time: 0.3960 data_time: 0.0128 memory: 6717 grad_norm: 2.6808 loss: 1.8654 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8654 2023/04/14 00:56:09 - mmengine - INFO - Epoch(train) [24][1480/1879] lr: 2.0000e-02 eta: 14:42:48 time: 0.3235 data_time: 0.0148 memory: 6717 grad_norm: 2.7845 loss: 1.8570 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8570 2023/04/14 00:56:18 - mmengine - INFO - Epoch(train) [24][1500/1879] lr: 2.0000e-02 eta: 14:42:45 time: 0.4404 data_time: 0.0130 memory: 6717 grad_norm: 2.7630 loss: 1.8571 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8571 2023/04/14 00:56:24 - mmengine - INFO - Epoch(train) [24][1520/1879] lr: 2.0000e-02 eta: 14:42:34 time: 0.3258 data_time: 0.0151 memory: 6717 grad_norm: 2.7649 loss: 1.7840 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.7840 2023/04/14 00:56:32 - mmengine - INFO - Epoch(train) [24][1540/1879] lr: 2.0000e-02 eta: 14:42:28 time: 0.3818 data_time: 0.0129 memory: 6717 grad_norm: 2.8549 loss: 1.7326 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.7326 2023/04/14 00:56:38 - mmengine - INFO - Epoch(train) [24][1560/1879] lr: 2.0000e-02 eta: 14:42:17 time: 0.3177 data_time: 0.0161 memory: 6717 grad_norm: 2.8190 loss: 1.7152 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7152 2023/04/14 00:56:46 - mmengine - INFO - Epoch(train) [24][1580/1879] lr: 2.0000e-02 eta: 14:42:11 time: 0.3874 data_time: 0.0131 memory: 6717 grad_norm: 2.7901 loss: 1.5974 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5974 2023/04/14 00:56:53 - mmengine - INFO - Epoch(train) [24][1600/1879] lr: 2.0000e-02 eta: 14:42:03 time: 0.3612 data_time: 0.0162 memory: 6717 grad_norm: 2.7231 loss: 1.9111 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.9111 2023/04/14 00:57:00 - mmengine - INFO - Epoch(train) [24][1620/1879] lr: 2.0000e-02 eta: 14:41:55 time: 0.3607 data_time: 0.0134 memory: 6717 grad_norm: 2.7456 loss: 1.7632 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7632 2023/04/14 00:57:09 - mmengine - INFO - Epoch(train) [24][1640/1879] lr: 2.0000e-02 eta: 14:41:52 time: 0.4440 data_time: 0.0160 memory: 6717 grad_norm: 2.7731 loss: 1.6184 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.6184 2023/04/14 00:57:15 - mmengine - INFO - Epoch(train) [24][1660/1879] lr: 2.0000e-02 eta: 14:41:41 time: 0.3093 data_time: 0.0131 memory: 6717 grad_norm: 2.7991 loss: 1.8413 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8413 2023/04/14 00:57:24 - mmengine - INFO - Epoch(train) [24][1680/1879] lr: 2.0000e-02 eta: 14:41:36 time: 0.4095 data_time: 0.0148 memory: 6717 grad_norm: 2.7615 loss: 1.6083 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6083 2023/04/14 00:57:30 - mmengine - INFO - Epoch(train) [24][1700/1879] lr: 2.0000e-02 eta: 14:41:25 time: 0.3116 data_time: 0.0128 memory: 6717 grad_norm: 2.7727 loss: 1.7288 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.7288 2023/04/14 00:57:38 - mmengine - INFO - Epoch(train) [24][1720/1879] lr: 2.0000e-02 eta: 14:41:19 time: 0.3949 data_time: 0.0137 memory: 6717 grad_norm: 2.7548 loss: 1.9729 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9729 2023/04/14 00:57:44 - mmengine - INFO - Epoch(train) [24][1740/1879] lr: 2.0000e-02 eta: 14:41:09 time: 0.3292 data_time: 0.0147 memory: 6717 grad_norm: 2.7833 loss: 1.8072 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8072 2023/04/14 00:57:52 - mmengine - INFO - Epoch(train) [24][1760/1879] lr: 2.0000e-02 eta: 14:41:04 time: 0.4037 data_time: 0.0147 memory: 6717 grad_norm: 2.7828 loss: 1.7659 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.7659 2023/04/14 00:57:59 - mmengine - INFO - Epoch(train) [24][1780/1879] lr: 2.0000e-02 eta: 14:40:54 time: 0.3375 data_time: 0.0148 memory: 6717 grad_norm: 2.7572 loss: 1.8503 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8503 2023/04/14 00:58:01 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 00:58:07 - mmengine - INFO - Epoch(train) [24][1800/1879] lr: 2.0000e-02 eta: 14:40:49 time: 0.4062 data_time: 0.0134 memory: 6717 grad_norm: 2.6795 loss: 1.8025 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8025 2023/04/14 00:58:14 - mmengine - INFO - Epoch(train) [24][1820/1879] lr: 2.0000e-02 eta: 14:40:38 time: 0.3152 data_time: 0.0216 memory: 6717 grad_norm: 2.7427 loss: 1.7226 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7226 2023/04/14 00:58:22 - mmengine - INFO - Epoch(train) [24][1840/1879] lr: 2.0000e-02 eta: 14:40:34 time: 0.4090 data_time: 0.0132 memory: 6717 grad_norm: 2.7969 loss: 1.8748 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.8748 2023/04/14 00:58:29 - mmengine - INFO - Epoch(train) [24][1860/1879] lr: 2.0000e-02 eta: 14:40:25 time: 0.3544 data_time: 0.0158 memory: 6717 grad_norm: 3.1044 loss: 1.6693 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6693 2023/04/14 00:58:35 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 00:58:35 - mmengine - INFO - Epoch(train) [24][1879/1879] lr: 2.0000e-02 eta: 14:40:16 time: 0.3222 data_time: 0.0105 memory: 6717 grad_norm: 2.9042 loss: 1.7021 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 1.7021 2023/04/14 00:58:35 - mmengine - INFO - Saving checkpoint at 24 epochs 2023/04/14 00:58:45 - mmengine - INFO - Epoch(val) [24][ 20/155] eta: 0:01:00 time: 0.4478 data_time: 0.4140 memory: 1391 2023/04/14 00:58:51 - mmengine - INFO - Epoch(val) [24][ 40/155] eta: 0:00:44 time: 0.3320 data_time: 0.2988 memory: 1391 2023/04/14 00:59:00 - mmengine - INFO - Epoch(val) [24][ 60/155] eta: 0:00:38 time: 0.4329 data_time: 0.3993 memory: 1391 2023/04/14 00:59:06 - mmengine - INFO - Epoch(val) [24][ 80/155] eta: 0:00:28 time: 0.3163 data_time: 0.2824 memory: 1391 2023/04/14 00:59:15 - mmengine - INFO - Epoch(val) [24][100/155] eta: 0:00:21 time: 0.4568 data_time: 0.4235 memory: 1391 2023/04/14 00:59:21 - mmengine - INFO - Epoch(val) [24][120/155] eta: 0:00:13 time: 0.2972 data_time: 0.2637 memory: 1391 2023/04/14 00:59:31 - mmengine - INFO - Epoch(val) [24][140/155] eta: 0:00:05 time: 0.4801 data_time: 0.4470 memory: 1391 2023/04/14 00:59:38 - mmengine - INFO - Epoch(val) [24][155/155] acc/top1: 0.5864 acc/top5: 0.8293 acc/mean1: 0.5863 data_time: 0.4155 time: 0.4480 2023/04/14 00:59:48 - mmengine - INFO - Epoch(train) [25][ 20/1879] lr: 2.0000e-02 eta: 14:40:17 time: 0.5074 data_time: 0.3285 memory: 6717 grad_norm: 2.8034 loss: 1.8478 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.8478 2023/04/14 00:59:54 - mmengine - INFO - Epoch(train) [25][ 40/1879] lr: 2.0000e-02 eta: 14:40:05 time: 0.3013 data_time: 0.1238 memory: 6717 grad_norm: 2.7978 loss: 1.6974 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6974 2023/04/14 01:00:03 - mmengine - INFO - Epoch(train) [25][ 60/1879] lr: 2.0000e-02 eta: 14:40:01 time: 0.4143 data_time: 0.1492 memory: 6717 grad_norm: 2.7499 loss: 1.5344 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5344 2023/04/14 01:00:10 - mmengine - INFO - Epoch(train) [25][ 80/1879] lr: 2.0000e-02 eta: 14:39:54 time: 0.3777 data_time: 0.0336 memory: 6717 grad_norm: 2.8220 loss: 1.5922 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5922 2023/04/14 01:00:18 - mmengine - INFO - Epoch(train) [25][ 100/1879] lr: 2.0000e-02 eta: 14:39:49 time: 0.4168 data_time: 0.0161 memory: 6717 grad_norm: 2.7637 loss: 1.7298 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7298 2023/04/14 01:00:24 - mmengine - INFO - Epoch(train) [25][ 120/1879] lr: 2.0000e-02 eta: 14:39:37 time: 0.2961 data_time: 0.0146 memory: 6717 grad_norm: 2.7741 loss: 1.5820 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.5820 2023/04/14 01:00:32 - mmengine - INFO - Epoch(train) [25][ 140/1879] lr: 2.0000e-02 eta: 14:39:31 time: 0.3879 data_time: 0.0155 memory: 6717 grad_norm: 2.7364 loss: 1.6574 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.6574 2023/04/14 01:00:39 - mmengine - INFO - Epoch(train) [25][ 160/1879] lr: 2.0000e-02 eta: 14:39:23 time: 0.3606 data_time: 0.0137 memory: 6717 grad_norm: 2.8290 loss: 1.8583 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8583 2023/04/14 01:00:48 - mmengine - INFO - Epoch(train) [25][ 180/1879] lr: 2.0000e-02 eta: 14:39:20 time: 0.4387 data_time: 0.0154 memory: 6717 grad_norm: 2.7352 loss: 1.6308 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6308 2023/04/14 01:00:54 - mmengine - INFO - Epoch(train) [25][ 200/1879] lr: 2.0000e-02 eta: 14:39:09 time: 0.3118 data_time: 0.0132 memory: 6717 grad_norm: 2.7571 loss: 1.6816 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6816 2023/04/14 01:01:03 - mmengine - INFO - Epoch(train) [25][ 220/1879] lr: 2.0000e-02 eta: 14:39:04 time: 0.4091 data_time: 0.0148 memory: 6717 grad_norm: 2.7901 loss: 1.6474 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6474 2023/04/14 01:01:09 - mmengine - INFO - Epoch(train) [25][ 240/1879] lr: 2.0000e-02 eta: 14:38:52 time: 0.2986 data_time: 0.0133 memory: 6717 grad_norm: 2.7507 loss: 1.7426 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.7426 2023/04/14 01:01:17 - mmengine - INFO - Epoch(train) [25][ 260/1879] lr: 2.0000e-02 eta: 14:38:48 time: 0.4151 data_time: 0.0157 memory: 6717 grad_norm: 2.6981 loss: 1.6731 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6731 2023/04/14 01:01:23 - mmengine - INFO - Epoch(train) [25][ 280/1879] lr: 2.0000e-02 eta: 14:38:38 time: 0.3311 data_time: 0.0298 memory: 6717 grad_norm: 2.7281 loss: 1.6314 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.6314 2023/04/14 01:01:32 - mmengine - INFO - Epoch(train) [25][ 300/1879] lr: 2.0000e-02 eta: 14:38:35 time: 0.4434 data_time: 0.0465 memory: 6717 grad_norm: 2.7813 loss: 1.5637 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5637 2023/04/14 01:01:39 - mmengine - INFO - Epoch(train) [25][ 320/1879] lr: 2.0000e-02 eta: 14:38:26 time: 0.3436 data_time: 0.0211 memory: 6717 grad_norm: 2.7250 loss: 1.5976 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5976 2023/04/14 01:01:47 - mmengine - INFO - Epoch(train) [25][ 340/1879] lr: 2.0000e-02 eta: 14:38:20 time: 0.3959 data_time: 0.0141 memory: 6717 grad_norm: 2.7773 loss: 1.7034 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.7034 2023/04/14 01:01:54 - mmengine - INFO - Epoch(train) [25][ 360/1879] lr: 2.0000e-02 eta: 14:38:10 time: 0.3317 data_time: 0.0121 memory: 6717 grad_norm: 2.7383 loss: 1.7067 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.7067 2023/04/14 01:02:02 - mmengine - INFO - Epoch(train) [25][ 380/1879] lr: 2.0000e-02 eta: 14:38:07 time: 0.4358 data_time: 0.0164 memory: 6717 grad_norm: 2.7636 loss: 1.7813 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7813 2023/04/14 01:02:09 - mmengine - INFO - Epoch(train) [25][ 400/1879] lr: 2.0000e-02 eta: 14:37:56 time: 0.3167 data_time: 0.0131 memory: 6717 grad_norm: 2.8403 loss: 1.6856 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6856 2023/04/14 01:02:17 - mmengine - INFO - Epoch(train) [25][ 420/1879] lr: 2.0000e-02 eta: 14:37:50 time: 0.3892 data_time: 0.0160 memory: 6717 grad_norm: 2.7201 loss: 1.7771 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7771 2023/04/14 01:02:23 - mmengine - INFO - Epoch(train) [25][ 440/1879] lr: 2.0000e-02 eta: 14:37:40 time: 0.3249 data_time: 0.0133 memory: 6717 grad_norm: 2.7318 loss: 1.5979 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.5979 2023/04/14 01:02:31 - mmengine - INFO - Epoch(train) [25][ 460/1879] lr: 2.0000e-02 eta: 14:37:34 time: 0.3945 data_time: 0.0146 memory: 6717 grad_norm: 2.7534 loss: 1.6456 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6456 2023/04/14 01:02:37 - mmengine - INFO - Epoch(train) [25][ 480/1879] lr: 2.0000e-02 eta: 14:37:24 time: 0.3237 data_time: 0.0129 memory: 6717 grad_norm: 2.8208 loss: 1.5903 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5903 2023/04/14 01:02:46 - mmengine - INFO - Epoch(train) [25][ 500/1879] lr: 2.0000e-02 eta: 14:37:19 time: 0.4091 data_time: 0.0141 memory: 6717 grad_norm: 2.8001 loss: 1.8461 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8461 2023/04/14 01:02:53 - mmengine - INFO - Epoch(train) [25][ 520/1879] lr: 2.0000e-02 eta: 14:37:10 time: 0.3437 data_time: 0.0144 memory: 6717 grad_norm: 2.7819 loss: 1.7588 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7588 2023/04/14 01:03:00 - mmengine - INFO - Epoch(train) [25][ 540/1879] lr: 2.0000e-02 eta: 14:37:04 time: 0.3962 data_time: 0.0156 memory: 6717 grad_norm: 2.8226 loss: 2.0679 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.0679 2023/04/14 01:03:07 - mmengine - INFO - Epoch(train) [25][ 560/1879] lr: 2.0000e-02 eta: 14:36:53 time: 0.3061 data_time: 0.0126 memory: 6717 grad_norm: 2.7265 loss: 1.7884 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7884 2023/04/14 01:03:15 - mmengine - INFO - Epoch(train) [25][ 580/1879] lr: 2.0000e-02 eta: 14:36:49 time: 0.4276 data_time: 0.0144 memory: 6717 grad_norm: 2.7248 loss: 1.6884 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6884 2023/04/14 01:03:21 - mmengine - INFO - Epoch(train) [25][ 600/1879] lr: 2.0000e-02 eta: 14:36:38 time: 0.3108 data_time: 0.0142 memory: 6717 grad_norm: 2.8092 loss: 1.8143 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8143 2023/04/14 01:03:30 - mmengine - INFO - Epoch(train) [25][ 620/1879] lr: 2.0000e-02 eta: 14:36:33 time: 0.4124 data_time: 0.0148 memory: 6717 grad_norm: 2.7254 loss: 1.5863 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5863 2023/04/14 01:03:36 - mmengine - INFO - Epoch(train) [25][ 640/1879] lr: 2.0000e-02 eta: 14:36:24 time: 0.3460 data_time: 0.0142 memory: 6717 grad_norm: 2.7530 loss: 1.7897 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7897 2023/04/14 01:03:44 - mmengine - INFO - Epoch(train) [25][ 660/1879] lr: 2.0000e-02 eta: 14:36:18 time: 0.3922 data_time: 0.0154 memory: 6717 grad_norm: 3.0330 loss: 1.6773 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6773 2023/04/14 01:03:51 - mmengine - INFO - Epoch(train) [25][ 680/1879] lr: 2.0000e-02 eta: 14:36:08 time: 0.3187 data_time: 0.0133 memory: 6717 grad_norm: 2.7991 loss: 1.8750 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8750 2023/04/14 01:03:59 - mmengine - INFO - Epoch(train) [25][ 700/1879] lr: 2.0000e-02 eta: 14:36:03 time: 0.4129 data_time: 0.0153 memory: 6717 grad_norm: 2.8093 loss: 1.6251 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6251 2023/04/14 01:04:05 - mmengine - INFO - Epoch(train) [25][ 720/1879] lr: 2.0000e-02 eta: 14:35:51 time: 0.2957 data_time: 0.0140 memory: 6717 grad_norm: 2.7927 loss: 1.9126 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.9126 2023/04/14 01:04:13 - mmengine - INFO - Epoch(train) [25][ 740/1879] lr: 2.0000e-02 eta: 14:35:46 time: 0.4154 data_time: 0.0136 memory: 6717 grad_norm: 2.7455 loss: 1.6785 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6785 2023/04/14 01:04:20 - mmengine - INFO - Epoch(train) [25][ 760/1879] lr: 2.0000e-02 eta: 14:35:36 time: 0.3185 data_time: 0.0136 memory: 6717 grad_norm: 2.7864 loss: 1.5318 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5318 2023/04/14 01:04:28 - mmengine - INFO - Epoch(train) [25][ 780/1879] lr: 2.0000e-02 eta: 14:35:31 time: 0.4043 data_time: 0.0134 memory: 6717 grad_norm: 2.8414 loss: 1.7819 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7819 2023/04/14 01:04:35 - mmengine - INFO - Epoch(train) [25][ 800/1879] lr: 2.0000e-02 eta: 14:35:22 time: 0.3436 data_time: 0.0148 memory: 6717 grad_norm: 2.8317 loss: 1.8559 top1_acc: 0.3750 top5_acc: 0.4375 loss_cls: 1.8559 2023/04/14 01:04:43 - mmengine - INFO - Epoch(train) [25][ 820/1879] lr: 2.0000e-02 eta: 14:35:17 time: 0.4118 data_time: 0.0140 memory: 6717 grad_norm: 2.7755 loss: 1.7623 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7623 2023/04/14 01:04:50 - mmengine - INFO - Epoch(train) [25][ 840/1879] lr: 2.0000e-02 eta: 14:35:08 time: 0.3418 data_time: 0.0147 memory: 6717 grad_norm: 2.8614 loss: 1.7599 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7599 2023/04/14 01:04:58 - mmengine - INFO - Epoch(train) [25][ 860/1879] lr: 2.0000e-02 eta: 14:35:04 time: 0.4250 data_time: 0.0145 memory: 6717 grad_norm: 2.7512 loss: 1.8755 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8755 2023/04/14 01:05:05 - mmengine - INFO - Epoch(train) [25][ 880/1879] lr: 2.0000e-02 eta: 14:34:54 time: 0.3388 data_time: 0.0144 memory: 6717 grad_norm: 2.7521 loss: 1.8643 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.8643 2023/04/14 01:05:12 - mmengine - INFO - Epoch(train) [25][ 900/1879] lr: 2.0000e-02 eta: 14:34:47 time: 0.3684 data_time: 0.0127 memory: 6717 grad_norm: 2.7640 loss: 1.8246 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8246 2023/04/14 01:05:13 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 01:05:19 - mmengine - INFO - Epoch(train) [25][ 920/1879] lr: 2.0000e-02 eta: 14:34:38 time: 0.3428 data_time: 0.0151 memory: 6717 grad_norm: 2.7667 loss: 1.8155 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8155 2023/04/14 01:05:27 - mmengine - INFO - Epoch(train) [25][ 940/1879] lr: 2.0000e-02 eta: 14:34:31 time: 0.3759 data_time: 0.0138 memory: 6717 grad_norm: 2.7538 loss: 1.7589 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7589 2023/04/14 01:05:34 - mmengine - INFO - Epoch(train) [25][ 960/1879] lr: 2.0000e-02 eta: 14:34:23 time: 0.3687 data_time: 0.0142 memory: 6717 grad_norm: 2.8124 loss: 1.6558 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6558 2023/04/14 01:05:41 - mmengine - INFO - Epoch(train) [25][ 980/1879] lr: 2.0000e-02 eta: 14:34:16 time: 0.3717 data_time: 0.0140 memory: 6717 grad_norm: 2.7588 loss: 1.8276 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8276 2023/04/14 01:05:49 - mmengine - INFO - Epoch(train) [25][1000/1879] lr: 2.0000e-02 eta: 14:34:08 time: 0.3548 data_time: 0.0122 memory: 6717 grad_norm: 2.7557 loss: 1.6723 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6723 2023/04/14 01:05:56 - mmengine - INFO - Epoch(train) [25][1020/1879] lr: 2.0000e-02 eta: 14:33:59 time: 0.3553 data_time: 0.0153 memory: 6717 grad_norm: 2.7394 loss: 1.9041 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.9041 2023/04/14 01:06:03 - mmengine - INFO - Epoch(train) [25][1040/1879] lr: 2.0000e-02 eta: 14:33:52 time: 0.3778 data_time: 0.0140 memory: 6717 grad_norm: 2.7543 loss: 1.8399 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8399 2023/04/14 01:06:11 - mmengine - INFO - Epoch(train) [25][1060/1879] lr: 2.0000e-02 eta: 14:33:45 time: 0.3645 data_time: 0.0145 memory: 6717 grad_norm: 2.7432 loss: 1.6114 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6114 2023/04/14 01:06:18 - mmengine - INFO - Epoch(train) [25][1080/1879] lr: 2.0000e-02 eta: 14:33:37 time: 0.3652 data_time: 0.0159 memory: 6717 grad_norm: 2.8122 loss: 1.5548 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5548 2023/04/14 01:06:25 - mmengine - INFO - Epoch(train) [25][1100/1879] lr: 2.0000e-02 eta: 14:33:29 time: 0.3577 data_time: 0.0137 memory: 6717 grad_norm: 2.7881 loss: 1.9329 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9329 2023/04/14 01:06:33 - mmengine - INFO - Epoch(train) [25][1120/1879] lr: 2.0000e-02 eta: 14:33:22 time: 0.3773 data_time: 0.0305 memory: 6717 grad_norm: 2.7703 loss: 1.6511 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6511 2023/04/14 01:06:40 - mmengine - INFO - Epoch(train) [25][1140/1879] lr: 2.0000e-02 eta: 14:33:14 time: 0.3638 data_time: 0.0124 memory: 6717 grad_norm: 2.8256 loss: 2.0985 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0985 2023/04/14 01:06:48 - mmengine - INFO - Epoch(train) [25][1160/1879] lr: 2.0000e-02 eta: 14:33:10 time: 0.4239 data_time: 0.0155 memory: 6717 grad_norm: 2.7497 loss: 1.9227 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9227 2023/04/14 01:06:55 - mmengine - INFO - Epoch(train) [25][1180/1879] lr: 2.0000e-02 eta: 14:32:59 time: 0.3180 data_time: 0.0127 memory: 6717 grad_norm: 2.7512 loss: 1.8102 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8102 2023/04/14 01:07:02 - mmengine - INFO - Epoch(train) [25][1200/1879] lr: 2.0000e-02 eta: 14:32:52 time: 0.3711 data_time: 0.0180 memory: 6717 grad_norm: 2.8040 loss: 1.8544 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8544 2023/04/14 01:07:09 - mmengine - INFO - Epoch(train) [25][1220/1879] lr: 2.0000e-02 eta: 14:32:44 time: 0.3565 data_time: 0.0130 memory: 6717 grad_norm: 2.8109 loss: 1.6546 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6546 2023/04/14 01:07:17 - mmengine - INFO - Epoch(train) [25][1240/1879] lr: 2.0000e-02 eta: 14:32:37 time: 0.3740 data_time: 0.0161 memory: 6717 grad_norm: 2.7334 loss: 1.9357 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9357 2023/04/14 01:07:24 - mmengine - INFO - Epoch(train) [25][1260/1879] lr: 2.0000e-02 eta: 14:32:28 time: 0.3452 data_time: 0.0127 memory: 6717 grad_norm: 2.6887 loss: 1.7381 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7381 2023/04/14 01:07:31 - mmengine - INFO - Epoch(train) [25][1280/1879] lr: 2.0000e-02 eta: 14:32:22 time: 0.3934 data_time: 0.0158 memory: 6717 grad_norm: 2.7550 loss: 1.8215 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.8215 2023/04/14 01:07:38 - mmengine - INFO - Epoch(train) [25][1300/1879] lr: 2.0000e-02 eta: 14:32:12 time: 0.3346 data_time: 0.0132 memory: 6717 grad_norm: 2.7792 loss: 1.8917 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.8917 2023/04/14 01:07:46 - mmengine - INFO - Epoch(train) [25][1320/1879] lr: 2.0000e-02 eta: 14:32:08 time: 0.4125 data_time: 0.0213 memory: 6717 grad_norm: 2.7075 loss: 1.7603 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.7603 2023/04/14 01:07:53 - mmengine - INFO - Epoch(train) [25][1340/1879] lr: 2.0000e-02 eta: 14:31:58 time: 0.3365 data_time: 0.0256 memory: 6717 grad_norm: 2.7638 loss: 1.8080 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8080 2023/04/14 01:08:02 - mmengine - INFO - Epoch(train) [25][1360/1879] lr: 2.0000e-02 eta: 14:31:54 time: 0.4199 data_time: 0.0730 memory: 6717 grad_norm: 2.7653 loss: 1.8034 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.8034 2023/04/14 01:08:08 - mmengine - INFO - Epoch(train) [25][1380/1879] lr: 2.0000e-02 eta: 14:31:45 time: 0.3400 data_time: 0.1223 memory: 6717 grad_norm: 2.7950 loss: 1.5748 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5748 2023/04/14 01:08:16 - mmengine - INFO - Epoch(train) [25][1400/1879] lr: 2.0000e-02 eta: 14:31:37 time: 0.3680 data_time: 0.1551 memory: 6717 grad_norm: 2.7324 loss: 1.4936 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4936 2023/04/14 01:08:22 - mmengine - INFO - Epoch(train) [25][1420/1879] lr: 2.0000e-02 eta: 14:31:27 time: 0.3326 data_time: 0.0893 memory: 6717 grad_norm: 2.8280 loss: 1.8513 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8513 2023/04/14 01:08:31 - mmengine - INFO - Epoch(train) [25][1440/1879] lr: 2.0000e-02 eta: 14:31:24 time: 0.4317 data_time: 0.0221 memory: 6717 grad_norm: 2.7746 loss: 1.6088 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6088 2023/04/14 01:08:38 - mmengine - INFO - Epoch(train) [25][1460/1879] lr: 2.0000e-02 eta: 14:31:14 time: 0.3306 data_time: 0.0130 memory: 6717 grad_norm: 2.7511 loss: 1.8795 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8795 2023/04/14 01:08:46 - mmengine - INFO - Epoch(train) [25][1480/1879] lr: 2.0000e-02 eta: 14:31:10 time: 0.4240 data_time: 0.0281 memory: 6717 grad_norm: 2.8248 loss: 1.8182 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8182 2023/04/14 01:08:53 - mmengine - INFO - Epoch(train) [25][1500/1879] lr: 2.0000e-02 eta: 14:31:00 time: 0.3263 data_time: 0.0138 memory: 6717 grad_norm: 2.7627 loss: 1.9339 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.9339 2023/04/14 01:09:00 - mmengine - INFO - Epoch(train) [25][1520/1879] lr: 2.0000e-02 eta: 14:30:54 time: 0.3889 data_time: 0.0163 memory: 6717 grad_norm: 2.7733 loss: 1.9384 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.9384 2023/04/14 01:09:07 - mmengine - INFO - Epoch(train) [25][1540/1879] lr: 2.0000e-02 eta: 14:30:44 time: 0.3364 data_time: 0.0128 memory: 6717 grad_norm: 2.8379 loss: 1.8325 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8325 2023/04/14 01:09:16 - mmengine - INFO - Epoch(train) [25][1560/1879] lr: 2.0000e-02 eta: 14:30:40 time: 0.4281 data_time: 0.0157 memory: 6717 grad_norm: 2.8161 loss: 1.7934 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7934 2023/04/14 01:09:22 - mmengine - INFO - Epoch(train) [25][1580/1879] lr: 2.0000e-02 eta: 14:30:30 time: 0.3176 data_time: 0.0125 memory: 6717 grad_norm: 2.7756 loss: 1.6636 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6636 2023/04/14 01:09:30 - mmengine - INFO - Epoch(train) [25][1600/1879] lr: 2.0000e-02 eta: 14:30:25 time: 0.4146 data_time: 0.0160 memory: 6717 grad_norm: 2.7713 loss: 1.8691 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8691 2023/04/14 01:09:37 - mmengine - INFO - Epoch(train) [25][1620/1879] lr: 2.0000e-02 eta: 14:30:14 time: 0.3099 data_time: 0.0121 memory: 6717 grad_norm: 2.7504 loss: 1.7838 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7838 2023/04/14 01:09:52 - mmengine - INFO - Epoch(train) [25][1640/1879] lr: 2.0000e-02 eta: 14:30:30 time: 0.7529 data_time: 0.2459 memory: 6717 grad_norm: 2.6976 loss: 1.7804 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.7804 2023/04/14 01:09:58 - mmengine - INFO - Epoch(train) [25][1660/1879] lr: 2.0000e-02 eta: 14:30:19 time: 0.3198 data_time: 0.1849 memory: 6717 grad_norm: 2.8024 loss: 1.7674 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7674 2023/04/14 01:10:06 - mmengine - INFO - Epoch(train) [25][1680/1879] lr: 2.0000e-02 eta: 14:30:14 time: 0.4085 data_time: 0.2704 memory: 6717 grad_norm: 2.7304 loss: 1.8337 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8337 2023/04/14 01:10:13 - mmengine - INFO - Epoch(train) [25][1700/1879] lr: 2.0000e-02 eta: 14:30:04 time: 0.3242 data_time: 0.1818 memory: 6717 grad_norm: 2.7571 loss: 1.6928 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6928 2023/04/14 01:10:22 - mmengine - INFO - Epoch(train) [25][1720/1879] lr: 2.0000e-02 eta: 14:30:01 time: 0.4446 data_time: 0.3024 memory: 6717 grad_norm: 2.7328 loss: 1.6533 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6533 2023/04/14 01:10:28 - mmengine - INFO - Epoch(train) [25][1740/1879] lr: 2.0000e-02 eta: 14:29:51 time: 0.3171 data_time: 0.1781 memory: 6717 grad_norm: 2.8155 loss: 1.9672 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9672 2023/04/14 01:10:36 - mmengine - INFO - Epoch(train) [25][1760/1879] lr: 2.0000e-02 eta: 14:29:44 time: 0.3819 data_time: 0.2399 memory: 6717 grad_norm: 2.7349 loss: 1.7179 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7179 2023/04/14 01:10:42 - mmengine - INFO - Epoch(train) [25][1780/1879] lr: 2.0000e-02 eta: 14:29:33 time: 0.3108 data_time: 0.1677 memory: 6717 grad_norm: 2.6905 loss: 1.6822 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6822 2023/04/14 01:10:49 - mmengine - INFO - Epoch(train) [25][1800/1879] lr: 2.0000e-02 eta: 14:29:27 time: 0.3852 data_time: 0.2433 memory: 6717 grad_norm: 2.8161 loss: 1.6233 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6233 2023/04/14 01:10:56 - mmengine - INFO - Epoch(train) [25][1820/1879] lr: 2.0000e-02 eta: 14:29:17 time: 0.3412 data_time: 0.1711 memory: 6717 grad_norm: 2.7576 loss: 1.8486 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.8486 2023/04/14 01:11:04 - mmengine - INFO - Epoch(train) [25][1840/1879] lr: 2.0000e-02 eta: 14:29:11 time: 0.3918 data_time: 0.2397 memory: 6717 grad_norm: 2.7927 loss: 1.7356 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7356 2023/04/14 01:11:11 - mmengine - INFO - Epoch(train) [25][1860/1879] lr: 2.0000e-02 eta: 14:29:03 time: 0.3557 data_time: 0.2175 memory: 6717 grad_norm: 2.7979 loss: 1.7491 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.7491 2023/04/14 01:11:17 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 01:11:17 - mmengine - INFO - Epoch(train) [25][1879/1879] lr: 2.0000e-02 eta: 14:28:52 time: 0.2861 data_time: 0.1564 memory: 6717 grad_norm: 2.7349 loss: 1.7378 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 1.7378 2023/04/14 01:11:27 - mmengine - INFO - Epoch(val) [25][ 20/155] eta: 0:01:10 time: 0.5256 data_time: 0.4926 memory: 1391 2023/04/14 01:11:36 - mmengine - INFO - Epoch(val) [25][ 40/155] eta: 0:00:56 time: 0.4497 data_time: 0.4168 memory: 1391 2023/04/14 01:11:42 - mmengine - INFO - Epoch(val) [25][ 60/155] eta: 0:00:39 time: 0.2856 data_time: 0.2528 memory: 1391 2023/04/14 01:11:51 - mmengine - INFO - Epoch(val) [25][ 80/155] eta: 0:00:31 time: 0.4285 data_time: 0.3957 memory: 1391 2023/04/14 01:11:57 - mmengine - INFO - Epoch(val) [25][100/155] eta: 0:00:21 time: 0.2956 data_time: 0.2592 memory: 1391 2023/04/14 01:12:05 - mmengine - INFO - Epoch(val) [25][120/155] eta: 0:00:13 time: 0.4071 data_time: 0.3743 memory: 1391 2023/04/14 01:12:12 - mmengine - INFO - Epoch(val) [25][140/155] eta: 0:00:05 time: 0.3810 data_time: 0.3482 memory: 1391 2023/04/14 01:12:19 - mmengine - INFO - Epoch(val) [25][155/155] acc/top1: 0.5943 acc/top5: 0.8275 acc/mean1: 0.5942 data_time: 0.4205 time: 0.4524 2023/04/14 01:12:19 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_22.pth is removed 2023/04/14 01:12:20 - mmengine - INFO - The best checkpoint with 0.5943 acc/top1 at 25 epoch is saved to best_acc_top1_epoch_25.pth. 2023/04/14 01:12:29 - mmengine - INFO - Epoch(train) [26][ 20/1879] lr: 2.0000e-02 eta: 14:28:50 time: 0.4726 data_time: 0.3326 memory: 6717 grad_norm: 2.7835 loss: 1.6894 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6894 2023/04/14 01:12:32 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 01:12:36 - mmengine - INFO - Epoch(train) [26][ 40/1879] lr: 2.0000e-02 eta: 14:28:41 time: 0.3337 data_time: 0.1958 memory: 6717 grad_norm: 2.7283 loss: 1.6827 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.6827 2023/04/14 01:12:44 - mmengine - INFO - Epoch(train) [26][ 60/1879] lr: 2.0000e-02 eta: 14:28:37 time: 0.4224 data_time: 0.2747 memory: 6717 grad_norm: 2.7723 loss: 1.7181 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7181 2023/04/14 01:12:51 - mmengine - INFO - Epoch(train) [26][ 80/1879] lr: 2.0000e-02 eta: 14:28:26 time: 0.3123 data_time: 0.1591 memory: 6717 grad_norm: 2.7514 loss: 1.7715 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7715 2023/04/14 01:12:59 - mmengine - INFO - Epoch(train) [26][ 100/1879] lr: 2.0000e-02 eta: 14:28:21 time: 0.4079 data_time: 0.2095 memory: 6717 grad_norm: 2.8791 loss: 1.7107 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7107 2023/04/14 01:13:06 - mmengine - INFO - Epoch(train) [26][ 120/1879] lr: 2.0000e-02 eta: 14:28:11 time: 0.3344 data_time: 0.1453 memory: 6717 grad_norm: 2.7418 loss: 1.7709 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7709 2023/04/14 01:13:14 - mmengine - INFO - Epoch(train) [26][ 140/1879] lr: 2.0000e-02 eta: 14:28:07 time: 0.4223 data_time: 0.0776 memory: 6717 grad_norm: 2.8453 loss: 1.6742 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6742 2023/04/14 01:13:21 - mmengine - INFO - Epoch(train) [26][ 160/1879] lr: 2.0000e-02 eta: 14:27:57 time: 0.3352 data_time: 0.0590 memory: 6717 grad_norm: 2.8666 loss: 1.4923 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.4923 2023/04/14 01:13:28 - mmengine - INFO - Epoch(train) [26][ 180/1879] lr: 2.0000e-02 eta: 14:27:50 time: 0.3717 data_time: 0.0403 memory: 6717 grad_norm: 2.9063 loss: 1.8820 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8820 2023/04/14 01:13:35 - mmengine - INFO - Epoch(train) [26][ 200/1879] lr: 2.0000e-02 eta: 14:27:42 time: 0.3632 data_time: 0.0547 memory: 6717 grad_norm: 2.7435 loss: 1.7175 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7175 2023/04/14 01:13:43 - mmengine - INFO - Epoch(train) [26][ 220/1879] lr: 2.0000e-02 eta: 14:27:36 time: 0.3923 data_time: 0.0925 memory: 6717 grad_norm: 2.7529 loss: 1.9636 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9636 2023/04/14 01:13:50 - mmengine - INFO - Epoch(train) [26][ 240/1879] lr: 2.0000e-02 eta: 14:27:28 time: 0.3489 data_time: 0.0429 memory: 6717 grad_norm: 2.6978 loss: 1.6797 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6797 2023/04/14 01:13:58 - mmengine - INFO - Epoch(train) [26][ 260/1879] lr: 2.0000e-02 eta: 14:27:23 time: 0.4101 data_time: 0.0514 memory: 6717 grad_norm: 2.9179 loss: 1.7995 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7995 2023/04/14 01:14:05 - mmengine - INFO - Epoch(train) [26][ 280/1879] lr: 2.0000e-02 eta: 14:27:14 time: 0.3442 data_time: 0.0127 memory: 6717 grad_norm: 2.7513 loss: 1.7844 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7844 2023/04/14 01:14:14 - mmengine - INFO - Epoch(train) [26][ 300/1879] lr: 2.0000e-02 eta: 14:27:09 time: 0.4165 data_time: 0.0158 memory: 6717 grad_norm: 2.7839 loss: 1.8222 top1_acc: 0.4375 top5_acc: 0.5000 loss_cls: 1.8222 2023/04/14 01:14:21 - mmengine - INFO - Epoch(train) [26][ 320/1879] lr: 2.0000e-02 eta: 14:27:02 time: 0.3737 data_time: 0.0122 memory: 6717 grad_norm: 2.7788 loss: 1.7068 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.7068 2023/04/14 01:14:29 - mmengine - INFO - Epoch(train) [26][ 340/1879] lr: 2.0000e-02 eta: 14:26:55 time: 0.3759 data_time: 0.0145 memory: 6717 grad_norm: 2.8187 loss: 1.7726 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.7726 2023/04/14 01:14:36 - mmengine - INFO - Epoch(train) [26][ 360/1879] lr: 2.0000e-02 eta: 14:26:47 time: 0.3621 data_time: 0.0138 memory: 6717 grad_norm: 2.7583 loss: 1.5432 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5432 2023/04/14 01:14:43 - mmengine - INFO - Epoch(train) [26][ 380/1879] lr: 2.0000e-02 eta: 14:26:37 time: 0.3319 data_time: 0.0145 memory: 6717 grad_norm: 2.7921 loss: 1.8027 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8027 2023/04/14 01:14:51 - mmengine - INFO - Epoch(train) [26][ 400/1879] lr: 2.0000e-02 eta: 14:26:32 time: 0.4084 data_time: 0.0174 memory: 6717 grad_norm: 2.7422 loss: 1.6234 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.6234 2023/04/14 01:14:57 - mmengine - INFO - Epoch(train) [26][ 420/1879] lr: 2.0000e-02 eta: 14:26:22 time: 0.3234 data_time: 0.0143 memory: 6717 grad_norm: 2.8172 loss: 1.7749 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7749 2023/04/14 01:15:05 - mmengine - INFO - Epoch(train) [26][ 440/1879] lr: 2.0000e-02 eta: 14:26:16 time: 0.3946 data_time: 0.0157 memory: 6717 grad_norm: 2.7721 loss: 1.8009 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8009 2023/04/14 01:15:12 - mmengine - INFO - Epoch(train) [26][ 460/1879] lr: 2.0000e-02 eta: 14:26:06 time: 0.3264 data_time: 0.0129 memory: 6717 grad_norm: 2.7783 loss: 1.6431 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6431 2023/04/14 01:15:19 - mmengine - INFO - Epoch(train) [26][ 480/1879] lr: 2.0000e-02 eta: 14:25:59 time: 0.3711 data_time: 0.0359 memory: 6717 grad_norm: 2.9484 loss: 1.6283 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6283 2023/04/14 01:15:27 - mmengine - INFO - Epoch(train) [26][ 500/1879] lr: 2.0000e-02 eta: 14:25:52 time: 0.3767 data_time: 0.0541 memory: 6717 grad_norm: 2.7754 loss: 1.6165 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.6165 2023/04/14 01:15:34 - mmengine - INFO - Epoch(train) [26][ 520/1879] lr: 2.0000e-02 eta: 14:25:43 time: 0.3551 data_time: 0.0422 memory: 6717 grad_norm: 2.7838 loss: 1.8091 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8091 2023/04/14 01:15:42 - mmengine - INFO - Epoch(train) [26][ 540/1879] lr: 2.0000e-02 eta: 14:25:38 time: 0.4076 data_time: 0.1915 memory: 6717 grad_norm: 2.7179 loss: 1.7346 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7346 2023/04/14 01:15:48 - mmengine - INFO - Epoch(train) [26][ 560/1879] lr: 2.0000e-02 eta: 14:25:28 time: 0.3162 data_time: 0.1743 memory: 6717 grad_norm: 2.7637 loss: 1.6127 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.6127 2023/04/14 01:15:56 - mmengine - INFO - Epoch(train) [26][ 580/1879] lr: 2.0000e-02 eta: 14:25:21 time: 0.3780 data_time: 0.2148 memory: 6717 grad_norm: 2.7171 loss: 1.7245 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7245 2023/04/14 01:16:03 - mmengine - INFO - Epoch(train) [26][ 600/1879] lr: 2.0000e-02 eta: 14:25:14 time: 0.3817 data_time: 0.0944 memory: 6717 grad_norm: 2.8461 loss: 1.6405 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6405 2023/04/14 01:16:11 - mmengine - INFO - Epoch(train) [26][ 620/1879] lr: 2.0000e-02 eta: 14:25:08 time: 0.3999 data_time: 0.1078 memory: 6717 grad_norm: 2.7682 loss: 1.7497 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.7497 2023/04/14 01:16:18 - mmengine - INFO - Epoch(train) [26][ 640/1879] lr: 2.0000e-02 eta: 14:24:59 time: 0.3331 data_time: 0.1083 memory: 6717 grad_norm: 2.8055 loss: 1.6202 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6202 2023/04/14 01:16:26 - mmengine - INFO - Epoch(train) [26][ 660/1879] lr: 2.0000e-02 eta: 14:24:53 time: 0.3911 data_time: 0.2281 memory: 6717 grad_norm: 2.8035 loss: 1.9132 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.9132 2023/04/14 01:16:33 - mmengine - INFO - Epoch(train) [26][ 680/1879] lr: 2.0000e-02 eta: 14:24:44 time: 0.3526 data_time: 0.1341 memory: 6717 grad_norm: 2.7313 loss: 1.7213 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.7213 2023/04/14 01:16:41 - mmengine - INFO - Epoch(train) [26][ 700/1879] lr: 2.0000e-02 eta: 14:24:38 time: 0.3841 data_time: 0.2158 memory: 6717 grad_norm: 2.8658 loss: 1.6723 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6723 2023/04/14 01:16:47 - mmengine - INFO - Epoch(train) [26][ 720/1879] lr: 2.0000e-02 eta: 14:24:27 time: 0.3138 data_time: 0.1730 memory: 6717 grad_norm: 2.7566 loss: 1.7500 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7500 2023/04/14 01:16:55 - mmengine - INFO - Epoch(train) [26][ 740/1879] lr: 2.0000e-02 eta: 14:24:22 time: 0.4143 data_time: 0.2720 memory: 6717 grad_norm: 2.8164 loss: 1.6130 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.6130 2023/04/14 01:17:01 - mmengine - INFO - Epoch(train) [26][ 760/1879] lr: 2.0000e-02 eta: 14:24:11 time: 0.3051 data_time: 0.1673 memory: 6717 grad_norm: 2.7861 loss: 2.0161 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 2.0161 2023/04/14 01:17:10 - mmengine - INFO - Epoch(train) [26][ 780/1879] lr: 2.0000e-02 eta: 14:24:08 time: 0.4527 data_time: 0.1790 memory: 6717 grad_norm: 2.6812 loss: 1.8669 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.8669 2023/04/14 01:17:17 - mmengine - INFO - Epoch(train) [26][ 800/1879] lr: 2.0000e-02 eta: 14:23:58 time: 0.3176 data_time: 0.0730 memory: 6717 grad_norm: 2.7862 loss: 1.7595 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7595 2023/04/14 01:17:25 - mmengine - INFO - Epoch(train) [26][ 820/1879] lr: 2.0000e-02 eta: 14:23:53 time: 0.4139 data_time: 0.0306 memory: 6717 grad_norm: 2.7951 loss: 1.8910 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8910 2023/04/14 01:17:31 - mmengine - INFO - Epoch(train) [26][ 840/1879] lr: 2.0000e-02 eta: 14:23:42 time: 0.3005 data_time: 0.0275 memory: 6717 grad_norm: 2.7157 loss: 1.7384 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7384 2023/04/14 01:17:39 - mmengine - INFO - Epoch(train) [26][ 860/1879] lr: 2.0000e-02 eta: 14:23:37 time: 0.4231 data_time: 0.0185 memory: 6717 grad_norm: 2.6715 loss: 1.8028 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8028 2023/04/14 01:17:46 - mmengine - INFO - Epoch(train) [26][ 880/1879] lr: 2.0000e-02 eta: 14:23:26 time: 0.3097 data_time: 0.0139 memory: 6717 grad_norm: 2.7602 loss: 1.8916 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8916 2023/04/14 01:17:55 - mmengine - INFO - Epoch(train) [26][ 900/1879] lr: 2.0000e-02 eta: 14:23:24 time: 0.4477 data_time: 0.0433 memory: 6717 grad_norm: 2.7741 loss: 1.8318 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8318 2023/04/14 01:18:01 - mmengine - INFO - Epoch(train) [26][ 920/1879] lr: 2.0000e-02 eta: 14:23:13 time: 0.3222 data_time: 0.0132 memory: 6717 grad_norm: 2.6340 loss: 1.7768 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7768 2023/04/14 01:18:09 - mmengine - INFO - Epoch(train) [26][ 940/1879] lr: 2.0000e-02 eta: 14:23:07 time: 0.3927 data_time: 0.0155 memory: 6717 grad_norm: 2.7808 loss: 1.7615 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7615 2023/04/14 01:18:16 - mmengine - INFO - Epoch(train) [26][ 960/1879] lr: 2.0000e-02 eta: 14:22:59 time: 0.3554 data_time: 0.0145 memory: 6717 grad_norm: 2.7088 loss: 1.8410 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8410 2023/04/14 01:18:24 - mmengine - INFO - Epoch(train) [26][ 980/1879] lr: 2.0000e-02 eta: 14:22:54 time: 0.4109 data_time: 0.0148 memory: 6717 grad_norm: 2.7818 loss: 1.5460 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5460 2023/04/14 01:18:31 - mmengine - INFO - Epoch(train) [26][1000/1879] lr: 2.0000e-02 eta: 14:22:45 time: 0.3337 data_time: 0.0132 memory: 6717 grad_norm: 2.7836 loss: 1.5679 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.5679 2023/04/14 01:18:38 - mmengine - INFO - Epoch(train) [26][1020/1879] lr: 2.0000e-02 eta: 14:22:38 time: 0.3789 data_time: 0.0152 memory: 6717 grad_norm: 2.7892 loss: 1.8678 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8678 2023/04/14 01:18:40 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 01:18:45 - mmengine - INFO - Epoch(train) [26][1040/1879] lr: 2.0000e-02 eta: 14:22:28 time: 0.3238 data_time: 0.0134 memory: 6717 grad_norm: 2.7799 loss: 1.6630 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6630 2023/04/14 01:18:53 - mmengine - INFO - Epoch(train) [26][1060/1879] lr: 2.0000e-02 eta: 14:22:22 time: 0.3981 data_time: 0.0161 memory: 6717 grad_norm: 2.7362 loss: 1.9003 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9003 2023/04/14 01:18:59 - mmengine - INFO - Epoch(train) [26][1080/1879] lr: 2.0000e-02 eta: 14:22:12 time: 0.3233 data_time: 0.0132 memory: 6717 grad_norm: 2.7648 loss: 2.0435 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0435 2023/04/14 01:19:07 - mmengine - INFO - Epoch(train) [26][1100/1879] lr: 2.0000e-02 eta: 14:22:06 time: 0.4056 data_time: 0.0153 memory: 6717 grad_norm: 2.7538 loss: 1.8407 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8407 2023/04/14 01:19:15 - mmengine - INFO - Epoch(train) [26][1120/1879] lr: 2.0000e-02 eta: 14:21:59 time: 0.3652 data_time: 0.0128 memory: 6717 grad_norm: 2.7050 loss: 1.6339 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6339 2023/04/14 01:19:23 - mmengine - INFO - Epoch(train) [26][1140/1879] lr: 2.0000e-02 eta: 14:21:53 time: 0.4053 data_time: 0.0150 memory: 6717 grad_norm: 2.7465 loss: 1.6552 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6552 2023/04/14 01:19:29 - mmengine - INFO - Epoch(train) [26][1160/1879] lr: 2.0000e-02 eta: 14:21:42 time: 0.3059 data_time: 0.0131 memory: 6717 grad_norm: 2.7460 loss: 1.6878 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6878 2023/04/14 01:19:37 - mmengine - INFO - Epoch(train) [26][1180/1879] lr: 2.0000e-02 eta: 14:21:38 time: 0.4175 data_time: 0.0133 memory: 6717 grad_norm: 2.7352 loss: 1.7968 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7968 2023/04/14 01:20:09 - mmengine - INFO - Epoch(train) [26][1200/1879] lr: 2.0000e-02 eta: 14:22:40 time: 1.5750 data_time: 0.0152 memory: 6717 grad_norm: 2.8687 loss: 1.6187 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6187 2023/04/14 01:20:15 - mmengine - INFO - Epoch(train) [26][1220/1879] lr: 2.0000e-02 eta: 14:22:29 time: 0.3113 data_time: 0.0142 memory: 6717 grad_norm: 2.8257 loss: 1.8813 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8813 2023/04/14 01:20:23 - mmengine - INFO - Epoch(train) [26][1240/1879] lr: 2.0000e-02 eta: 14:22:23 time: 0.3966 data_time: 0.0146 memory: 6717 grad_norm: 2.8513 loss: 1.7841 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7841 2023/04/14 01:20:28 - mmengine - INFO - Epoch(train) [26][1260/1879] lr: 2.0000e-02 eta: 14:22:10 time: 0.2730 data_time: 0.0124 memory: 6717 grad_norm: 2.8597 loss: 1.8236 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.8236 2023/04/14 01:20:37 - mmengine - INFO - Epoch(train) [26][1280/1879] lr: 2.0000e-02 eta: 14:22:07 time: 0.4340 data_time: 0.0162 memory: 6717 grad_norm: 2.7825 loss: 2.0015 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 2.0015 2023/04/14 01:20:43 - mmengine - INFO - Epoch(train) [26][1300/1879] lr: 2.0000e-02 eta: 14:21:56 time: 0.3102 data_time: 0.0131 memory: 6717 grad_norm: 2.7995 loss: 1.6210 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.6210 2023/04/14 01:20:51 - mmengine - INFO - Epoch(train) [26][1320/1879] lr: 2.0000e-02 eta: 14:21:50 time: 0.3981 data_time: 0.0146 memory: 6717 grad_norm: 2.7861 loss: 1.6123 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6123 2023/04/14 01:20:57 - mmengine - INFO - Epoch(train) [26][1340/1879] lr: 2.0000e-02 eta: 14:21:39 time: 0.3057 data_time: 0.0128 memory: 6717 grad_norm: 2.7271 loss: 1.8503 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.8503 2023/04/14 01:21:05 - mmengine - INFO - Epoch(train) [26][1360/1879] lr: 2.0000e-02 eta: 14:21:32 time: 0.3848 data_time: 0.0156 memory: 6717 grad_norm: 2.7272 loss: 1.8046 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8046 2023/04/14 01:21:13 - mmengine - INFO - Epoch(train) [26][1380/1879] lr: 2.0000e-02 eta: 14:21:26 time: 0.3855 data_time: 0.0717 memory: 6717 grad_norm: 2.8100 loss: 1.6809 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6809 2023/04/14 01:21:20 - mmengine - INFO - Epoch(train) [26][1400/1879] lr: 2.0000e-02 eta: 14:21:19 time: 0.3738 data_time: 0.0358 memory: 6717 grad_norm: 2.7243 loss: 1.4228 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.4228 2023/04/14 01:21:28 - mmengine - INFO - Epoch(train) [26][1420/1879] lr: 2.0000e-02 eta: 14:21:10 time: 0.3580 data_time: 0.0160 memory: 6717 grad_norm: 2.7558 loss: 1.7110 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7110 2023/04/14 01:21:36 - mmengine - INFO - Epoch(train) [26][1440/1879] lr: 2.0000e-02 eta: 14:21:05 time: 0.4096 data_time: 0.0150 memory: 6717 grad_norm: 2.7918 loss: 1.9372 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9372 2023/04/14 01:21:42 - mmengine - INFO - Epoch(train) [26][1460/1879] lr: 2.0000e-02 eta: 14:20:56 time: 0.3389 data_time: 0.0137 memory: 6717 grad_norm: 2.7686 loss: 1.5931 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.5931 2023/04/14 01:21:50 - mmengine - INFO - Epoch(train) [26][1480/1879] lr: 2.0000e-02 eta: 14:20:48 time: 0.3658 data_time: 0.0229 memory: 6717 grad_norm: 2.6911 loss: 1.8304 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8304 2023/04/14 01:21:57 - mmengine - INFO - Epoch(train) [26][1500/1879] lr: 2.0000e-02 eta: 14:20:41 time: 0.3695 data_time: 0.0125 memory: 6717 grad_norm: 2.7441 loss: 1.7531 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7531 2023/04/14 01:22:05 - mmengine - INFO - Epoch(train) [26][1520/1879] lr: 2.0000e-02 eta: 14:20:34 time: 0.3750 data_time: 0.0165 memory: 6717 grad_norm: 2.7753 loss: 1.8197 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8197 2023/04/14 01:22:12 - mmengine - INFO - Epoch(train) [26][1540/1879] lr: 2.0000e-02 eta: 14:20:25 time: 0.3534 data_time: 0.0127 memory: 6717 grad_norm: 2.7302 loss: 1.8089 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.8089 2023/04/14 01:22:20 - mmengine - INFO - Epoch(train) [26][1560/1879] lr: 2.0000e-02 eta: 14:20:22 time: 0.4340 data_time: 0.0162 memory: 6717 grad_norm: 2.7063 loss: 1.7846 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7846 2023/04/14 01:22:27 - mmengine - INFO - Epoch(train) [26][1580/1879] lr: 2.0000e-02 eta: 14:20:13 time: 0.3432 data_time: 0.0139 memory: 6717 grad_norm: 2.7783 loss: 1.8872 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8872 2023/04/14 01:22:35 - mmengine - INFO - Epoch(train) [26][1600/1879] lr: 2.0000e-02 eta: 14:20:06 time: 0.3767 data_time: 0.0129 memory: 6717 grad_norm: 2.6973 loss: 1.6443 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.6443 2023/04/14 01:22:42 - mmengine - INFO - Epoch(train) [26][1620/1879] lr: 2.0000e-02 eta: 14:19:57 time: 0.3443 data_time: 0.0152 memory: 6717 grad_norm: 2.7552 loss: 1.7388 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.7388 2023/04/14 01:22:50 - mmengine - INFO - Epoch(train) [26][1640/1879] lr: 2.0000e-02 eta: 14:19:51 time: 0.4056 data_time: 0.0133 memory: 6717 grad_norm: 2.6510 loss: 1.7186 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7186 2023/04/14 01:22:56 - mmengine - INFO - Epoch(train) [26][1660/1879] lr: 2.0000e-02 eta: 14:19:41 time: 0.3237 data_time: 0.0163 memory: 6717 grad_norm: 2.8006 loss: 1.9078 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9078 2023/04/14 01:23:04 - mmengine - INFO - Epoch(train) [26][1680/1879] lr: 2.0000e-02 eta: 14:19:35 time: 0.3958 data_time: 0.0137 memory: 6717 grad_norm: 2.6687 loss: 1.7833 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7833 2023/04/14 01:23:12 - mmengine - INFO - Epoch(train) [26][1700/1879] lr: 2.0000e-02 eta: 14:19:28 time: 0.3650 data_time: 0.0152 memory: 6717 grad_norm: 2.7444 loss: 1.7488 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7488 2023/04/14 01:23:18 - mmengine - INFO - Epoch(train) [26][1720/1879] lr: 2.0000e-02 eta: 14:19:19 time: 0.3454 data_time: 0.0128 memory: 6717 grad_norm: 2.7442 loss: 1.7647 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7647 2023/04/14 01:23:26 - mmengine - INFO - Epoch(train) [26][1740/1879] lr: 2.0000e-02 eta: 14:19:11 time: 0.3617 data_time: 0.0156 memory: 6717 grad_norm: 2.6788 loss: 1.7211 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7211 2023/04/14 01:23:33 - mmengine - INFO - Epoch(train) [26][1760/1879] lr: 2.0000e-02 eta: 14:19:03 time: 0.3647 data_time: 0.0129 memory: 6717 grad_norm: 2.7683 loss: 1.8677 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8677 2023/04/14 01:23:40 - mmengine - INFO - Epoch(train) [26][1780/1879] lr: 2.0000e-02 eta: 14:18:54 time: 0.3425 data_time: 0.0157 memory: 6717 grad_norm: 2.6749 loss: 1.7653 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7653 2023/04/14 01:23:48 - mmengine - INFO - Epoch(train) [26][1800/1879] lr: 2.0000e-02 eta: 14:18:50 time: 0.4247 data_time: 0.0147 memory: 6717 grad_norm: 2.7761 loss: 1.7185 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7185 2023/04/14 01:23:55 - mmengine - INFO - Epoch(train) [26][1820/1879] lr: 2.0000e-02 eta: 14:18:41 time: 0.3438 data_time: 0.0164 memory: 6717 grad_norm: 2.7556 loss: 1.6319 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6319 2023/04/14 01:24:04 - mmengine - INFO - Epoch(train) [26][1840/1879] lr: 2.0000e-02 eta: 14:18:36 time: 0.4157 data_time: 0.0128 memory: 6717 grad_norm: 2.7564 loss: 1.8152 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8152 2023/04/14 01:24:10 - mmengine - INFO - Epoch(train) [26][1860/1879] lr: 2.0000e-02 eta: 14:18:26 time: 0.3206 data_time: 0.0157 memory: 6717 grad_norm: 2.7992 loss: 1.8788 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.8788 2023/04/14 01:24:16 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 01:24:16 - mmengine - INFO - Epoch(train) [26][1879/1879] lr: 2.0000e-02 eta: 14:18:16 time: 0.3034 data_time: 0.0123 memory: 6717 grad_norm: 2.7765 loss: 1.6891 top1_acc: 0.0000 top5_acc: 0.5714 loss_cls: 1.6891 2023/04/14 01:24:25 - mmengine - INFO - Epoch(val) [26][ 20/155] eta: 0:01:02 time: 0.4634 data_time: 0.4298 memory: 1391 2023/04/14 01:24:31 - mmengine - INFO - Epoch(val) [26][ 40/155] eta: 0:00:44 time: 0.3111 data_time: 0.2781 memory: 1391 2023/04/14 01:24:40 - mmengine - INFO - Epoch(val) [26][ 60/155] eta: 0:00:38 time: 0.4386 data_time: 0.4050 memory: 1391 2023/04/14 01:24:46 - mmengine - INFO - Epoch(val) [26][ 80/155] eta: 0:00:28 time: 0.3172 data_time: 0.2837 memory: 1391 2023/04/14 01:24:56 - mmengine - INFO - Epoch(val) [26][100/155] eta: 0:00:21 time: 0.4547 data_time: 0.4219 memory: 1391 2023/04/14 01:25:02 - mmengine - INFO - Epoch(val) [26][120/155] eta: 0:00:13 time: 0.2980 data_time: 0.2657 memory: 1391 2023/04/14 01:25:11 - mmengine - INFO - Epoch(val) [26][140/155] eta: 0:00:05 time: 0.4492 data_time: 0.4157 memory: 1391 2023/04/14 01:25:18 - mmengine - INFO - Epoch(val) [26][155/155] acc/top1: 0.5859 acc/top5: 0.8275 acc/mean1: 0.5857 data_time: 0.3758 time: 0.4085 2023/04/14 01:25:28 - mmengine - INFO - Epoch(train) [27][ 20/1879] lr: 2.0000e-02 eta: 14:18:15 time: 0.4859 data_time: 0.2648 memory: 6717 grad_norm: 2.7550 loss: 1.6463 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6463 2023/04/14 01:25:34 - mmengine - INFO - Epoch(train) [27][ 40/1879] lr: 2.0000e-02 eta: 14:18:05 time: 0.3233 data_time: 0.1110 memory: 6717 grad_norm: 2.7362 loss: 1.8067 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8067 2023/04/14 01:25:42 - mmengine - INFO - Epoch(train) [27][ 60/1879] lr: 2.0000e-02 eta: 14:17:59 time: 0.4084 data_time: 0.1564 memory: 6717 grad_norm: 2.6945 loss: 1.6780 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6780 2023/04/14 01:25:48 - mmengine - INFO - Epoch(train) [27][ 80/1879] lr: 2.0000e-02 eta: 14:17:49 time: 0.3116 data_time: 0.0844 memory: 6717 grad_norm: 2.7596 loss: 1.6915 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.6915 2023/04/14 01:25:56 - mmengine - INFO - Epoch(train) [27][ 100/1879] lr: 2.0000e-02 eta: 14:17:43 time: 0.4046 data_time: 0.1578 memory: 6717 grad_norm: 2.7327 loss: 1.6739 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6739 2023/04/14 01:26:03 - mmengine - INFO - Epoch(train) [27][ 120/1879] lr: 2.0000e-02 eta: 14:17:34 time: 0.3430 data_time: 0.0882 memory: 6717 grad_norm: 2.8196 loss: 1.7368 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7368 2023/04/14 01:26:12 - mmengine - INFO - Epoch(train) [27][ 140/1879] lr: 2.0000e-02 eta: 14:17:30 time: 0.4262 data_time: 0.0278 memory: 6717 grad_norm: 2.7428 loss: 1.6391 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6391 2023/04/14 01:26:15 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 01:26:19 - mmengine - INFO - Epoch(train) [27][ 160/1879] lr: 2.0000e-02 eta: 14:17:21 time: 0.3508 data_time: 0.0126 memory: 6717 grad_norm: 2.7755 loss: 1.7680 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7680 2023/04/14 01:26:27 - mmengine - INFO - Epoch(train) [27][ 180/1879] lr: 2.0000e-02 eta: 14:17:16 time: 0.4057 data_time: 0.0138 memory: 6717 grad_norm: 2.7137 loss: 1.6064 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6064 2023/04/14 01:26:33 - mmengine - INFO - Epoch(train) [27][ 200/1879] lr: 2.0000e-02 eta: 14:17:06 time: 0.3180 data_time: 0.0156 memory: 6717 grad_norm: 2.8434 loss: 1.7391 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7391 2023/04/14 01:26:42 - mmengine - INFO - Epoch(train) [27][ 220/1879] lr: 2.0000e-02 eta: 14:17:01 time: 0.4216 data_time: 0.0157 memory: 6717 grad_norm: 2.7765 loss: 1.7944 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7944 2023/04/14 01:26:48 - mmengine - INFO - Epoch(train) [27][ 240/1879] lr: 2.0000e-02 eta: 14:16:51 time: 0.3132 data_time: 0.0150 memory: 6717 grad_norm: 2.7923 loss: 1.6849 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6849 2023/04/14 01:26:56 - mmengine - INFO - Epoch(train) [27][ 260/1879] lr: 2.0000e-02 eta: 14:16:45 time: 0.3950 data_time: 0.0159 memory: 6717 grad_norm: 2.7856 loss: 1.7025 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7025 2023/04/14 01:27:03 - mmengine - INFO - Epoch(train) [27][ 280/1879] lr: 2.0000e-02 eta: 14:16:35 time: 0.3282 data_time: 0.0129 memory: 6717 grad_norm: 2.7347 loss: 1.5849 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5849 2023/04/14 01:27:12 - mmengine - INFO - Epoch(train) [27][ 300/1879] lr: 2.0000e-02 eta: 14:16:32 time: 0.4503 data_time: 0.0162 memory: 6717 grad_norm: 2.8337 loss: 1.7469 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.7469 2023/04/14 01:27:18 - mmengine - INFO - Epoch(train) [27][ 320/1879] lr: 2.0000e-02 eta: 14:16:23 time: 0.3450 data_time: 0.0121 memory: 6717 grad_norm: 2.7588 loss: 1.7763 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.7763 2023/04/14 01:27:26 - mmengine - INFO - Epoch(train) [27][ 340/1879] lr: 2.0000e-02 eta: 14:16:16 time: 0.3863 data_time: 0.0137 memory: 6717 grad_norm: 2.8033 loss: 1.6953 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6953 2023/04/14 01:27:33 - mmengine - INFO - Epoch(train) [27][ 360/1879] lr: 2.0000e-02 eta: 14:16:07 time: 0.3350 data_time: 0.0146 memory: 6717 grad_norm: 2.7830 loss: 1.6960 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6960 2023/04/14 01:27:41 - mmengine - INFO - Epoch(train) [27][ 380/1879] lr: 2.0000e-02 eta: 14:16:01 time: 0.3974 data_time: 0.0167 memory: 6717 grad_norm: 2.7712 loss: 1.6348 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6348 2023/04/14 01:27:48 - mmengine - INFO - Epoch(train) [27][ 400/1879] lr: 2.0000e-02 eta: 14:15:52 time: 0.3390 data_time: 0.0163 memory: 6717 grad_norm: 2.8185 loss: 1.6731 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6731 2023/04/14 01:27:56 - mmengine - INFO - Epoch(train) [27][ 420/1879] lr: 2.0000e-02 eta: 14:15:48 time: 0.4296 data_time: 0.0135 memory: 6717 grad_norm: 2.7287 loss: 1.5962 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.5962 2023/04/14 01:28:02 - mmengine - INFO - Epoch(train) [27][ 440/1879] lr: 2.0000e-02 eta: 14:15:37 time: 0.3068 data_time: 0.0132 memory: 6717 grad_norm: 3.1411 loss: 1.7270 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7270 2023/04/14 01:28:10 - mmengine - INFO - Epoch(train) [27][ 460/1879] lr: 2.0000e-02 eta: 14:15:31 time: 0.4037 data_time: 0.0132 memory: 6717 grad_norm: 2.7791 loss: 2.0479 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 2.0479 2023/04/14 01:28:17 - mmengine - INFO - Epoch(train) [27][ 480/1879] lr: 2.0000e-02 eta: 14:15:20 time: 0.3056 data_time: 0.0144 memory: 6717 grad_norm: 2.7748 loss: 1.7545 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.7545 2023/04/14 01:28:25 - mmengine - INFO - Epoch(train) [27][ 500/1879] lr: 2.0000e-02 eta: 14:15:17 time: 0.4416 data_time: 0.0132 memory: 6717 grad_norm: 2.8630 loss: 1.3934 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.3934 2023/04/14 01:28:32 - mmengine - INFO - Epoch(train) [27][ 520/1879] lr: 2.0000e-02 eta: 14:15:06 time: 0.3112 data_time: 0.0149 memory: 6717 grad_norm: 2.8664 loss: 1.8507 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8507 2023/04/14 01:28:40 - mmengine - INFO - Epoch(train) [27][ 540/1879] lr: 2.0000e-02 eta: 14:15:01 time: 0.4093 data_time: 0.0135 memory: 6717 grad_norm: 2.7298 loss: 1.7168 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7168 2023/04/14 01:28:46 - mmengine - INFO - Epoch(train) [27][ 560/1879] lr: 2.0000e-02 eta: 14:14:50 time: 0.3088 data_time: 0.0147 memory: 6717 grad_norm: 2.8973 loss: 2.0189 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 2.0189 2023/04/14 01:28:54 - mmengine - INFO - Epoch(train) [27][ 580/1879] lr: 2.0000e-02 eta: 14:14:45 time: 0.4111 data_time: 0.0146 memory: 6717 grad_norm: 2.6524 loss: 1.6231 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6231 2023/04/14 01:29:01 - mmengine - INFO - Epoch(train) [27][ 600/1879] lr: 2.0000e-02 eta: 14:14:35 time: 0.3186 data_time: 0.0149 memory: 6717 grad_norm: 2.7839 loss: 1.6787 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6787 2023/04/14 01:29:09 - mmengine - INFO - Epoch(train) [27][ 620/1879] lr: 2.0000e-02 eta: 14:14:29 time: 0.4094 data_time: 0.0162 memory: 6717 grad_norm: 2.6769 loss: 1.6806 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6806 2023/04/14 01:29:16 - mmengine - INFO - Epoch(train) [27][ 640/1879] lr: 2.0000e-02 eta: 14:14:22 time: 0.3705 data_time: 0.1085 memory: 6717 grad_norm: 2.7297 loss: 1.7613 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7613 2023/04/14 01:29:23 - mmengine - INFO - Epoch(train) [27][ 660/1879] lr: 2.0000e-02 eta: 14:14:13 time: 0.3488 data_time: 0.0970 memory: 6717 grad_norm: 2.7107 loss: 1.7634 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.7634 2023/04/14 01:29:31 - mmengine - INFO - Epoch(train) [27][ 680/1879] lr: 2.0000e-02 eta: 14:14:07 time: 0.3928 data_time: 0.1929 memory: 6717 grad_norm: 2.7587 loss: 1.6380 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6380 2023/04/14 01:29:38 - mmengine - INFO - Epoch(train) [27][ 700/1879] lr: 2.0000e-02 eta: 14:13:59 time: 0.3484 data_time: 0.1235 memory: 6717 grad_norm: 2.7359 loss: 1.7065 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7065 2023/04/14 01:29:46 - mmengine - INFO - Epoch(train) [27][ 720/1879] lr: 2.0000e-02 eta: 14:13:52 time: 0.3887 data_time: 0.1337 memory: 6717 grad_norm: 2.7572 loss: 1.6101 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6101 2023/04/14 01:29:54 - mmengine - INFO - Epoch(train) [27][ 740/1879] lr: 2.0000e-02 eta: 14:13:47 time: 0.4153 data_time: 0.2229 memory: 6717 grad_norm: 2.8140 loss: 1.8832 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8832 2023/04/14 01:30:01 - mmengine - INFO - Epoch(train) [27][ 760/1879] lr: 2.0000e-02 eta: 14:13:39 time: 0.3468 data_time: 0.2110 memory: 6717 grad_norm: 2.7423 loss: 1.6906 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.6906 2023/04/14 01:30:09 - mmengine - INFO - Epoch(train) [27][ 780/1879] lr: 2.0000e-02 eta: 14:13:34 time: 0.4180 data_time: 0.2795 memory: 6717 grad_norm: 2.6933 loss: 1.8103 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8103 2023/04/14 01:30:16 - mmengine - INFO - Epoch(train) [27][ 800/1879] lr: 2.0000e-02 eta: 14:13:24 time: 0.3270 data_time: 0.1863 memory: 6717 grad_norm: 2.7659 loss: 1.7239 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7239 2023/04/14 01:30:24 - mmengine - INFO - Epoch(train) [27][ 820/1879] lr: 2.0000e-02 eta: 14:13:20 time: 0.4268 data_time: 0.2851 memory: 6717 grad_norm: 2.8264 loss: 1.7524 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.7524 2023/04/14 01:30:31 - mmengine - INFO - Epoch(train) [27][ 840/1879] lr: 2.0000e-02 eta: 14:13:10 time: 0.3290 data_time: 0.1902 memory: 6717 grad_norm: 2.8065 loss: 1.6007 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6007 2023/04/14 01:30:39 - mmengine - INFO - Epoch(train) [27][ 860/1879] lr: 2.0000e-02 eta: 14:13:05 time: 0.4165 data_time: 0.2766 memory: 6717 grad_norm: 2.7155 loss: 1.5430 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.5430 2023/04/14 01:30:46 - mmengine - INFO - Epoch(train) [27][ 880/1879] lr: 2.0000e-02 eta: 14:12:56 time: 0.3308 data_time: 0.1908 memory: 6717 grad_norm: 2.7815 loss: 1.7575 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7575 2023/04/14 01:30:54 - mmengine - INFO - Epoch(train) [27][ 900/1879] lr: 2.0000e-02 eta: 14:12:50 time: 0.4029 data_time: 0.2619 memory: 6717 grad_norm: 2.7367 loss: 1.8965 top1_acc: 0.3125 top5_acc: 0.8750 loss_cls: 1.8965 2023/04/14 01:31:01 - mmengine - INFO - Epoch(train) [27][ 920/1879] lr: 2.0000e-02 eta: 14:12:40 time: 0.3248 data_time: 0.1819 memory: 6717 grad_norm: 2.7185 loss: 1.6521 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6521 2023/04/14 01:31:09 - mmengine - INFO - Epoch(train) [27][ 940/1879] lr: 2.0000e-02 eta: 14:12:36 time: 0.4233 data_time: 0.2821 memory: 6717 grad_norm: 2.7455 loss: 1.9658 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9658 2023/04/14 01:31:15 - mmengine - INFO - Epoch(train) [27][ 960/1879] lr: 2.0000e-02 eta: 14:12:26 time: 0.3236 data_time: 0.1814 memory: 6717 grad_norm: 2.7809 loss: 1.8328 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 1.8328 2023/04/14 01:31:23 - mmengine - INFO - Epoch(train) [27][ 980/1879] lr: 2.0000e-02 eta: 14:12:19 time: 0.3784 data_time: 0.2367 memory: 6717 grad_norm: 2.7017 loss: 1.6539 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6539 2023/04/14 01:31:29 - mmengine - INFO - Epoch(train) [27][1000/1879] lr: 2.0000e-02 eta: 14:12:08 time: 0.3196 data_time: 0.1747 memory: 6717 grad_norm: 2.8307 loss: 1.7750 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7750 2023/04/14 01:31:38 - mmengine - INFO - Epoch(train) [27][1020/1879] lr: 2.0000e-02 eta: 14:12:04 time: 0.4180 data_time: 0.2771 memory: 6717 grad_norm: 2.8024 loss: 1.5796 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5796 2023/04/14 01:31:44 - mmengine - INFO - Epoch(train) [27][1040/1879] lr: 2.0000e-02 eta: 14:11:53 time: 0.3187 data_time: 0.1814 memory: 6717 grad_norm: 2.7084 loss: 1.5809 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5809 2023/04/14 01:31:52 - mmengine - INFO - Epoch(train) [27][1060/1879] lr: 2.0000e-02 eta: 14:11:48 time: 0.3979 data_time: 0.2552 memory: 6717 grad_norm: 2.7726 loss: 1.6276 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6276 2023/04/14 01:31:59 - mmengine - INFO - Epoch(train) [27][1080/1879] lr: 2.0000e-02 eta: 14:11:38 time: 0.3267 data_time: 0.1289 memory: 6717 grad_norm: 2.8068 loss: 1.5027 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5027 2023/04/14 01:32:07 - mmengine - INFO - Epoch(train) [27][1100/1879] lr: 2.0000e-02 eta: 14:11:33 time: 0.4281 data_time: 0.1702 memory: 6717 grad_norm: 2.7375 loss: 1.7016 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7016 2023/04/14 01:32:13 - mmengine - INFO - Epoch(train) [27][1120/1879] lr: 2.0000e-02 eta: 14:11:23 time: 0.3137 data_time: 0.1234 memory: 6717 grad_norm: 2.7539 loss: 1.5880 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.5880 2023/04/14 01:32:22 - mmengine - INFO - Epoch(train) [27][1140/1879] lr: 2.0000e-02 eta: 14:11:20 time: 0.4430 data_time: 0.1842 memory: 6717 grad_norm: 2.7220 loss: 1.8913 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8913 2023/04/14 01:32:24 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 01:32:29 - mmengine - INFO - Epoch(train) [27][1160/1879] lr: 2.0000e-02 eta: 14:11:10 time: 0.3277 data_time: 0.0939 memory: 6717 grad_norm: 2.7504 loss: 1.7300 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7300 2023/04/14 01:32:37 - mmengine - INFO - Epoch(train) [27][1180/1879] lr: 2.0000e-02 eta: 14:11:05 time: 0.4167 data_time: 0.0794 memory: 6717 grad_norm: 2.7756 loss: 1.8026 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8026 2023/04/14 01:32:43 - mmengine - INFO - Epoch(train) [27][1200/1879] lr: 2.0000e-02 eta: 14:10:54 time: 0.3084 data_time: 0.0901 memory: 6717 grad_norm: 2.7770 loss: 1.6989 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6989 2023/04/14 01:32:51 - mmengine - INFO - Epoch(train) [27][1220/1879] lr: 2.0000e-02 eta: 14:10:48 time: 0.3917 data_time: 0.2153 memory: 6717 grad_norm: 2.7319 loss: 1.7534 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.7534 2023/04/14 01:32:59 - mmengine - INFO - Epoch(train) [27][1240/1879] lr: 2.0000e-02 eta: 14:10:41 time: 0.3805 data_time: 0.2217 memory: 6717 grad_norm: 2.7917 loss: 1.7773 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7773 2023/04/14 01:33:06 - mmengine - INFO - Epoch(train) [27][1260/1879] lr: 2.0000e-02 eta: 14:10:34 time: 0.3744 data_time: 0.1663 memory: 6717 grad_norm: 2.7398 loss: 1.7034 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7034 2023/04/14 01:33:14 - mmengine - INFO - Epoch(train) [27][1280/1879] lr: 2.0000e-02 eta: 14:10:28 time: 0.4063 data_time: 0.2492 memory: 6717 grad_norm: 2.8713 loss: 1.6777 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6777 2023/04/14 01:33:21 - mmengine - INFO - Epoch(train) [27][1300/1879] lr: 2.0000e-02 eta: 14:10:18 time: 0.3240 data_time: 0.1802 memory: 6717 grad_norm: 2.7132 loss: 1.7466 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7466 2023/04/14 01:33:29 - mmengine - INFO - Epoch(train) [27][1320/1879] lr: 2.0000e-02 eta: 14:10:12 time: 0.3927 data_time: 0.2522 memory: 6717 grad_norm: 2.8752 loss: 1.6136 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6136 2023/04/14 01:33:35 - mmengine - INFO - Epoch(train) [27][1340/1879] lr: 2.0000e-02 eta: 14:10:03 time: 0.3312 data_time: 0.1886 memory: 6717 grad_norm: 2.7787 loss: 1.8807 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 1.8807 2023/04/14 01:33:43 - mmengine - INFO - Epoch(train) [27][1360/1879] lr: 2.0000e-02 eta: 14:09:57 time: 0.3976 data_time: 0.2579 memory: 6717 grad_norm: 2.8059 loss: 1.9492 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.9492 2023/04/14 01:33:50 - mmengine - INFO - Epoch(train) [27][1380/1879] lr: 2.0000e-02 eta: 14:09:48 time: 0.3523 data_time: 0.2119 memory: 6717 grad_norm: 2.7853 loss: 1.8573 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8573 2023/04/14 01:33:59 - mmengine - INFO - Epoch(train) [27][1400/1879] lr: 2.0000e-02 eta: 14:09:44 time: 0.4174 data_time: 0.2800 memory: 6717 grad_norm: 2.7108 loss: 1.6973 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6973 2023/04/14 01:34:05 - mmengine - INFO - Epoch(train) [27][1420/1879] lr: 2.0000e-02 eta: 14:09:32 time: 0.3021 data_time: 0.1651 memory: 6717 grad_norm: 2.7379 loss: 1.6873 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6873 2023/04/14 01:34:12 - mmengine - INFO - Epoch(train) [27][1440/1879] lr: 2.0000e-02 eta: 14:09:25 time: 0.3687 data_time: 0.1519 memory: 6717 grad_norm: 2.7652 loss: 1.7108 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7108 2023/04/14 01:34:20 - mmengine - INFO - Epoch(train) [27][1460/1879] lr: 2.0000e-02 eta: 14:09:18 time: 0.3742 data_time: 0.0348 memory: 6717 grad_norm: 2.7365 loss: 1.7851 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7851 2023/04/14 01:34:27 - mmengine - INFO - Epoch(train) [27][1480/1879] lr: 2.0000e-02 eta: 14:09:10 time: 0.3587 data_time: 0.1377 memory: 6717 grad_norm: 2.6957 loss: 1.7083 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7083 2023/04/14 01:34:34 - mmengine - INFO - Epoch(train) [27][1500/1879] lr: 2.0000e-02 eta: 14:09:02 time: 0.3605 data_time: 0.1838 memory: 6717 grad_norm: 2.8024 loss: 1.8826 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8826 2023/04/14 01:34:42 - mmengine - INFO - Epoch(train) [27][1520/1879] lr: 2.0000e-02 eta: 14:08:56 time: 0.4047 data_time: 0.2702 memory: 6717 grad_norm: 2.7705 loss: 1.8371 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8371 2023/04/14 01:34:49 - mmengine - INFO - Epoch(train) [27][1540/1879] lr: 2.0000e-02 eta: 14:08:46 time: 0.3162 data_time: 0.1578 memory: 6717 grad_norm: 2.7496 loss: 1.5522 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5522 2023/04/14 01:34:56 - mmengine - INFO - Epoch(train) [27][1560/1879] lr: 2.0000e-02 eta: 14:08:39 time: 0.3766 data_time: 0.1582 memory: 6717 grad_norm: 2.6872 loss: 1.5888 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5888 2023/04/14 01:35:03 - mmengine - INFO - Epoch(train) [27][1580/1879] lr: 2.0000e-02 eta: 14:08:31 time: 0.3721 data_time: 0.0771 memory: 6717 grad_norm: 2.7092 loss: 1.6159 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6159 2023/04/14 01:35:11 - mmengine - INFO - Epoch(train) [27][1600/1879] lr: 2.0000e-02 eta: 14:08:26 time: 0.3994 data_time: 0.2132 memory: 6717 grad_norm: 2.7463 loss: 1.9850 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9850 2023/04/14 01:35:19 - mmengine - INFO - Epoch(train) [27][1620/1879] lr: 2.0000e-02 eta: 14:08:18 time: 0.3728 data_time: 0.0933 memory: 6717 grad_norm: 2.7777 loss: 1.5530 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5530 2023/04/14 01:35:26 - mmengine - INFO - Epoch(train) [27][1640/1879] lr: 2.0000e-02 eta: 14:08:10 time: 0.3458 data_time: 0.0935 memory: 6717 grad_norm: 2.6895 loss: 1.7331 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7331 2023/04/14 01:35:33 - mmengine - INFO - Epoch(train) [27][1660/1879] lr: 2.0000e-02 eta: 14:08:01 time: 0.3412 data_time: 0.0843 memory: 6717 grad_norm: 2.7819 loss: 1.6215 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6215 2023/04/14 01:35:41 - mmengine - INFO - Epoch(train) [27][1680/1879] lr: 2.0000e-02 eta: 14:07:56 time: 0.4157 data_time: 0.1538 memory: 6717 grad_norm: 2.7734 loss: 1.7178 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7178 2023/04/14 01:35:47 - mmengine - INFO - Epoch(train) [27][1700/1879] lr: 2.0000e-02 eta: 14:07:46 time: 0.3213 data_time: 0.1060 memory: 6717 grad_norm: 2.7428 loss: 1.7108 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7108 2023/04/14 01:35:56 - mmengine - INFO - Epoch(train) [27][1720/1879] lr: 2.0000e-02 eta: 14:07:42 time: 0.4315 data_time: 0.2680 memory: 6717 grad_norm: 2.8048 loss: 1.7589 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7589 2023/04/14 01:36:02 - mmengine - INFO - Epoch(train) [27][1740/1879] lr: 2.0000e-02 eta: 14:07:30 time: 0.3005 data_time: 0.1401 memory: 6717 grad_norm: 2.7480 loss: 1.7590 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.7590 2023/04/14 01:36:10 - mmengine - INFO - Epoch(train) [27][1760/1879] lr: 2.0000e-02 eta: 14:07:25 time: 0.4023 data_time: 0.0679 memory: 6717 grad_norm: 2.6651 loss: 1.7630 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7630 2023/04/14 01:36:17 - mmengine - INFO - Epoch(train) [27][1780/1879] lr: 2.0000e-02 eta: 14:07:17 time: 0.3662 data_time: 0.0270 memory: 6717 grad_norm: 2.6835 loss: 1.6786 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.6786 2023/04/14 01:36:25 - mmengine - INFO - Epoch(train) [27][1800/1879] lr: 2.0000e-02 eta: 14:07:11 time: 0.4024 data_time: 0.0180 memory: 6717 grad_norm: 2.7875 loss: 1.6603 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.6603 2023/04/14 01:36:32 - mmengine - INFO - Epoch(train) [27][1820/1879] lr: 2.0000e-02 eta: 14:07:02 time: 0.3316 data_time: 0.0128 memory: 6717 grad_norm: 2.7780 loss: 1.7229 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7229 2023/04/14 01:36:41 - mmengine - INFO - Epoch(train) [27][1840/1879] lr: 2.0000e-02 eta: 14:06:57 time: 0.4235 data_time: 0.0137 memory: 6717 grad_norm: 2.7431 loss: 1.6625 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6625 2023/04/14 01:36:47 - mmengine - INFO - Epoch(train) [27][1860/1879] lr: 2.0000e-02 eta: 14:06:47 time: 0.3084 data_time: 0.0138 memory: 6717 grad_norm: 2.6933 loss: 1.5288 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5288 2023/04/14 01:36:54 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 01:36:54 - mmengine - INFO - Epoch(train) [27][1879/1879] lr: 2.0000e-02 eta: 14:06:40 time: 0.3730 data_time: 0.0126 memory: 6717 grad_norm: 2.7865 loss: 1.7256 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 1.7256 2023/04/14 01:36:54 - mmengine - INFO - Saving checkpoint at 27 epochs 2023/04/14 01:37:04 - mmengine - INFO - Epoch(val) [27][ 20/155] eta: 0:01:02 time: 0.4648 data_time: 0.4323 memory: 1391 2023/04/14 01:37:10 - mmengine - INFO - Epoch(val) [27][ 40/155] eta: 0:00:45 time: 0.3189 data_time: 0.2858 memory: 1391 2023/04/14 01:37:19 - mmengine - INFO - Epoch(val) [27][ 60/155] eta: 0:00:38 time: 0.4248 data_time: 0.3920 memory: 1391 2023/04/14 01:37:25 - mmengine - INFO - Epoch(val) [27][ 80/155] eta: 0:00:28 time: 0.3144 data_time: 0.2811 memory: 1391 2023/04/14 01:37:34 - mmengine - INFO - Epoch(val) [27][100/155] eta: 0:00:21 time: 0.4570 data_time: 0.4241 memory: 1391 2023/04/14 01:37:40 - mmengine - INFO - Epoch(val) [27][120/155] eta: 0:00:13 time: 0.3011 data_time: 0.2680 memory: 1391 2023/04/14 01:37:49 - mmengine - INFO - Epoch(val) [27][140/155] eta: 0:00:05 time: 0.4438 data_time: 0.4102 memory: 1391 2023/04/14 01:37:56 - mmengine - INFO - Epoch(val) [27][155/155] acc/top1: 0.6001 acc/top5: 0.8328 acc/mean1: 0.6001 data_time: 0.3922 time: 0.4250 2023/04/14 01:37:56 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_25.pth is removed 2023/04/14 01:37:56 - mmengine - INFO - The best checkpoint with 0.6001 acc/top1 at 27 epoch is saved to best_acc_top1_epoch_27.pth. 2023/04/14 01:38:06 - mmengine - INFO - Epoch(train) [28][ 20/1879] lr: 2.0000e-02 eta: 14:06:40 time: 0.5029 data_time: 0.3661 memory: 6717 grad_norm: 2.7695 loss: 1.5755 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.5755 2023/04/14 01:38:13 - mmengine - INFO - Epoch(train) [28][ 40/1879] lr: 2.0000e-02 eta: 14:06:31 time: 0.3401 data_time: 0.2143 memory: 6717 grad_norm: 2.7896 loss: 1.6599 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6599 2023/04/14 01:38:21 - mmengine - INFO - Epoch(train) [28][ 60/1879] lr: 2.0000e-02 eta: 14:06:26 time: 0.4148 data_time: 0.2256 memory: 6717 grad_norm: 2.7851 loss: 1.5733 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5733 2023/04/14 01:38:28 - mmengine - INFO - Epoch(train) [28][ 80/1879] lr: 2.0000e-02 eta: 14:06:17 time: 0.3322 data_time: 0.1289 memory: 6717 grad_norm: 2.7592 loss: 1.6520 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6520 2023/04/14 01:38:36 - mmengine - INFO - Epoch(train) [28][ 100/1879] lr: 2.0000e-02 eta: 14:06:10 time: 0.3927 data_time: 0.1703 memory: 6717 grad_norm: 2.7690 loss: 1.5180 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5180 2023/04/14 01:38:42 - mmengine - INFO - Epoch(train) [28][ 120/1879] lr: 2.0000e-02 eta: 14:06:00 time: 0.3228 data_time: 0.1285 memory: 6717 grad_norm: 2.7567 loss: 1.4258 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4258 2023/04/14 01:38:50 - mmengine - INFO - Epoch(train) [28][ 140/1879] lr: 2.0000e-02 eta: 14:05:55 time: 0.4020 data_time: 0.1538 memory: 6717 grad_norm: 2.7989 loss: 1.8265 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8265 2023/04/14 01:38:57 - mmengine - INFO - Epoch(train) [28][ 160/1879] lr: 2.0000e-02 eta: 14:05:44 time: 0.3102 data_time: 0.0442 memory: 6717 grad_norm: 2.8409 loss: 1.6364 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6364 2023/04/14 01:39:05 - mmengine - INFO - Epoch(train) [28][ 180/1879] lr: 2.0000e-02 eta: 14:05:39 time: 0.4118 data_time: 0.0571 memory: 6717 grad_norm: 2.7398 loss: 1.7787 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.7787 2023/04/14 01:39:11 - mmengine - INFO - Epoch(train) [28][ 200/1879] lr: 2.0000e-02 eta: 14:05:29 time: 0.3272 data_time: 0.0401 memory: 6717 grad_norm: 2.9521 loss: 1.7530 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7530 2023/04/14 01:39:19 - mmengine - INFO - Epoch(train) [28][ 220/1879] lr: 2.0000e-02 eta: 14:05:23 time: 0.4042 data_time: 0.0239 memory: 6717 grad_norm: 2.8188 loss: 1.8234 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.8234 2023/04/14 01:39:26 - mmengine - INFO - Epoch(train) [28][ 240/1879] lr: 2.0000e-02 eta: 14:05:15 time: 0.3457 data_time: 0.0151 memory: 6717 grad_norm: 2.7065 loss: 1.5442 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.5442 2023/04/14 01:39:35 - mmengine - INFO - Epoch(train) [28][ 260/1879] lr: 2.0000e-02 eta: 14:05:10 time: 0.4147 data_time: 0.0503 memory: 6717 grad_norm: 2.8452 loss: 1.6718 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6718 2023/04/14 01:39:37 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 01:39:41 - mmengine - INFO - Epoch(train) [28][ 280/1879] lr: 2.0000e-02 eta: 14:04:59 time: 0.3177 data_time: 0.0245 memory: 6717 grad_norm: 2.7844 loss: 1.8066 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.8066 2023/04/14 01:39:49 - mmengine - INFO - Epoch(train) [28][ 300/1879] lr: 2.0000e-02 eta: 14:04:54 time: 0.4145 data_time: 0.0178 memory: 6717 grad_norm: 2.8774 loss: 1.5672 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5672 2023/04/14 01:39:56 - mmengine - INFO - Epoch(train) [28][ 320/1879] lr: 2.0000e-02 eta: 14:04:45 time: 0.3230 data_time: 0.0118 memory: 6717 grad_norm: 2.6979 loss: 1.5518 top1_acc: 0.3750 top5_acc: 0.9375 loss_cls: 1.5518 2023/04/14 01:40:05 - mmengine - INFO - Epoch(train) [28][ 340/1879] lr: 2.0000e-02 eta: 14:04:41 time: 0.4375 data_time: 0.0146 memory: 6717 grad_norm: 2.7707 loss: 1.7314 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7314 2023/04/14 01:40:11 - mmengine - INFO - Epoch(train) [28][ 360/1879] lr: 2.0000e-02 eta: 14:04:32 time: 0.3426 data_time: 0.0146 memory: 6717 grad_norm: 2.7829 loss: 1.7018 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7018 2023/04/14 01:40:20 - mmengine - INFO - Epoch(train) [28][ 380/1879] lr: 2.0000e-02 eta: 14:04:28 time: 0.4362 data_time: 0.0162 memory: 6717 grad_norm: 2.7329 loss: 1.6496 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.6496 2023/04/14 01:40:26 - mmengine - INFO - Epoch(train) [28][ 400/1879] lr: 2.0000e-02 eta: 14:04:16 time: 0.2932 data_time: 0.0129 memory: 6717 grad_norm: 2.7787 loss: 1.4992 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4992 2023/04/14 01:40:35 - mmengine - INFO - Epoch(train) [28][ 420/1879] lr: 2.0000e-02 eta: 14:04:13 time: 0.4424 data_time: 0.0144 memory: 6717 grad_norm: 2.7564 loss: 1.8353 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8353 2023/04/14 01:40:41 - mmengine - INFO - Epoch(train) [28][ 440/1879] lr: 2.0000e-02 eta: 14:04:03 time: 0.3226 data_time: 0.0144 memory: 6717 grad_norm: 2.6977 loss: 1.6178 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.6178 2023/04/14 01:40:50 - mmengine - INFO - Epoch(train) [28][ 460/1879] lr: 2.0000e-02 eta: 14:03:59 time: 0.4322 data_time: 0.0131 memory: 6717 grad_norm: 2.7190 loss: 1.6581 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6581 2023/04/14 01:40:57 - mmengine - INFO - Epoch(train) [28][ 480/1879] lr: 2.0000e-02 eta: 14:03:50 time: 0.3400 data_time: 0.0168 memory: 6717 grad_norm: 2.7390 loss: 1.7621 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7621 2023/04/14 01:41:05 - mmengine - INFO - Epoch(train) [28][ 500/1879] lr: 2.0000e-02 eta: 14:03:45 time: 0.4155 data_time: 0.0146 memory: 6717 grad_norm: 2.8133 loss: 1.5514 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5514 2023/04/14 01:41:12 - mmengine - INFO - Epoch(train) [28][ 520/1879] lr: 2.0000e-02 eta: 14:03:35 time: 0.3237 data_time: 0.0140 memory: 6717 grad_norm: 2.7849 loss: 1.8914 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.8914 2023/04/14 01:41:20 - mmengine - INFO - Epoch(train) [28][ 540/1879] lr: 2.0000e-02 eta: 14:03:31 time: 0.4311 data_time: 0.0145 memory: 6717 grad_norm: 2.7711 loss: 1.7470 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7470 2023/04/14 01:41:26 - mmengine - INFO - Epoch(train) [28][ 560/1879] lr: 2.0000e-02 eta: 14:03:20 time: 0.3117 data_time: 0.0145 memory: 6717 grad_norm: 2.7656 loss: 1.7534 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7534 2023/04/14 01:41:34 - mmengine - INFO - Epoch(train) [28][ 580/1879] lr: 2.0000e-02 eta: 14:03:12 time: 0.3603 data_time: 0.0156 memory: 6717 grad_norm: 2.7679 loss: 1.8537 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8537 2023/04/14 01:41:40 - mmengine - INFO - Epoch(train) [28][ 600/1879] lr: 2.0000e-02 eta: 14:03:02 time: 0.3148 data_time: 0.0138 memory: 6717 grad_norm: 2.7652 loss: 1.6488 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6488 2023/04/14 01:41:49 - mmengine - INFO - Epoch(train) [28][ 620/1879] lr: 2.0000e-02 eta: 14:03:00 time: 0.4792 data_time: 0.0151 memory: 6717 grad_norm: 2.7619 loss: 1.6452 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.6452 2023/04/14 01:41:56 - mmengine - INFO - Epoch(train) [28][ 640/1879] lr: 2.0000e-02 eta: 14:02:51 time: 0.3398 data_time: 0.0127 memory: 6717 grad_norm: 2.8468 loss: 1.7111 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.7111 2023/04/14 01:42:04 - mmengine - INFO - Epoch(train) [28][ 660/1879] lr: 2.0000e-02 eta: 14:02:44 time: 0.3752 data_time: 0.0140 memory: 6717 grad_norm: 2.8245 loss: 1.8352 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8352 2023/04/14 01:42:11 - mmengine - INFO - Epoch(train) [28][ 680/1879] lr: 2.0000e-02 eta: 14:02:36 time: 0.3581 data_time: 0.0141 memory: 6717 grad_norm: 2.7649 loss: 1.7754 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7754 2023/04/14 01:42:19 - mmengine - INFO - Epoch(train) [28][ 700/1879] lr: 2.0000e-02 eta: 14:02:31 time: 0.4140 data_time: 0.0161 memory: 6717 grad_norm: 2.7342 loss: 1.7382 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7382 2023/04/14 01:42:25 - mmengine - INFO - Epoch(train) [28][ 720/1879] lr: 2.0000e-02 eta: 14:02:19 time: 0.2906 data_time: 0.0131 memory: 6717 grad_norm: 2.7492 loss: 1.6359 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6359 2023/04/14 01:42:34 - mmengine - INFO - Epoch(train) [28][ 740/1879] lr: 2.0000e-02 eta: 14:02:15 time: 0.4276 data_time: 0.0161 memory: 6717 grad_norm: 2.8384 loss: 1.7258 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.7258 2023/04/14 01:42:40 - mmengine - INFO - Epoch(train) [28][ 760/1879] lr: 2.0000e-02 eta: 14:02:06 time: 0.3391 data_time: 0.0130 memory: 6717 grad_norm: 2.7475 loss: 1.7611 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7611 2023/04/14 01:42:48 - mmengine - INFO - Epoch(train) [28][ 780/1879] lr: 2.0000e-02 eta: 14:02:00 time: 0.4036 data_time: 0.0155 memory: 6717 grad_norm: 2.7162 loss: 1.5324 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 1.5324 2023/04/14 01:42:55 - mmengine - INFO - Epoch(train) [28][ 800/1879] lr: 2.0000e-02 eta: 14:01:50 time: 0.3187 data_time: 0.0132 memory: 6717 grad_norm: 2.7316 loss: 1.6507 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6507 2023/04/14 01:43:02 - mmengine - INFO - Epoch(train) [28][ 820/1879] lr: 2.0000e-02 eta: 14:01:43 time: 0.3755 data_time: 0.0163 memory: 6717 grad_norm: 2.7347 loss: 1.6669 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6669 2023/04/14 01:43:09 - mmengine - INFO - Epoch(train) [28][ 840/1879] lr: 2.0000e-02 eta: 14:01:32 time: 0.3123 data_time: 0.0132 memory: 6717 grad_norm: 2.7789 loss: 1.8467 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 1.8467 2023/04/14 01:43:17 - mmengine - INFO - Epoch(train) [28][ 860/1879] lr: 2.0000e-02 eta: 14:01:28 time: 0.4304 data_time: 0.0192 memory: 6717 grad_norm: 2.8287 loss: 1.7333 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.7333 2023/04/14 01:43:24 - mmengine - INFO - Epoch(train) [28][ 880/1879] lr: 2.0000e-02 eta: 14:01:19 time: 0.3398 data_time: 0.0341 memory: 6717 grad_norm: 2.6558 loss: 1.6649 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6649 2023/04/14 01:43:32 - mmengine - INFO - Epoch(train) [28][ 900/1879] lr: 2.0000e-02 eta: 14:01:13 time: 0.3884 data_time: 0.0484 memory: 6717 grad_norm: 2.7628 loss: 1.6108 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6108 2023/04/14 01:43:39 - mmengine - INFO - Epoch(train) [28][ 920/1879] lr: 2.0000e-02 eta: 14:01:04 time: 0.3549 data_time: 0.0539 memory: 6717 grad_norm: 2.7619 loss: 2.0016 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0016 2023/04/14 01:43:47 - mmengine - INFO - Epoch(train) [28][ 940/1879] lr: 2.0000e-02 eta: 14:00:58 time: 0.3873 data_time: 0.0976 memory: 6717 grad_norm: 2.7719 loss: 1.6719 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6719 2023/04/14 01:43:54 - mmengine - INFO - Epoch(train) [28][ 960/1879] lr: 2.0000e-02 eta: 14:00:50 time: 0.3695 data_time: 0.1312 memory: 6717 grad_norm: 2.7976 loss: 1.7737 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7737 2023/04/14 01:44:02 - mmengine - INFO - Epoch(train) [28][ 980/1879] lr: 2.0000e-02 eta: 14:00:46 time: 0.4204 data_time: 0.0345 memory: 6717 grad_norm: 2.7661 loss: 1.8923 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.8923 2023/04/14 01:44:09 - mmengine - INFO - Epoch(train) [28][1000/1879] lr: 2.0000e-02 eta: 14:00:37 time: 0.3389 data_time: 0.0140 memory: 6717 grad_norm: 2.7553 loss: 1.6991 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6991 2023/04/14 01:44:18 - mmengine - INFO - Epoch(train) [28][1020/1879] lr: 2.0000e-02 eta: 14:00:32 time: 0.4236 data_time: 0.0141 memory: 6717 grad_norm: 2.7624 loss: 1.7339 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7339 2023/04/14 01:44:24 - mmengine - INFO - Epoch(train) [28][1040/1879] lr: 2.0000e-02 eta: 14:00:23 time: 0.3365 data_time: 0.0133 memory: 6717 grad_norm: 2.7603 loss: 1.8438 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8438 2023/04/14 01:44:33 - mmengine - INFO - Epoch(train) [28][1060/1879] lr: 2.0000e-02 eta: 14:00:18 time: 0.4264 data_time: 0.0140 memory: 6717 grad_norm: 2.7426 loss: 1.8447 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8447 2023/04/14 01:44:40 - mmengine - INFO - Epoch(train) [28][1080/1879] lr: 2.0000e-02 eta: 14:00:09 time: 0.3323 data_time: 0.0140 memory: 6717 grad_norm: 2.7567 loss: 1.8179 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8179 2023/04/14 01:44:48 - mmengine - INFO - Epoch(train) [28][1100/1879] lr: 2.0000e-02 eta: 14:00:03 time: 0.4049 data_time: 0.0142 memory: 6717 grad_norm: 2.7956 loss: 1.5959 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5959 2023/04/14 01:44:55 - mmengine - INFO - Epoch(train) [28][1120/1879] lr: 2.0000e-02 eta: 13:59:55 time: 0.3466 data_time: 0.0189 memory: 6717 grad_norm: 2.7671 loss: 1.7450 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7450 2023/04/14 01:45:03 - mmengine - INFO - Epoch(train) [28][1140/1879] lr: 2.0000e-02 eta: 13:59:50 time: 0.4283 data_time: 0.0142 memory: 6717 grad_norm: 2.7496 loss: 1.7270 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7270 2023/04/14 01:45:09 - mmengine - INFO - Epoch(train) [28][1160/1879] lr: 2.0000e-02 eta: 13:59:40 time: 0.3135 data_time: 0.0185 memory: 6717 grad_norm: 2.7520 loss: 1.7131 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7131 2023/04/14 01:45:18 - mmengine - INFO - Epoch(train) [28][1180/1879] lr: 2.0000e-02 eta: 13:59:35 time: 0.4114 data_time: 0.0150 memory: 6717 grad_norm: 2.8054 loss: 1.8128 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8128 2023/04/14 01:45:24 - mmengine - INFO - Epoch(train) [28][1200/1879] lr: 2.0000e-02 eta: 13:59:25 time: 0.3227 data_time: 0.0141 memory: 6717 grad_norm: 2.7235 loss: 1.6762 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6762 2023/04/14 01:45:32 - mmengine - INFO - Epoch(train) [28][1220/1879] lr: 2.0000e-02 eta: 13:59:20 time: 0.4163 data_time: 0.0144 memory: 6717 grad_norm: 2.8374 loss: 1.9486 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9486 2023/04/14 01:45:39 - mmengine - INFO - Epoch(train) [28][1240/1879] lr: 2.0000e-02 eta: 13:59:09 time: 0.3032 data_time: 0.0140 memory: 6717 grad_norm: 2.7569 loss: 1.8611 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8611 2023/04/14 01:45:46 - mmengine - INFO - Epoch(train) [28][1260/1879] lr: 2.0000e-02 eta: 13:59:02 time: 0.3883 data_time: 0.0143 memory: 6717 grad_norm: 2.7024 loss: 1.7739 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7739 2023/04/14 01:45:48 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 01:45:53 - mmengine - INFO - Epoch(train) [28][1280/1879] lr: 2.0000e-02 eta: 13:58:54 time: 0.3472 data_time: 0.0150 memory: 6717 grad_norm: 3.6357 loss: 1.6568 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6568 2023/04/14 01:46:01 - mmengine - INFO - Epoch(train) [28][1300/1879] lr: 2.0000e-02 eta: 13:58:48 time: 0.3957 data_time: 0.0156 memory: 6717 grad_norm: 2.8955 loss: 1.7567 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.7567 2023/04/14 01:46:08 - mmengine - INFO - Epoch(train) [28][1320/1879] lr: 2.0000e-02 eta: 13:58:38 time: 0.3248 data_time: 0.0132 memory: 6717 grad_norm: 2.8231 loss: 1.8527 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8527 2023/04/14 01:46:16 - mmengine - INFO - Epoch(train) [28][1340/1879] lr: 2.0000e-02 eta: 13:58:32 time: 0.4006 data_time: 0.0162 memory: 6717 grad_norm: 2.7658 loss: 1.8229 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8229 2023/04/14 01:46:22 - mmengine - INFO - Epoch(train) [28][1360/1879] lr: 2.0000e-02 eta: 13:58:22 time: 0.3243 data_time: 0.0457 memory: 6717 grad_norm: 2.7276 loss: 1.7208 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.7208 2023/04/14 01:46:31 - mmengine - INFO - Epoch(train) [28][1380/1879] lr: 2.0000e-02 eta: 13:58:18 time: 0.4273 data_time: 0.0857 memory: 6717 grad_norm: 2.7594 loss: 1.7193 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7193 2023/04/14 01:46:37 - mmengine - INFO - Epoch(train) [28][1400/1879] lr: 2.0000e-02 eta: 13:58:09 time: 0.3378 data_time: 0.0765 memory: 6717 grad_norm: 2.7881 loss: 1.8475 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8475 2023/04/14 01:46:45 - mmengine - INFO - Epoch(train) [28][1420/1879] lr: 2.0000e-02 eta: 13:58:02 time: 0.3835 data_time: 0.0498 memory: 6717 grad_norm: 2.7438 loss: 1.8878 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8878 2023/04/14 01:46:52 - mmengine - INFO - Epoch(train) [28][1440/1879] lr: 2.0000e-02 eta: 13:57:53 time: 0.3432 data_time: 0.0135 memory: 6717 grad_norm: 2.8575 loss: 1.8756 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.8756 2023/04/14 01:47:00 - mmengine - INFO - Epoch(train) [28][1460/1879] lr: 2.0000e-02 eta: 13:57:47 time: 0.3960 data_time: 0.0155 memory: 6717 grad_norm: 2.8109 loss: 1.7821 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7821 2023/04/14 01:47:06 - mmengine - INFO - Epoch(train) [28][1480/1879] lr: 2.0000e-02 eta: 13:57:37 time: 0.3211 data_time: 0.0156 memory: 6717 grad_norm: 2.6666 loss: 1.7186 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7186 2023/04/14 01:47:14 - mmengine - INFO - Epoch(train) [28][1500/1879] lr: 2.0000e-02 eta: 13:57:31 time: 0.3950 data_time: 0.0195 memory: 6717 grad_norm: 2.8219 loss: 1.7856 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7856 2023/04/14 01:47:21 - mmengine - INFO - Epoch(train) [28][1520/1879] lr: 2.0000e-02 eta: 13:57:22 time: 0.3423 data_time: 0.0431 memory: 6717 grad_norm: 2.6864 loss: 1.6542 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6542 2023/04/14 01:47:29 - mmengine - INFO - Epoch(train) [28][1540/1879] lr: 2.0000e-02 eta: 13:57:15 time: 0.3796 data_time: 0.0820 memory: 6717 grad_norm: 2.7130 loss: 1.5145 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5145 2023/04/14 01:47:36 - mmengine - INFO - Epoch(train) [28][1560/1879] lr: 2.0000e-02 eta: 13:57:08 time: 0.3827 data_time: 0.1845 memory: 6717 grad_norm: 2.7678 loss: 1.6431 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6431 2023/04/14 01:47:44 - mmengine - INFO - Epoch(train) [28][1580/1879] lr: 2.0000e-02 eta: 13:57:01 time: 0.3819 data_time: 0.2101 memory: 6717 grad_norm: 2.8000 loss: 1.9794 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.9794 2023/04/14 01:47:51 - mmengine - INFO - Epoch(train) [28][1600/1879] lr: 2.0000e-02 eta: 13:56:52 time: 0.3377 data_time: 0.1265 memory: 6717 grad_norm: 2.6870 loss: 1.8039 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8039 2023/04/14 01:47:58 - mmengine - INFO - Epoch(train) [28][1620/1879] lr: 2.0000e-02 eta: 13:56:45 time: 0.3734 data_time: 0.1248 memory: 6717 grad_norm: 2.7117 loss: 1.7082 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7082 2023/04/14 01:48:05 - mmengine - INFO - Epoch(train) [28][1640/1879] lr: 2.0000e-02 eta: 13:56:36 time: 0.3330 data_time: 0.0300 memory: 6717 grad_norm: 2.6987 loss: 1.7905 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7905 2023/04/14 01:48:13 - mmengine - INFO - Epoch(train) [28][1660/1879] lr: 2.0000e-02 eta: 13:56:31 time: 0.4178 data_time: 0.0994 memory: 6717 grad_norm: 2.6578 loss: 1.6903 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6903 2023/04/14 01:48:20 - mmengine - INFO - Epoch(train) [28][1680/1879] lr: 2.0000e-02 eta: 13:56:22 time: 0.3393 data_time: 0.1195 memory: 6717 grad_norm: 2.7821 loss: 1.8759 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8759 2023/04/14 01:48:28 - mmengine - INFO - Epoch(train) [28][1700/1879] lr: 2.0000e-02 eta: 13:56:15 time: 0.3888 data_time: 0.1756 memory: 6717 grad_norm: 2.7519 loss: 1.8253 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8253 2023/04/14 01:48:35 - mmengine - INFO - Epoch(train) [28][1720/1879] lr: 2.0000e-02 eta: 13:56:07 time: 0.3472 data_time: 0.1438 memory: 6717 grad_norm: 2.6705 loss: 1.6233 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.6233 2023/04/14 01:48:43 - mmengine - INFO - Epoch(train) [28][1740/1879] lr: 2.0000e-02 eta: 13:56:01 time: 0.4022 data_time: 0.2557 memory: 6717 grad_norm: 2.6716 loss: 1.7806 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7806 2023/04/14 01:48:49 - mmengine - INFO - Epoch(train) [28][1760/1879] lr: 2.0000e-02 eta: 13:55:51 time: 0.3282 data_time: 0.1878 memory: 6717 grad_norm: 2.7397 loss: 1.9494 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9494 2023/04/14 01:48:57 - mmengine - INFO - Epoch(train) [28][1780/1879] lr: 2.0000e-02 eta: 13:55:45 time: 0.3912 data_time: 0.2531 memory: 6717 grad_norm: 2.7255 loss: 1.8672 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.8672 2023/04/14 01:49:04 - mmengine - INFO - Epoch(train) [28][1800/1879] lr: 2.0000e-02 eta: 13:55:37 time: 0.3518 data_time: 0.1397 memory: 6717 grad_norm: 2.8064 loss: 1.8790 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8790 2023/04/14 01:49:12 - mmengine - INFO - Epoch(train) [28][1820/1879] lr: 2.0000e-02 eta: 13:55:30 time: 0.3909 data_time: 0.1243 memory: 6717 grad_norm: 2.7827 loss: 1.8787 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8787 2023/04/14 01:49:19 - mmengine - INFO - Epoch(train) [28][1840/1879] lr: 2.0000e-02 eta: 13:55:22 time: 0.3436 data_time: 0.1101 memory: 6717 grad_norm: 2.7684 loss: 1.7464 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7464 2023/04/14 01:49:27 - mmengine - INFO - Epoch(train) [28][1860/1879] lr: 2.0000e-02 eta: 13:55:16 time: 0.4121 data_time: 0.2581 memory: 6717 grad_norm: 2.8046 loss: 1.8737 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8737 2023/04/14 01:49:33 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 01:49:33 - mmengine - INFO - Epoch(train) [28][1879/1879] lr: 2.0000e-02 eta: 13:55:06 time: 0.2999 data_time: 0.1676 memory: 6717 grad_norm: 2.7305 loss: 1.6376 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.6376 2023/04/14 01:49:42 - mmengine - INFO - Epoch(val) [28][ 20/155] eta: 0:01:02 time: 0.4612 data_time: 0.4286 memory: 1391 2023/04/14 01:49:49 - mmengine - INFO - Epoch(val) [28][ 40/155] eta: 0:00:44 time: 0.3204 data_time: 0.2869 memory: 1391 2023/04/14 01:49:57 - mmengine - INFO - Epoch(val) [28][ 60/155] eta: 0:00:38 time: 0.4197 data_time: 0.3865 memory: 1391 2023/04/14 01:50:04 - mmengine - INFO - Epoch(val) [28][ 80/155] eta: 0:00:28 time: 0.3276 data_time: 0.2950 memory: 1391 2023/04/14 01:50:12 - mmengine - INFO - Epoch(val) [28][100/155] eta: 0:00:21 time: 0.4184 data_time: 0.3851 memory: 1391 2023/04/14 01:50:19 - mmengine - INFO - Epoch(val) [28][120/155] eta: 0:00:13 time: 0.3389 data_time: 0.3053 memory: 1391 2023/04/14 01:50:28 - mmengine - INFO - Epoch(val) [28][140/155] eta: 0:00:05 time: 0.4863 data_time: 0.4534 memory: 1391 2023/04/14 01:50:36 - mmengine - INFO - Epoch(val) [28][155/155] acc/top1: 0.6007 acc/top5: 0.8357 acc/mean1: 0.6007 data_time: 0.4227 time: 0.4548 2023/04/14 01:50:36 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_27.pth is removed 2023/04/14 01:50:36 - mmengine - INFO - The best checkpoint with 0.6007 acc/top1 at 28 epoch is saved to best_acc_top1_epoch_28.pth. 2023/04/14 01:50:46 - mmengine - INFO - Epoch(train) [29][ 20/1879] lr: 2.0000e-02 eta: 13:55:04 time: 0.4827 data_time: 0.3385 memory: 6717 grad_norm: 2.7437 loss: 1.6289 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6289 2023/04/14 01:50:52 - mmengine - INFO - Epoch(train) [29][ 40/1879] lr: 2.0000e-02 eta: 13:54:54 time: 0.3204 data_time: 0.1403 memory: 6717 grad_norm: 2.7985 loss: 1.7783 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7783 2023/04/14 01:51:00 - mmengine - INFO - Epoch(train) [29][ 60/1879] lr: 2.0000e-02 eta: 13:54:49 time: 0.4135 data_time: 0.1263 memory: 6717 grad_norm: 2.7996 loss: 1.6977 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6977 2023/04/14 01:51:07 - mmengine - INFO - Epoch(train) [29][ 80/1879] lr: 2.0000e-02 eta: 13:54:40 time: 0.3394 data_time: 0.0301 memory: 6717 grad_norm: 2.7017 loss: 1.5495 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5495 2023/04/14 01:51:16 - mmengine - INFO - Epoch(train) [29][ 100/1879] lr: 2.0000e-02 eta: 13:54:37 time: 0.4418 data_time: 0.0142 memory: 6717 grad_norm: 2.6666 loss: 1.8049 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8049 2023/04/14 01:51:22 - mmengine - INFO - Epoch(train) [29][ 120/1879] lr: 2.0000e-02 eta: 13:54:25 time: 0.2845 data_time: 0.0144 memory: 6717 grad_norm: 2.7564 loss: 1.6406 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6406 2023/04/14 01:51:30 - mmengine - INFO - Epoch(train) [29][ 140/1879] lr: 2.0000e-02 eta: 13:54:19 time: 0.4041 data_time: 0.0152 memory: 6717 grad_norm: 2.7407 loss: 1.8303 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 1.8303 2023/04/14 01:51:37 - mmengine - INFO - Epoch(train) [29][ 160/1879] lr: 2.0000e-02 eta: 13:54:11 time: 0.3488 data_time: 0.0134 memory: 6717 grad_norm: 2.9181 loss: 1.7451 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.7451 2023/04/14 01:51:44 - mmengine - INFO - Epoch(train) [29][ 180/1879] lr: 2.0000e-02 eta: 13:54:03 time: 0.3747 data_time: 0.0140 memory: 6717 grad_norm: 2.7788 loss: 1.6012 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6012 2023/04/14 01:51:51 - mmengine - INFO - Epoch(train) [29][ 200/1879] lr: 2.0000e-02 eta: 13:53:55 time: 0.3581 data_time: 0.0153 memory: 6717 grad_norm: 2.7586 loss: 1.7354 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7354 2023/04/14 01:51:59 - mmengine - INFO - Epoch(train) [29][ 220/1879] lr: 2.0000e-02 eta: 13:53:49 time: 0.3835 data_time: 0.0147 memory: 6717 grad_norm: 2.7553 loss: 1.7262 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7262 2023/04/14 01:52:06 - mmengine - INFO - Epoch(train) [29][ 240/1879] lr: 2.0000e-02 eta: 13:53:39 time: 0.3268 data_time: 0.0141 memory: 6717 grad_norm: 2.7628 loss: 1.7359 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7359 2023/04/14 01:52:14 - mmengine - INFO - Epoch(train) [29][ 260/1879] lr: 2.0000e-02 eta: 13:53:35 time: 0.4345 data_time: 0.0139 memory: 6717 grad_norm: 2.7291 loss: 1.8657 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8657 2023/04/14 01:52:21 - mmengine - INFO - Epoch(train) [29][ 280/1879] lr: 2.0000e-02 eta: 13:53:26 time: 0.3354 data_time: 0.0144 memory: 6717 grad_norm: 2.7438 loss: 1.6912 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6912 2023/04/14 01:52:29 - mmengine - INFO - Epoch(train) [29][ 300/1879] lr: 2.0000e-02 eta: 13:53:19 time: 0.3850 data_time: 0.0141 memory: 6717 grad_norm: 2.6592 loss: 1.6318 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6318 2023/04/14 01:52:36 - mmengine - INFO - Epoch(train) [29][ 320/1879] lr: 2.0000e-02 eta: 13:53:12 time: 0.3705 data_time: 0.0149 memory: 6717 grad_norm: 2.7354 loss: 1.7346 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7346 2023/04/14 01:52:43 - mmengine - INFO - Epoch(train) [29][ 340/1879] lr: 2.0000e-02 eta: 13:53:03 time: 0.3452 data_time: 0.0125 memory: 6717 grad_norm: 2.8584 loss: 1.5583 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.5583 2023/04/14 01:52:51 - mmengine - INFO - Epoch(train) [29][ 360/1879] lr: 2.0000e-02 eta: 13:52:56 time: 0.3800 data_time: 0.0147 memory: 6717 grad_norm: 2.7208 loss: 1.5493 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.5493 2023/04/14 01:52:58 - mmengine - INFO - Epoch(train) [29][ 380/1879] lr: 2.0000e-02 eta: 13:52:47 time: 0.3394 data_time: 0.0145 memory: 6717 grad_norm: 2.8016 loss: 1.5853 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.5853 2023/04/14 01:53:01 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 01:53:06 - mmengine - INFO - Epoch(train) [29][ 400/1879] lr: 2.0000e-02 eta: 13:52:42 time: 0.4232 data_time: 0.0156 memory: 6717 grad_norm: 2.7842 loss: 1.7657 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7657 2023/04/14 01:53:12 - mmengine - INFO - Epoch(train) [29][ 420/1879] lr: 2.0000e-02 eta: 13:52:32 time: 0.3175 data_time: 0.0153 memory: 6717 grad_norm: 2.6819 loss: 1.5630 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.5630 2023/04/14 01:53:21 - mmengine - INFO - Epoch(train) [29][ 440/1879] lr: 2.0000e-02 eta: 13:52:27 time: 0.4225 data_time: 0.0146 memory: 6717 grad_norm: 2.8126 loss: 1.6804 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6804 2023/04/14 01:53:28 - mmengine - INFO - Epoch(train) [29][ 460/1879] lr: 2.0000e-02 eta: 13:52:18 time: 0.3377 data_time: 0.0132 memory: 6717 grad_norm: 2.7327 loss: 1.6499 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.6499 2023/04/14 01:53:36 - mmengine - INFO - Epoch(train) [29][ 480/1879] lr: 2.0000e-02 eta: 13:52:13 time: 0.4130 data_time: 0.0142 memory: 6717 grad_norm: 2.7502 loss: 1.9132 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.9132 2023/04/14 01:53:43 - mmengine - INFO - Epoch(train) [29][ 500/1879] lr: 2.0000e-02 eta: 13:52:04 time: 0.3369 data_time: 0.0133 memory: 6717 grad_norm: 2.7287 loss: 1.6304 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.6304 2023/04/14 01:53:51 - mmengine - INFO - Epoch(train) [29][ 520/1879] lr: 2.0000e-02 eta: 13:52:00 time: 0.4316 data_time: 0.0150 memory: 6717 grad_norm: 2.8234 loss: 1.7690 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7690 2023/04/14 01:53:57 - mmengine - INFO - Epoch(train) [29][ 540/1879] lr: 2.0000e-02 eta: 13:51:48 time: 0.2960 data_time: 0.0126 memory: 6717 grad_norm: 2.7942 loss: 1.8416 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8416 2023/04/14 01:54:05 - mmengine - INFO - Epoch(train) [29][ 560/1879] lr: 2.0000e-02 eta: 13:51:43 time: 0.4152 data_time: 0.0156 memory: 6717 grad_norm: 2.6679 loss: 1.7401 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7401 2023/04/14 01:54:12 - mmengine - INFO - Epoch(train) [29][ 580/1879] lr: 2.0000e-02 eta: 13:51:34 time: 0.3260 data_time: 0.0121 memory: 6717 grad_norm: 2.7783 loss: 1.7787 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7787 2023/04/14 01:54:20 - mmengine - INFO - Epoch(train) [29][ 600/1879] lr: 2.0000e-02 eta: 13:51:27 time: 0.3929 data_time: 0.0154 memory: 6717 grad_norm: 2.7250 loss: 1.6677 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6677 2023/04/14 01:54:26 - mmengine - INFO - Epoch(train) [29][ 620/1879] lr: 2.0000e-02 eta: 13:51:17 time: 0.3068 data_time: 0.0142 memory: 6717 grad_norm: 2.7349 loss: 1.5310 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5310 2023/04/14 01:54:34 - mmengine - INFO - Epoch(train) [29][ 640/1879] lr: 2.0000e-02 eta: 13:51:12 time: 0.4256 data_time: 0.0148 memory: 6717 grad_norm: 2.7103 loss: 1.5890 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5890 2023/04/14 01:54:41 - mmengine - INFO - Epoch(train) [29][ 660/1879] lr: 2.0000e-02 eta: 13:51:03 time: 0.3331 data_time: 0.0133 memory: 6717 grad_norm: 2.8178 loss: 1.6113 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 1.6113 2023/04/14 01:54:50 - mmengine - INFO - Epoch(train) [29][ 680/1879] lr: 2.0000e-02 eta: 13:50:58 time: 0.4263 data_time: 0.0162 memory: 6717 grad_norm: 2.6500 loss: 1.7200 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7200 2023/04/14 01:54:56 - mmengine - INFO - Epoch(train) [29][ 700/1879] lr: 2.0000e-02 eta: 13:50:48 time: 0.3066 data_time: 0.0128 memory: 6717 grad_norm: 2.7528 loss: 1.8017 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8017 2023/04/14 01:55:04 - mmengine - INFO - Epoch(train) [29][ 720/1879] lr: 2.0000e-02 eta: 13:50:43 time: 0.4222 data_time: 0.0211 memory: 6717 grad_norm: 2.7243 loss: 1.6666 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6666 2023/04/14 01:55:10 - mmengine - INFO - Epoch(train) [29][ 740/1879] lr: 2.0000e-02 eta: 13:50:32 time: 0.3003 data_time: 0.0130 memory: 6717 grad_norm: 2.7903 loss: 1.8387 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8387 2023/04/14 01:55:19 - mmengine - INFO - Epoch(train) [29][ 760/1879] lr: 2.0000e-02 eta: 13:50:27 time: 0.4184 data_time: 0.0162 memory: 6717 grad_norm: 2.6446 loss: 1.7842 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7842 2023/04/14 01:55:25 - mmengine - INFO - Epoch(train) [29][ 780/1879] lr: 2.0000e-02 eta: 13:50:18 time: 0.3331 data_time: 0.0124 memory: 6717 grad_norm: 2.7259 loss: 1.7113 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7113 2023/04/14 01:55:33 - mmengine - INFO - Epoch(train) [29][ 800/1879] lr: 2.0000e-02 eta: 13:50:11 time: 0.3852 data_time: 0.0154 memory: 6717 grad_norm: 2.6887 loss: 1.7502 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.7502 2023/04/14 01:55:40 - mmengine - INFO - Epoch(train) [29][ 820/1879] lr: 2.0000e-02 eta: 13:50:01 time: 0.3277 data_time: 0.0487 memory: 6717 grad_norm: 2.7523 loss: 1.7013 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7013 2023/04/14 01:55:49 - mmengine - INFO - Epoch(train) [29][ 840/1879] lr: 2.0000e-02 eta: 13:49:59 time: 0.4611 data_time: 0.0573 memory: 6717 grad_norm: 2.7827 loss: 1.7584 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7584 2023/04/14 01:55:55 - mmengine - INFO - Epoch(train) [29][ 860/1879] lr: 2.0000e-02 eta: 13:49:48 time: 0.3127 data_time: 0.0894 memory: 6717 grad_norm: 2.7250 loss: 1.6092 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6092 2023/04/14 01:56:03 - mmengine - INFO - Epoch(train) [29][ 880/1879] lr: 2.0000e-02 eta: 13:49:42 time: 0.3971 data_time: 0.0917 memory: 6717 grad_norm: 2.7295 loss: 1.7517 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7517 2023/04/14 01:56:10 - mmengine - INFO - Epoch(train) [29][ 900/1879] lr: 2.0000e-02 eta: 13:49:33 time: 0.3292 data_time: 0.0788 memory: 6717 grad_norm: 2.7468 loss: 1.8721 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8721 2023/04/14 01:56:18 - mmengine - INFO - Epoch(train) [29][ 920/1879] lr: 2.0000e-02 eta: 13:49:28 time: 0.4203 data_time: 0.2408 memory: 6717 grad_norm: 2.6023 loss: 1.7062 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7062 2023/04/14 01:56:25 - mmengine - INFO - Epoch(train) [29][ 940/1879] lr: 2.0000e-02 eta: 13:49:19 time: 0.3352 data_time: 0.1188 memory: 6717 grad_norm: 2.7620 loss: 1.6923 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6923 2023/04/14 01:56:32 - mmengine - INFO - Epoch(train) [29][ 960/1879] lr: 2.0000e-02 eta: 13:49:12 time: 0.3892 data_time: 0.1072 memory: 6717 grad_norm: 2.7073 loss: 1.5773 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.5773 2023/04/14 01:56:39 - mmengine - INFO - Epoch(train) [29][ 980/1879] lr: 2.0000e-02 eta: 13:49:03 time: 0.3404 data_time: 0.0820 memory: 6717 grad_norm: 2.7598 loss: 1.6663 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.6663 2023/04/14 01:56:48 - mmengine - INFO - Epoch(train) [29][1000/1879] lr: 2.0000e-02 eta: 13:48:59 time: 0.4246 data_time: 0.2567 memory: 6717 grad_norm: 2.7546 loss: 1.6579 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6579 2023/04/14 01:56:54 - mmengine - INFO - Epoch(train) [29][1020/1879] lr: 2.0000e-02 eta: 13:48:48 time: 0.3037 data_time: 0.1301 memory: 6717 grad_norm: 2.6793 loss: 1.5804 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.5804 2023/04/14 01:57:03 - mmengine - INFO - Epoch(train) [29][1040/1879] lr: 2.0000e-02 eta: 13:48:45 time: 0.4581 data_time: 0.2801 memory: 6717 grad_norm: 2.7946 loss: 1.8946 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.8946 2023/04/14 01:57:09 - mmengine - INFO - Epoch(train) [29][1060/1879] lr: 2.0000e-02 eta: 13:48:34 time: 0.2999 data_time: 0.1360 memory: 6717 grad_norm: 2.6914 loss: 1.6211 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6211 2023/04/14 01:57:17 - mmengine - INFO - Epoch(train) [29][1080/1879] lr: 2.0000e-02 eta: 13:48:28 time: 0.4044 data_time: 0.1541 memory: 6717 grad_norm: 2.7679 loss: 1.7356 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7356 2023/04/14 01:57:23 - mmengine - INFO - Epoch(train) [29][1100/1879] lr: 2.0000e-02 eta: 13:48:18 time: 0.3147 data_time: 0.1390 memory: 6717 grad_norm: 2.7169 loss: 1.6143 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6143 2023/04/14 01:57:31 - mmengine - INFO - Epoch(train) [29][1120/1879] lr: 2.0000e-02 eta: 13:48:11 time: 0.3850 data_time: 0.1315 memory: 6717 grad_norm: 2.7907 loss: 1.7943 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.7943 2023/04/14 01:57:38 - mmengine - INFO - Epoch(train) [29][1140/1879] lr: 2.0000e-02 eta: 13:48:02 time: 0.3331 data_time: 0.0371 memory: 6717 grad_norm: 2.7729 loss: 1.4284 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4284 2023/04/14 01:57:46 - mmengine - INFO - Epoch(train) [29][1160/1879] lr: 2.0000e-02 eta: 13:47:56 time: 0.3960 data_time: 0.0355 memory: 6717 grad_norm: 2.6923 loss: 1.8429 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8429 2023/04/14 01:57:52 - mmengine - INFO - Epoch(train) [29][1180/1879] lr: 2.0000e-02 eta: 13:47:47 time: 0.3341 data_time: 0.0216 memory: 6717 grad_norm: 2.7560 loss: 1.8649 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8649 2023/04/14 01:58:00 - mmengine - INFO - Epoch(train) [29][1200/1879] lr: 2.0000e-02 eta: 13:47:41 time: 0.4012 data_time: 0.0145 memory: 6717 grad_norm: 2.7332 loss: 1.7070 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7070 2023/04/14 01:58:07 - mmengine - INFO - Epoch(train) [29][1220/1879] lr: 2.0000e-02 eta: 13:47:31 time: 0.3296 data_time: 0.0213 memory: 6717 grad_norm: 2.7083 loss: 1.5614 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5614 2023/04/14 01:58:15 - mmengine - INFO - Epoch(train) [29][1240/1879] lr: 2.0000e-02 eta: 13:47:25 time: 0.3897 data_time: 0.0171 memory: 6717 grad_norm: 2.7815 loss: 1.7713 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7713 2023/04/14 01:58:22 - mmengine - INFO - Epoch(train) [29][1260/1879] lr: 2.0000e-02 eta: 13:47:17 time: 0.3602 data_time: 0.1393 memory: 6717 grad_norm: 2.7360 loss: 1.5511 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5511 2023/04/14 01:58:29 - mmengine - INFO - Epoch(train) [29][1280/1879] lr: 2.0000e-02 eta: 13:47:09 time: 0.3484 data_time: 0.1106 memory: 6717 grad_norm: 2.7541 loss: 1.5985 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.5985 2023/04/14 01:58:37 - mmengine - INFO - Epoch(train) [29][1300/1879] lr: 2.0000e-02 eta: 13:47:02 time: 0.3889 data_time: 0.2414 memory: 6717 grad_norm: 2.6143 loss: 1.8343 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8343 2023/04/14 01:58:45 - mmengine - INFO - Epoch(train) [29][1320/1879] lr: 2.0000e-02 eta: 13:46:56 time: 0.3931 data_time: 0.2309 memory: 6717 grad_norm: 2.7341 loss: 1.7355 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7355 2023/04/14 01:58:52 - mmengine - INFO - Epoch(train) [29][1340/1879] lr: 2.0000e-02 eta: 13:46:48 time: 0.3641 data_time: 0.2133 memory: 6717 grad_norm: 2.7784 loss: 1.8727 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8727 2023/04/14 01:58:58 - mmengine - INFO - Epoch(train) [29][1360/1879] lr: 2.0000e-02 eta: 13:46:39 time: 0.3281 data_time: 0.1816 memory: 6717 grad_norm: 2.6592 loss: 1.6333 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6333 2023/04/14 01:59:06 - mmengine - INFO - Epoch(train) [29][1380/1879] lr: 2.0000e-02 eta: 13:46:33 time: 0.4016 data_time: 0.1576 memory: 6717 grad_norm: 2.7358 loss: 1.8368 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.8368 2023/04/14 01:59:10 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 01:59:13 - mmengine - INFO - Epoch(train) [29][1400/1879] lr: 2.0000e-02 eta: 13:46:24 time: 0.3438 data_time: 0.1868 memory: 6717 grad_norm: 2.8086 loss: 1.8889 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.8889 2023/04/14 01:59:22 - mmengine - INFO - Epoch(train) [29][1420/1879] lr: 2.0000e-02 eta: 13:46:19 time: 0.4140 data_time: 0.2358 memory: 6717 grad_norm: 2.7518 loss: 1.9726 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.9726 2023/04/14 01:59:28 - mmengine - INFO - Epoch(train) [29][1440/1879] lr: 2.0000e-02 eta: 13:46:09 time: 0.3249 data_time: 0.1857 memory: 6717 grad_norm: 2.7912 loss: 1.6513 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6513 2023/04/14 01:59:36 - mmengine - INFO - Epoch(train) [29][1460/1879] lr: 2.0000e-02 eta: 13:46:03 time: 0.3984 data_time: 0.2614 memory: 6717 grad_norm: 2.7247 loss: 1.7737 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7737 2023/04/14 01:59:43 - mmengine - INFO - Epoch(train) [29][1480/1879] lr: 2.0000e-02 eta: 13:45:55 time: 0.3482 data_time: 0.1954 memory: 6717 grad_norm: 2.7518 loss: 1.6122 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6122 2023/04/14 01:59:51 - mmengine - INFO - Epoch(train) [29][1500/1879] lr: 2.0000e-02 eta: 13:45:49 time: 0.4035 data_time: 0.2660 memory: 6717 grad_norm: 2.7271 loss: 1.8216 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.8216 2023/04/14 01:59:57 - mmengine - INFO - Epoch(train) [29][1520/1879] lr: 2.0000e-02 eta: 13:45:38 time: 0.3085 data_time: 0.1621 memory: 6717 grad_norm: 2.6696 loss: 1.7457 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.7457 2023/04/14 02:00:05 - mmengine - INFO - Epoch(train) [29][1540/1879] lr: 2.0000e-02 eta: 13:45:32 time: 0.3927 data_time: 0.2173 memory: 6717 grad_norm: 2.7742 loss: 1.4886 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4886 2023/04/14 02:00:12 - mmengine - INFO - Epoch(train) [29][1560/1879] lr: 2.0000e-02 eta: 13:45:24 time: 0.3483 data_time: 0.1715 memory: 6717 grad_norm: 2.6880 loss: 1.7101 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7101 2023/04/14 02:00:20 - mmengine - INFO - Epoch(train) [29][1580/1879] lr: 2.0000e-02 eta: 13:45:17 time: 0.3798 data_time: 0.1911 memory: 6717 grad_norm: 2.6711 loss: 1.9092 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9092 2023/04/14 02:00:27 - mmengine - INFO - Epoch(train) [29][1600/1879] lr: 2.0000e-02 eta: 13:45:10 time: 0.3799 data_time: 0.0762 memory: 6717 grad_norm: 2.7682 loss: 1.7543 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7543 2023/04/14 02:00:34 - mmengine - INFO - Epoch(train) [29][1620/1879] lr: 2.0000e-02 eta: 13:45:01 time: 0.3385 data_time: 0.0331 memory: 6717 grad_norm: 2.7075 loss: 1.5813 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5813 2023/04/14 02:00:42 - mmengine - INFO - Epoch(train) [29][1640/1879] lr: 2.0000e-02 eta: 13:44:55 time: 0.3978 data_time: 0.0296 memory: 6717 grad_norm: 2.7596 loss: 1.6722 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6722 2023/04/14 02:00:49 - mmengine - INFO - Epoch(train) [29][1660/1879] lr: 2.0000e-02 eta: 13:44:47 time: 0.3558 data_time: 0.0146 memory: 6717 grad_norm: 3.1825 loss: 1.8630 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8630 2023/04/14 02:00:56 - mmengine - INFO - Epoch(train) [29][1680/1879] lr: 2.0000e-02 eta: 13:44:39 time: 0.3622 data_time: 0.0139 memory: 6717 grad_norm: 2.7470 loss: 1.7371 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7371 2023/04/14 02:01:04 - mmengine - INFO - Epoch(train) [29][1700/1879] lr: 2.0000e-02 eta: 13:44:31 time: 0.3706 data_time: 0.0141 memory: 6717 grad_norm: 2.7216 loss: 1.7034 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7034 2023/04/14 02:01:11 - mmengine - INFO - Epoch(train) [29][1720/1879] lr: 2.0000e-02 eta: 13:44:24 time: 0.3650 data_time: 0.0150 memory: 6717 grad_norm: 2.7630 loss: 1.8891 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.8891 2023/04/14 02:01:18 - mmengine - INFO - Epoch(train) [29][1740/1879] lr: 2.0000e-02 eta: 13:44:15 time: 0.3551 data_time: 0.0131 memory: 6717 grad_norm: 2.6295 loss: 1.6599 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6599 2023/04/14 02:01:27 - mmengine - INFO - Epoch(train) [29][1760/1879] lr: 2.0000e-02 eta: 13:44:12 time: 0.4456 data_time: 0.0144 memory: 6717 grad_norm: 2.7761 loss: 1.6361 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6361 2023/04/14 02:01:33 - mmengine - INFO - Epoch(train) [29][1780/1879] lr: 2.0000e-02 eta: 13:44:01 time: 0.3089 data_time: 0.0144 memory: 6717 grad_norm: 2.8035 loss: 1.5368 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5368 2023/04/14 02:01:41 - mmengine - INFO - Epoch(train) [29][1800/1879] lr: 2.0000e-02 eta: 13:43:55 time: 0.3999 data_time: 0.0153 memory: 6717 grad_norm: 2.6871 loss: 1.8383 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8383 2023/04/14 02:01:48 - mmengine - INFO - Epoch(train) [29][1820/1879] lr: 2.0000e-02 eta: 13:43:46 time: 0.3303 data_time: 0.0131 memory: 6717 grad_norm: 2.7654 loss: 1.6823 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6823 2023/04/14 02:01:57 - mmengine - INFO - Epoch(train) [29][1840/1879] lr: 2.0000e-02 eta: 13:43:42 time: 0.4317 data_time: 0.0150 memory: 6717 grad_norm: 2.6864 loss: 1.7312 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7312 2023/04/14 02:02:03 - mmengine - INFO - Epoch(train) [29][1860/1879] lr: 2.0000e-02 eta: 13:43:32 time: 0.3229 data_time: 0.0152 memory: 6717 grad_norm: 2.7297 loss: 1.8920 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8920 2023/04/14 02:02:10 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 02:02:10 - mmengine - INFO - Epoch(train) [29][1879/1879] lr: 2.0000e-02 eta: 13:43:24 time: 0.3476 data_time: 0.0133 memory: 6717 grad_norm: 2.8562 loss: 1.7774 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.7774 2023/04/14 02:02:19 - mmengine - INFO - Epoch(val) [29][ 20/155] eta: 0:00:58 time: 0.4339 data_time: 0.4002 memory: 1391 2023/04/14 02:02:25 - mmengine - INFO - Epoch(val) [29][ 40/155] eta: 0:00:44 time: 0.3424 data_time: 0.3093 memory: 1391 2023/04/14 02:02:33 - mmengine - INFO - Epoch(val) [29][ 60/155] eta: 0:00:36 time: 0.3687 data_time: 0.3346 memory: 1391 2023/04/14 02:02:40 - mmengine - INFO - Epoch(val) [29][ 80/155] eta: 0:00:28 time: 0.3763 data_time: 0.3429 memory: 1391 2023/04/14 02:02:49 - mmengine - INFO - Epoch(val) [29][100/155] eta: 0:00:21 time: 0.4235 data_time: 0.3900 memory: 1391 2023/04/14 02:02:55 - mmengine - INFO - Epoch(val) [29][120/155] eta: 0:00:13 time: 0.3134 data_time: 0.2800 memory: 1391 2023/04/14 02:03:03 - mmengine - INFO - Epoch(val) [29][140/155] eta: 0:00:05 time: 0.3741 data_time: 0.3408 memory: 1391 2023/04/14 02:03:11 - mmengine - INFO - Epoch(val) [29][155/155] acc/top1: 0.5991 acc/top5: 0.8289 acc/mean1: 0.5991 data_time: 0.3081 time: 0.3413 2023/04/14 02:03:21 - mmengine - INFO - Epoch(train) [30][ 20/1879] lr: 2.0000e-02 eta: 13:43:22 time: 0.4736 data_time: 0.2368 memory: 6717 grad_norm: 2.7302 loss: 1.5781 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5781 2023/04/14 02:03:28 - mmengine - INFO - Epoch(train) [30][ 40/1879] lr: 2.0000e-02 eta: 13:43:13 time: 0.3325 data_time: 0.0294 memory: 6717 grad_norm: 2.7839 loss: 1.4138 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.4138 2023/04/14 02:03:36 - mmengine - INFO - Epoch(train) [30][ 60/1879] lr: 2.0000e-02 eta: 13:43:07 time: 0.4111 data_time: 0.0142 memory: 6717 grad_norm: 2.7137 loss: 1.7809 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7809 2023/04/14 02:03:43 - mmengine - INFO - Epoch(train) [30][ 80/1879] lr: 2.0000e-02 eta: 13:42:58 time: 0.3353 data_time: 0.0136 memory: 6717 grad_norm: 2.7570 loss: 1.7881 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7881 2023/04/14 02:03:51 - mmengine - INFO - Epoch(train) [30][ 100/1879] lr: 2.0000e-02 eta: 13:42:53 time: 0.4150 data_time: 0.0153 memory: 6717 grad_norm: 2.7319 loss: 1.6350 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6350 2023/04/14 02:03:57 - mmengine - INFO - Epoch(train) [30][ 120/1879] lr: 2.0000e-02 eta: 13:42:43 time: 0.3257 data_time: 0.0625 memory: 6717 grad_norm: 2.7447 loss: 1.8678 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8678 2023/04/14 02:04:06 - mmengine - INFO - Epoch(train) [30][ 140/1879] lr: 2.0000e-02 eta: 13:42:40 time: 0.4452 data_time: 0.0325 memory: 6717 grad_norm: 2.7539 loss: 1.7568 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.7568 2023/04/14 02:04:13 - mmengine - INFO - Epoch(train) [30][ 160/1879] lr: 2.0000e-02 eta: 13:42:31 time: 0.3538 data_time: 0.0415 memory: 6717 grad_norm: 2.8577 loss: 1.7880 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7880 2023/04/14 02:04:21 - mmengine - INFO - Epoch(train) [30][ 180/1879] lr: 2.0000e-02 eta: 13:42:25 time: 0.3919 data_time: 0.0352 memory: 6717 grad_norm: 2.7715 loss: 1.5448 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.5448 2023/04/14 02:04:28 - mmengine - INFO - Epoch(train) [30][ 200/1879] lr: 2.0000e-02 eta: 13:42:15 time: 0.3185 data_time: 0.0304 memory: 6717 grad_norm: 2.7020 loss: 1.5268 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5268 2023/04/14 02:04:36 - mmengine - INFO - Epoch(train) [30][ 220/1879] lr: 2.0000e-02 eta: 13:42:11 time: 0.4373 data_time: 0.0133 memory: 6717 grad_norm: 2.7794 loss: 1.7284 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7284 2023/04/14 02:04:43 - mmengine - INFO - Epoch(train) [30][ 240/1879] lr: 2.0000e-02 eta: 13:42:02 time: 0.3316 data_time: 0.0135 memory: 6717 grad_norm: 2.6755 loss: 1.8492 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8492 2023/04/14 02:04:52 - mmengine - INFO - Epoch(train) [30][ 260/1879] lr: 2.0000e-02 eta: 13:41:57 time: 0.4266 data_time: 0.0153 memory: 6717 grad_norm: 2.7788 loss: 1.7309 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.7309 2023/04/14 02:04:57 - mmengine - INFO - Epoch(train) [30][ 280/1879] lr: 2.0000e-02 eta: 13:41:46 time: 0.2882 data_time: 0.0137 memory: 6717 grad_norm: 2.7895 loss: 1.7298 top1_acc: 0.3750 top5_acc: 0.9375 loss_cls: 1.7298 2023/04/14 02:05:05 - mmengine - INFO - Epoch(train) [30][ 300/1879] lr: 2.0000e-02 eta: 13:41:39 time: 0.3927 data_time: 0.0154 memory: 6717 grad_norm: 2.7700 loss: 1.6904 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6904 2023/04/14 02:05:12 - mmengine - INFO - Epoch(train) [30][ 320/1879] lr: 2.0000e-02 eta: 13:41:30 time: 0.3282 data_time: 0.0130 memory: 6717 grad_norm: 2.6488 loss: 1.8150 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8150 2023/04/14 02:05:20 - mmengine - INFO - Epoch(train) [30][ 340/1879] lr: 2.0000e-02 eta: 13:41:25 time: 0.4202 data_time: 0.0143 memory: 6717 grad_norm: 2.8643 loss: 1.6154 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6154 2023/04/14 02:05:27 - mmengine - INFO - Epoch(train) [30][ 360/1879] lr: 2.0000e-02 eta: 13:41:15 time: 0.3211 data_time: 0.0132 memory: 6717 grad_norm: 2.7213 loss: 1.6592 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6592 2023/04/14 02:05:35 - mmengine - INFO - Epoch(train) [30][ 380/1879] lr: 2.0000e-02 eta: 13:41:09 time: 0.4019 data_time: 0.0130 memory: 6717 grad_norm: 2.7683 loss: 1.6484 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6484 2023/04/14 02:05:41 - mmengine - INFO - Epoch(train) [30][ 400/1879] lr: 2.0000e-02 eta: 13:41:00 time: 0.3292 data_time: 0.0150 memory: 6717 grad_norm: 2.6954 loss: 1.8236 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.8236 2023/04/14 02:05:50 - mmengine - INFO - Epoch(train) [30][ 420/1879] lr: 2.0000e-02 eta: 13:40:55 time: 0.4351 data_time: 0.0136 memory: 6717 grad_norm: 2.7925 loss: 1.8654 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.8654 2023/04/14 02:05:56 - mmengine - INFO - Epoch(train) [30][ 440/1879] lr: 2.0000e-02 eta: 13:40:45 time: 0.3091 data_time: 0.0145 memory: 6717 grad_norm: 2.8187 loss: 1.5948 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.5948 2023/04/14 02:06:05 - mmengine - INFO - Epoch(train) [30][ 460/1879] lr: 2.0000e-02 eta: 13:40:41 time: 0.4375 data_time: 0.0137 memory: 6717 grad_norm: 2.6616 loss: 1.7878 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7878 2023/04/14 02:06:11 - mmengine - INFO - Epoch(train) [30][ 480/1879] lr: 2.0000e-02 eta: 13:40:30 time: 0.3052 data_time: 0.0136 memory: 6717 grad_norm: 2.7665 loss: 1.8149 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.8149 2023/04/14 02:06:19 - mmengine - INFO - Epoch(train) [30][ 500/1879] lr: 2.0000e-02 eta: 13:40:24 time: 0.3979 data_time: 0.0153 memory: 6717 grad_norm: 2.7659 loss: 1.7499 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7499 2023/04/14 02:06:22 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 02:06:26 - mmengine - INFO - Epoch(train) [30][ 520/1879] lr: 2.0000e-02 eta: 13:40:15 time: 0.3312 data_time: 0.0130 memory: 6717 grad_norm: 2.7543 loss: 1.4381 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4381 2023/04/14 02:06:34 - mmengine - INFO - Epoch(train) [30][ 540/1879] lr: 2.0000e-02 eta: 13:40:11 time: 0.4310 data_time: 0.0139 memory: 6717 grad_norm: 2.7926 loss: 1.7644 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7644 2023/04/14 02:06:40 - mmengine - INFO - Epoch(train) [30][ 560/1879] lr: 2.0000e-02 eta: 13:39:59 time: 0.2827 data_time: 0.0138 memory: 6717 grad_norm: 2.7348 loss: 1.7047 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.7047 2023/04/14 02:06:48 - mmengine - INFO - Epoch(train) [30][ 580/1879] lr: 2.0000e-02 eta: 13:39:53 time: 0.3958 data_time: 0.0146 memory: 6717 grad_norm: 2.7035 loss: 1.6248 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6248 2023/04/14 02:06:54 - mmengine - INFO - Epoch(train) [30][ 600/1879] lr: 2.0000e-02 eta: 13:39:42 time: 0.3102 data_time: 0.0132 memory: 6717 grad_norm: 2.8091 loss: 1.5292 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.5292 2023/04/14 02:07:02 - mmengine - INFO - Epoch(train) [30][ 620/1879] lr: 2.0000e-02 eta: 13:39:37 time: 0.4196 data_time: 0.0155 memory: 6717 grad_norm: 2.7719 loss: 1.5703 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5703 2023/04/14 02:07:09 - mmengine - INFO - Epoch(train) [30][ 640/1879] lr: 2.0000e-02 eta: 13:39:28 time: 0.3391 data_time: 0.0127 memory: 6717 grad_norm: 2.7216 loss: 1.8786 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8786 2023/04/14 02:07:17 - mmengine - INFO - Epoch(train) [30][ 660/1879] lr: 2.0000e-02 eta: 13:39:23 time: 0.4066 data_time: 0.0144 memory: 6717 grad_norm: 2.7330 loss: 1.8179 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8179 2023/04/14 02:07:24 - mmengine - INFO - Epoch(train) [30][ 680/1879] lr: 2.0000e-02 eta: 13:39:15 time: 0.3606 data_time: 0.0137 memory: 6717 grad_norm: 2.8637 loss: 1.5987 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5987 2023/04/14 02:07:32 - mmengine - INFO - Epoch(train) [30][ 700/1879] lr: 2.0000e-02 eta: 13:39:07 time: 0.3587 data_time: 0.0143 memory: 6717 grad_norm: 2.7077 loss: 1.8572 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8572 2023/04/14 02:07:39 - mmengine - INFO - Epoch(train) [30][ 720/1879] lr: 2.0000e-02 eta: 13:39:00 time: 0.3878 data_time: 0.0139 memory: 6717 grad_norm: 2.7522 loss: 1.8094 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8094 2023/04/14 02:07:47 - mmengine - INFO - Epoch(train) [30][ 740/1879] lr: 2.0000e-02 eta: 13:38:53 time: 0.3646 data_time: 0.0140 memory: 6717 grad_norm: 2.7388 loss: 1.8283 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8283 2023/04/14 02:07:53 - mmengine - INFO - Epoch(train) [30][ 760/1879] lr: 2.0000e-02 eta: 13:38:44 time: 0.3383 data_time: 0.0139 memory: 6717 grad_norm: 2.6835 loss: 1.5647 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5647 2023/04/14 02:08:02 - mmengine - INFO - Epoch(train) [30][ 780/1879] lr: 2.0000e-02 eta: 13:38:40 time: 0.4459 data_time: 0.0134 memory: 6717 grad_norm: 2.6803 loss: 1.5280 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5280 2023/04/14 02:08:08 - mmengine - INFO - Epoch(train) [30][ 800/1879] lr: 2.0000e-02 eta: 13:38:28 time: 0.2852 data_time: 0.0153 memory: 6717 grad_norm: 2.8006 loss: 1.9284 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.9284 2023/04/14 02:08:16 - mmengine - INFO - Epoch(train) [30][ 820/1879] lr: 2.0000e-02 eta: 13:38:23 time: 0.4148 data_time: 0.0298 memory: 6717 grad_norm: 2.6529 loss: 1.6616 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6616 2023/04/14 02:08:23 - mmengine - INFO - Epoch(train) [30][ 840/1879] lr: 2.0000e-02 eta: 13:38:14 time: 0.3229 data_time: 0.0148 memory: 6717 grad_norm: 2.8646 loss: 1.7946 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7946 2023/04/14 02:08:31 - mmengine - INFO - Epoch(train) [30][ 860/1879] lr: 2.0000e-02 eta: 13:38:07 time: 0.3992 data_time: 0.0267 memory: 6717 grad_norm: 2.7332 loss: 1.8950 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8950 2023/04/14 02:08:38 - mmengine - INFO - Epoch(train) [30][ 880/1879] lr: 2.0000e-02 eta: 13:37:59 time: 0.3548 data_time: 0.0141 memory: 6717 grad_norm: 2.7469 loss: 1.6781 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6781 2023/04/14 02:08:46 - mmengine - INFO - Epoch(train) [30][ 900/1879] lr: 2.0000e-02 eta: 13:37:53 time: 0.4000 data_time: 0.0466 memory: 6717 grad_norm: 2.7719 loss: 1.7584 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7584 2023/04/14 02:08:52 - mmengine - INFO - Epoch(train) [30][ 920/1879] lr: 2.0000e-02 eta: 13:37:44 time: 0.3216 data_time: 0.0473 memory: 6717 grad_norm: 2.7805 loss: 1.8807 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8807 2023/04/14 02:09:00 - mmengine - INFO - Epoch(train) [30][ 940/1879] lr: 2.0000e-02 eta: 13:37:37 time: 0.3921 data_time: 0.0442 memory: 6717 grad_norm: 2.7224 loss: 1.5279 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5279 2023/04/14 02:09:06 - mmengine - INFO - Epoch(train) [30][ 960/1879] lr: 2.0000e-02 eta: 13:37:27 time: 0.3100 data_time: 0.0306 memory: 6717 grad_norm: 2.7325 loss: 1.5567 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5567 2023/04/14 02:09:15 - mmengine - INFO - Epoch(train) [30][ 980/1879] lr: 2.0000e-02 eta: 13:37:21 time: 0.4058 data_time: 0.0600 memory: 6717 grad_norm: 2.7424 loss: 1.8267 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.8267 2023/04/14 02:09:21 - mmengine - INFO - Epoch(train) [30][1000/1879] lr: 2.0000e-02 eta: 13:37:12 time: 0.3215 data_time: 0.0501 memory: 6717 grad_norm: 2.7282 loss: 1.6596 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6596 2023/04/14 02:09:29 - mmengine - INFO - Epoch(train) [30][1020/1879] lr: 2.0000e-02 eta: 13:37:05 time: 0.3817 data_time: 0.1299 memory: 6717 grad_norm: 2.7144 loss: 1.5995 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5995 2023/04/14 02:09:36 - mmengine - INFO - Epoch(train) [30][1040/1879] lr: 2.0000e-02 eta: 13:36:56 time: 0.3494 data_time: 0.2001 memory: 6717 grad_norm: 2.7279 loss: 1.6268 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6268 2023/04/14 02:09:44 - mmengine - INFO - Epoch(train) [30][1060/1879] lr: 2.0000e-02 eta: 13:36:51 time: 0.4254 data_time: 0.2871 memory: 6717 grad_norm: 2.7466 loss: 1.6400 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6400 2023/04/14 02:09:50 - mmengine - INFO - Epoch(train) [30][1080/1879] lr: 2.0000e-02 eta: 13:36:41 time: 0.3059 data_time: 0.1699 memory: 6717 grad_norm: 2.7093 loss: 1.7762 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.7762 2023/04/14 02:09:58 - mmengine - INFO - Epoch(train) [30][1100/1879] lr: 2.0000e-02 eta: 13:36:35 time: 0.3961 data_time: 0.2503 memory: 6717 grad_norm: 2.6388 loss: 1.5513 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5513 2023/04/14 02:10:05 - mmengine - INFO - Epoch(train) [30][1120/1879] lr: 2.0000e-02 eta: 13:36:27 time: 0.3543 data_time: 0.2175 memory: 6717 grad_norm: 2.7414 loss: 1.6132 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6132 2023/04/14 02:10:13 - mmengine - INFO - Epoch(train) [30][1140/1879] lr: 2.0000e-02 eta: 13:36:20 time: 0.3909 data_time: 0.2550 memory: 6717 grad_norm: 2.7083 loss: 1.8617 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.8617 2023/04/14 02:10:20 - mmengine - INFO - Epoch(train) [30][1160/1879] lr: 2.0000e-02 eta: 13:36:12 time: 0.3495 data_time: 0.1979 memory: 6717 grad_norm: 2.8271 loss: 1.7475 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7475 2023/04/14 02:10:28 - mmengine - INFO - Epoch(train) [30][1180/1879] lr: 2.0000e-02 eta: 13:36:05 time: 0.3779 data_time: 0.1458 memory: 6717 grad_norm: 2.7416 loss: 1.6222 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6222 2023/04/14 02:10:35 - mmengine - INFO - Epoch(train) [30][1200/1879] lr: 2.0000e-02 eta: 13:35:57 time: 0.3550 data_time: 0.0438 memory: 6717 grad_norm: 2.7319 loss: 1.7787 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.7787 2023/04/14 02:10:45 - mmengine - INFO - Epoch(train) [30][1220/1879] lr: 2.0000e-02 eta: 13:35:57 time: 0.5342 data_time: 0.0393 memory: 6717 grad_norm: 2.8128 loss: 1.7031 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7031 2023/04/14 02:10:53 - mmengine - INFO - Epoch(train) [30][1240/1879] lr: 2.0000e-02 eta: 13:35:51 time: 0.3998 data_time: 0.0159 memory: 6717 grad_norm: 2.6673 loss: 1.6703 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.6703 2023/04/14 02:11:00 - mmengine - INFO - Epoch(train) [30][1260/1879] lr: 2.0000e-02 eta: 13:35:41 time: 0.3173 data_time: 0.0137 memory: 6717 grad_norm: 2.8512 loss: 1.5809 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5809 2023/04/14 02:11:08 - mmengine - INFO - Epoch(train) [30][1280/1879] lr: 2.0000e-02 eta: 13:35:35 time: 0.3961 data_time: 0.0136 memory: 6717 grad_norm: 2.6867 loss: 1.5900 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.5900 2023/04/14 02:11:15 - mmengine - INFO - Epoch(train) [30][1300/1879] lr: 2.0000e-02 eta: 13:35:28 time: 0.3838 data_time: 0.0136 memory: 6717 grad_norm: 2.7677 loss: 1.8266 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8266 2023/04/14 02:11:23 - mmengine - INFO - Epoch(train) [30][1320/1879] lr: 2.0000e-02 eta: 13:35:22 time: 0.3905 data_time: 0.0138 memory: 6717 grad_norm: 2.7371 loss: 1.6239 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6239 2023/04/14 02:11:30 - mmengine - INFO - Epoch(train) [30][1340/1879] lr: 2.0000e-02 eta: 13:35:14 time: 0.3623 data_time: 0.0157 memory: 6717 grad_norm: 2.7231 loss: 1.6662 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.6662 2023/04/14 02:11:38 - mmengine - INFO - Epoch(train) [30][1360/1879] lr: 2.0000e-02 eta: 13:35:08 time: 0.3964 data_time: 0.0149 memory: 6717 grad_norm: 2.6676 loss: 1.6648 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6648 2023/04/14 02:11:45 - mmengine - INFO - Epoch(train) [30][1380/1879] lr: 2.0000e-02 eta: 13:34:58 time: 0.3119 data_time: 0.0139 memory: 6717 grad_norm: 2.6726 loss: 1.7559 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7559 2023/04/14 02:11:53 - mmengine - INFO - Epoch(train) [30][1400/1879] lr: 2.0000e-02 eta: 13:34:53 time: 0.4298 data_time: 0.0142 memory: 6717 grad_norm: 2.6603 loss: 1.9499 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9499 2023/04/14 02:11:59 - mmengine - INFO - Epoch(train) [30][1420/1879] lr: 2.0000e-02 eta: 13:34:42 time: 0.3043 data_time: 0.0159 memory: 6717 grad_norm: 2.7041 loss: 1.6125 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6125 2023/04/14 02:12:08 - mmengine - INFO - Epoch(train) [30][1440/1879] lr: 2.0000e-02 eta: 13:34:37 time: 0.4198 data_time: 0.0144 memory: 6717 grad_norm: 2.7529 loss: 1.7688 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7688 2023/04/14 02:12:14 - mmengine - INFO - Epoch(train) [30][1460/1879] lr: 2.0000e-02 eta: 13:34:27 time: 0.3172 data_time: 0.0171 memory: 6717 grad_norm: 2.7705 loss: 1.6144 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6144 2023/04/14 02:12:23 - mmengine - INFO - Epoch(train) [30][1480/1879] lr: 2.0000e-02 eta: 13:34:23 time: 0.4307 data_time: 0.0137 memory: 6717 grad_norm: 2.7151 loss: 1.6417 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6417 2023/04/14 02:12:30 - mmengine - INFO - Epoch(train) [30][1500/1879] lr: 2.0000e-02 eta: 13:34:15 time: 0.3645 data_time: 0.0133 memory: 6717 grad_norm: 2.7427 loss: 1.4686 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.4686 2023/04/14 02:12:33 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 02:12:38 - mmengine - INFO - Epoch(train) [30][1520/1879] lr: 2.0000e-02 eta: 13:34:10 time: 0.4154 data_time: 0.0135 memory: 6717 grad_norm: 2.7403 loss: 1.5889 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.5889 2023/04/14 02:12:45 - mmengine - INFO - Epoch(train) [30][1540/1879] lr: 2.0000e-02 eta: 13:34:00 time: 0.3280 data_time: 0.0136 memory: 6717 grad_norm: 2.7643 loss: 1.7592 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.7592 2023/04/14 02:12:53 - mmengine - INFO - Epoch(train) [30][1560/1879] lr: 2.0000e-02 eta: 13:33:55 time: 0.4076 data_time: 0.0146 memory: 6717 grad_norm: 2.7484 loss: 1.8784 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8784 2023/04/14 02:13:00 - mmengine - INFO - Epoch(train) [30][1580/1879] lr: 2.0000e-02 eta: 13:33:46 time: 0.3426 data_time: 0.0153 memory: 6717 grad_norm: 2.7080 loss: 1.5611 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5611 2023/04/14 02:13:08 - mmengine - INFO - Epoch(train) [30][1600/1879] lr: 2.0000e-02 eta: 13:33:40 time: 0.3983 data_time: 0.0130 memory: 6717 grad_norm: 2.7864 loss: 1.8324 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8324 2023/04/14 02:13:14 - mmengine - INFO - Epoch(train) [30][1620/1879] lr: 2.0000e-02 eta: 13:33:30 time: 0.3234 data_time: 0.0141 memory: 6717 grad_norm: 2.6420 loss: 1.7994 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7994 2023/04/14 02:13:23 - mmengine - INFO - Epoch(train) [30][1640/1879] lr: 2.0000e-02 eta: 13:33:26 time: 0.4314 data_time: 0.0159 memory: 6717 grad_norm: 2.7271 loss: 1.7916 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7916 2023/04/14 02:13:29 - mmengine - INFO - Epoch(train) [30][1660/1879] lr: 2.0000e-02 eta: 13:33:16 time: 0.3088 data_time: 0.0124 memory: 6717 grad_norm: 2.7520 loss: 1.5703 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.5703 2023/04/14 02:13:37 - mmengine - INFO - Epoch(train) [30][1680/1879] lr: 2.0000e-02 eta: 13:33:10 time: 0.4168 data_time: 0.0142 memory: 6717 grad_norm: 2.7130 loss: 1.5808 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5808 2023/04/14 02:13:44 - mmengine - INFO - Epoch(train) [30][1700/1879] lr: 2.0000e-02 eta: 13:33:01 time: 0.3275 data_time: 0.0151 memory: 6717 grad_norm: 2.7517 loss: 1.5926 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5926 2023/04/14 02:13:52 - mmengine - INFO - Epoch(train) [30][1720/1879] lr: 2.0000e-02 eta: 13:32:56 time: 0.4138 data_time: 0.0125 memory: 6717 grad_norm: 2.7392 loss: 1.7647 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7647 2023/04/14 02:13:59 - mmengine - INFO - Epoch(train) [30][1740/1879] lr: 2.0000e-02 eta: 13:32:46 time: 0.3203 data_time: 0.0149 memory: 6717 grad_norm: 2.7975 loss: 1.8746 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8746 2023/04/14 02:14:07 - mmengine - INFO - Epoch(train) [30][1760/1879] lr: 2.0000e-02 eta: 13:32:40 time: 0.4130 data_time: 0.0131 memory: 6717 grad_norm: 2.7602 loss: 1.8450 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8450 2023/04/14 02:14:13 - mmengine - INFO - Epoch(train) [30][1780/1879] lr: 2.0000e-02 eta: 13:32:31 time: 0.3205 data_time: 0.0155 memory: 6717 grad_norm: 2.7081 loss: 1.7782 top1_acc: 0.3750 top5_acc: 0.4375 loss_cls: 1.7782 2023/04/14 02:14:21 - mmengine - INFO - Epoch(train) [30][1800/1879] lr: 2.0000e-02 eta: 13:32:25 time: 0.4022 data_time: 0.0127 memory: 6717 grad_norm: 2.7707 loss: 1.7483 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7483 2023/04/14 02:14:28 - mmengine - INFO - Epoch(train) [30][1820/1879] lr: 2.0000e-02 eta: 13:32:15 time: 0.3197 data_time: 0.0147 memory: 6717 grad_norm: 2.7049 loss: 1.6120 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6120 2023/04/14 02:14:37 - mmengine - INFO - Epoch(train) [30][1840/1879] lr: 2.0000e-02 eta: 13:32:11 time: 0.4466 data_time: 0.0123 memory: 6717 grad_norm: 2.6883 loss: 1.8512 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.8512 2023/04/14 02:14:43 - mmengine - INFO - Epoch(train) [30][1860/1879] lr: 2.0000e-02 eta: 13:32:00 time: 0.2960 data_time: 0.0174 memory: 6717 grad_norm: 2.7275 loss: 1.6652 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6652 2023/04/14 02:14:48 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 02:14:48 - mmengine - INFO - Epoch(train) [30][1879/1879] lr: 2.0000e-02 eta: 13:31:50 time: 0.2970 data_time: 0.0122 memory: 6717 grad_norm: 2.8195 loss: 1.7788 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.7788 2023/04/14 02:14:48 - mmengine - INFO - Saving checkpoint at 30 epochs 2023/04/14 02:14:58 - mmengine - INFO - Epoch(val) [30][ 20/155] eta: 0:01:00 time: 0.4472 data_time: 0.4139 memory: 1391 2023/04/14 02:15:04 - mmengine - INFO - Epoch(val) [30][ 40/155] eta: 0:00:44 time: 0.3228 data_time: 0.2895 memory: 1391 2023/04/14 02:15:13 - mmengine - INFO - Epoch(val) [30][ 60/155] eta: 0:00:38 time: 0.4399 data_time: 0.4066 memory: 1391 2023/04/14 02:15:19 - mmengine - INFO - Epoch(val) [30][ 80/155] eta: 0:00:28 time: 0.3178 data_time: 0.2843 memory: 1391 2023/04/14 02:15:28 - mmengine - INFO - Epoch(val) [30][100/155] eta: 0:00:21 time: 0.4542 data_time: 0.4210 memory: 1391 2023/04/14 02:15:34 - mmengine - INFO - Epoch(val) [30][120/155] eta: 0:00:13 time: 0.2993 data_time: 0.2657 memory: 1391 2023/04/14 02:15:44 - mmengine - INFO - Epoch(val) [30][140/155] eta: 0:00:05 time: 0.4771 data_time: 0.4442 memory: 1391 2023/04/14 02:15:51 - mmengine - INFO - Epoch(val) [30][155/155] acc/top1: 0.6032 acc/top5: 0.8346 acc/mean1: 0.6032 data_time: 0.4154 time: 0.4476 2023/04/14 02:15:51 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_28.pth is removed 2023/04/14 02:15:52 - mmengine - INFO - The best checkpoint with 0.6032 acc/top1 at 30 epoch is saved to best_acc_top1_epoch_30.pth. 2023/04/14 02:16:01 - mmengine - INFO - Epoch(train) [31][ 20/1879] lr: 2.0000e-02 eta: 13:31:47 time: 0.4666 data_time: 0.3284 memory: 6717 grad_norm: 2.8055 loss: 1.5692 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5692 2023/04/14 02:16:08 - mmengine - INFO - Epoch(train) [31][ 40/1879] lr: 2.0000e-02 eta: 13:31:38 time: 0.3336 data_time: 0.1330 memory: 6717 grad_norm: 2.7514 loss: 1.5790 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5790 2023/04/14 02:16:16 - mmengine - INFO - Epoch(train) [31][ 60/1879] lr: 2.0000e-02 eta: 13:31:33 time: 0.4141 data_time: 0.1112 memory: 6717 grad_norm: 2.7144 loss: 1.6259 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6259 2023/04/14 02:16:23 - mmengine - INFO - Epoch(train) [31][ 80/1879] lr: 2.0000e-02 eta: 13:31:24 time: 0.3312 data_time: 0.0535 memory: 6717 grad_norm: 2.7696 loss: 1.6085 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6085 2023/04/14 02:16:31 - mmengine - INFO - Epoch(train) [31][ 100/1879] lr: 2.0000e-02 eta: 13:31:18 time: 0.4184 data_time: 0.0177 memory: 6717 grad_norm: 2.7410 loss: 1.7288 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7288 2023/04/14 02:16:37 - mmengine - INFO - Epoch(train) [31][ 120/1879] lr: 2.0000e-02 eta: 13:31:08 time: 0.3106 data_time: 0.0136 memory: 6717 grad_norm: 2.7821 loss: 1.6675 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6675 2023/04/14 02:16:46 - mmengine - INFO - Epoch(train) [31][ 140/1879] lr: 2.0000e-02 eta: 13:31:04 time: 0.4274 data_time: 0.0141 memory: 6717 grad_norm: 2.6528 loss: 1.6546 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6546 2023/04/14 02:16:52 - mmengine - INFO - Epoch(train) [31][ 160/1879] lr: 2.0000e-02 eta: 13:30:54 time: 0.3299 data_time: 0.0136 memory: 6717 grad_norm: 2.6915 loss: 1.4558 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4558 2023/04/14 02:17:01 - mmengine - INFO - Epoch(train) [31][ 180/1879] lr: 2.0000e-02 eta: 13:30:49 time: 0.4239 data_time: 0.0169 memory: 6717 grad_norm: 2.8118 loss: 1.6642 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6642 2023/04/14 02:17:07 - mmengine - INFO - Epoch(train) [31][ 200/1879] lr: 2.0000e-02 eta: 13:30:39 time: 0.3177 data_time: 0.0136 memory: 6717 grad_norm: 2.7389 loss: 1.5827 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5827 2023/04/14 02:17:16 - mmengine - INFO - Epoch(train) [31][ 220/1879] lr: 2.0000e-02 eta: 13:30:35 time: 0.4311 data_time: 0.0150 memory: 6717 grad_norm: 2.6936 loss: 1.5058 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.5058 2023/04/14 02:17:22 - mmengine - INFO - Epoch(train) [31][ 240/1879] lr: 2.0000e-02 eta: 13:30:25 time: 0.3183 data_time: 0.0137 memory: 6717 grad_norm: 2.7789 loss: 1.6374 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6374 2023/04/14 02:17:31 - mmengine - INFO - Epoch(train) [31][ 260/1879] lr: 2.0000e-02 eta: 13:30:20 time: 0.4313 data_time: 0.0149 memory: 6717 grad_norm: 2.6671 loss: 1.7771 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7771 2023/04/14 02:17:37 - mmengine - INFO - Epoch(train) [31][ 280/1879] lr: 2.0000e-02 eta: 13:30:10 time: 0.3045 data_time: 0.0141 memory: 6717 grad_norm: 2.7334 loss: 1.9115 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9115 2023/04/14 02:17:45 - mmengine - INFO - Epoch(train) [31][ 300/1879] lr: 2.0000e-02 eta: 13:30:04 time: 0.3946 data_time: 0.0148 memory: 6717 grad_norm: 2.7605 loss: 1.8526 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8526 2023/04/14 02:17:51 - mmengine - INFO - Epoch(train) [31][ 320/1879] lr: 2.0000e-02 eta: 13:29:54 time: 0.3193 data_time: 0.0147 memory: 6717 grad_norm: 2.7081 loss: 1.6126 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.6126 2023/04/14 02:18:00 - mmengine - INFO - Epoch(train) [31][ 340/1879] lr: 2.0000e-02 eta: 13:29:49 time: 0.4331 data_time: 0.0147 memory: 6717 grad_norm: 2.6435 loss: 1.6823 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.6823 2023/04/14 02:18:06 - mmengine - INFO - Epoch(train) [31][ 360/1879] lr: 2.0000e-02 eta: 13:29:40 time: 0.3204 data_time: 0.0128 memory: 6717 grad_norm: 2.7701 loss: 1.8265 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8265 2023/04/14 02:18:15 - mmengine - INFO - Epoch(train) [31][ 380/1879] lr: 2.0000e-02 eta: 13:29:34 time: 0.4140 data_time: 0.0149 memory: 6717 grad_norm: 2.7549 loss: 1.9143 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9143 2023/04/14 02:18:21 - mmengine - INFO - Epoch(train) [31][ 400/1879] lr: 2.0000e-02 eta: 13:29:25 time: 0.3294 data_time: 0.0149 memory: 6717 grad_norm: 2.6842 loss: 1.7215 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.7215 2023/04/14 02:18:29 - mmengine - INFO - Epoch(train) [31][ 420/1879] lr: 2.0000e-02 eta: 13:29:18 time: 0.3755 data_time: 0.0207 memory: 6717 grad_norm: 2.7074 loss: 1.5143 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.5143 2023/04/14 02:18:36 - mmengine - INFO - Epoch(train) [31][ 440/1879] lr: 2.0000e-02 eta: 13:29:10 time: 0.3651 data_time: 0.0136 memory: 6717 grad_norm: 2.7034 loss: 1.4747 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4747 2023/04/14 02:18:43 - mmengine - INFO - Epoch(train) [31][ 460/1879] lr: 2.0000e-02 eta: 13:29:02 time: 0.3525 data_time: 0.0233 memory: 6717 grad_norm: 2.7648 loss: 1.5285 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5285 2023/04/14 02:18:51 - mmengine - INFO - Epoch(train) [31][ 480/1879] lr: 2.0000e-02 eta: 13:28:55 time: 0.3808 data_time: 0.0409 memory: 6717 grad_norm: 2.7703 loss: 1.5594 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5594 2023/04/14 02:18:58 - mmengine - INFO - Epoch(train) [31][ 500/1879] lr: 2.0000e-02 eta: 13:28:48 time: 0.3741 data_time: 0.0398 memory: 6717 grad_norm: 2.7268 loss: 1.5474 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5474 2023/04/14 02:19:06 - mmengine - INFO - Epoch(train) [31][ 520/1879] lr: 2.0000e-02 eta: 13:28:41 time: 0.3789 data_time: 0.0191 memory: 6717 grad_norm: 2.7501 loss: 1.4926 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.4926 2023/04/14 02:19:13 - mmengine - INFO - Epoch(train) [31][ 540/1879] lr: 2.0000e-02 eta: 13:28:32 time: 0.3504 data_time: 0.0153 memory: 6717 grad_norm: 2.6627 loss: 1.7756 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.7756 2023/04/14 02:19:21 - mmengine - INFO - Epoch(train) [31][ 560/1879] lr: 2.0000e-02 eta: 13:28:26 time: 0.3930 data_time: 0.0145 memory: 6717 grad_norm: 2.7240 loss: 1.7941 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7941 2023/04/14 02:19:27 - mmengine - INFO - Epoch(train) [31][ 580/1879] lr: 2.0000e-02 eta: 13:28:17 time: 0.3316 data_time: 0.0157 memory: 6717 grad_norm: 2.8376 loss: 1.7645 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7645 2023/04/14 02:19:36 - mmengine - INFO - Epoch(train) [31][ 600/1879] lr: 2.0000e-02 eta: 13:28:12 time: 0.4241 data_time: 0.0140 memory: 6717 grad_norm: 2.7278 loss: 1.4651 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.4651 2023/04/14 02:19:42 - mmengine - INFO - Epoch(train) [31][ 620/1879] lr: 2.0000e-02 eta: 13:28:03 time: 0.3278 data_time: 0.0155 memory: 6717 grad_norm: 2.6200 loss: 1.6546 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6546 2023/04/14 02:19:47 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 02:19:51 - mmengine - INFO - Epoch(train) [31][ 640/1879] lr: 2.0000e-02 eta: 13:27:58 time: 0.4352 data_time: 0.0157 memory: 6717 grad_norm: 2.7264 loss: 1.8363 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8363 2023/04/14 02:19:57 - mmengine - INFO - Epoch(train) [31][ 660/1879] lr: 2.0000e-02 eta: 13:27:47 time: 0.2881 data_time: 0.0131 memory: 6717 grad_norm: 2.6216 loss: 1.5049 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5049 2023/04/14 02:20:05 - mmengine - INFO - Epoch(train) [31][ 680/1879] lr: 2.0000e-02 eta: 13:27:42 time: 0.4229 data_time: 0.0152 memory: 6717 grad_norm: 2.7528 loss: 1.8533 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.8533 2023/04/14 02:20:12 - mmengine - INFO - Epoch(train) [31][ 700/1879] lr: 2.0000e-02 eta: 13:27:33 time: 0.3420 data_time: 0.0167 memory: 6717 grad_norm: 2.7565 loss: 1.7461 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.7461 2023/04/14 02:20:20 - mmengine - INFO - Epoch(train) [31][ 720/1879] lr: 2.0000e-02 eta: 13:27:28 time: 0.4079 data_time: 0.0169 memory: 6717 grad_norm: 2.7548 loss: 1.8564 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.8564 2023/04/14 02:20:26 - mmengine - INFO - Epoch(train) [31][ 740/1879] lr: 2.0000e-02 eta: 13:27:17 time: 0.3098 data_time: 0.0506 memory: 6717 grad_norm: 2.7357 loss: 1.7757 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.7757 2023/04/14 02:20:35 - mmengine - INFO - Epoch(train) [31][ 760/1879] lr: 2.0000e-02 eta: 13:27:13 time: 0.4397 data_time: 0.1037 memory: 6717 grad_norm: 2.7528 loss: 1.5836 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.5836 2023/04/14 02:20:41 - mmengine - INFO - Epoch(train) [31][ 780/1879] lr: 2.0000e-02 eta: 13:27:02 time: 0.2962 data_time: 0.0441 memory: 6717 grad_norm: 2.7309 loss: 1.6233 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.6233 2023/04/14 02:20:48 - mmengine - INFO - Epoch(train) [31][ 800/1879] lr: 2.0000e-02 eta: 13:26:55 time: 0.3677 data_time: 0.0175 memory: 6717 grad_norm: 2.7805 loss: 1.3759 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3759 2023/04/14 02:20:55 - mmengine - INFO - Epoch(train) [31][ 820/1879] lr: 2.0000e-02 eta: 13:26:46 time: 0.3281 data_time: 0.0265 memory: 6717 grad_norm: 2.7363 loss: 1.7127 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7127 2023/04/14 02:21:04 - mmengine - INFO - Epoch(train) [31][ 840/1879] lr: 2.0000e-02 eta: 13:26:41 time: 0.4387 data_time: 0.0158 memory: 6717 grad_norm: 2.6706 loss: 1.7528 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.7528 2023/04/14 02:21:11 - mmengine - INFO - Epoch(train) [31][ 860/1879] lr: 2.0000e-02 eta: 13:26:32 time: 0.3388 data_time: 0.0129 memory: 6717 grad_norm: 2.7341 loss: 1.6517 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6517 2023/04/14 02:21:18 - mmengine - INFO - Epoch(train) [31][ 880/1879] lr: 2.0000e-02 eta: 13:26:26 time: 0.3879 data_time: 0.0141 memory: 6717 grad_norm: 2.7500 loss: 1.5477 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5477 2023/04/14 02:21:24 - mmengine - INFO - Epoch(train) [31][ 900/1879] lr: 2.0000e-02 eta: 13:26:15 time: 0.3056 data_time: 0.0161 memory: 6717 grad_norm: 2.6780 loss: 1.7544 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.7544 2023/04/14 02:21:32 - mmengine - INFO - Epoch(train) [31][ 920/1879] lr: 2.0000e-02 eta: 13:26:09 time: 0.3991 data_time: 0.0380 memory: 6717 grad_norm: 2.8307 loss: 1.6378 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.6378 2023/04/14 02:21:39 - mmengine - INFO - Epoch(train) [31][ 940/1879] lr: 2.0000e-02 eta: 13:26:01 time: 0.3423 data_time: 0.0856 memory: 6717 grad_norm: 2.7569 loss: 1.5570 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5570 2023/04/14 02:21:47 - mmengine - INFO - Epoch(train) [31][ 960/1879] lr: 2.0000e-02 eta: 13:25:55 time: 0.4024 data_time: 0.0486 memory: 6717 grad_norm: 2.7018 loss: 1.7417 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7417 2023/04/14 02:21:54 - mmengine - INFO - Epoch(train) [31][ 980/1879] lr: 2.0000e-02 eta: 13:25:45 time: 0.3242 data_time: 0.0142 memory: 6717 grad_norm: 2.7547 loss: 1.5503 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5503 2023/04/14 02:22:02 - mmengine - INFO - Epoch(train) [31][1000/1879] lr: 2.0000e-02 eta: 13:25:40 time: 0.4105 data_time: 0.0137 memory: 6717 grad_norm: 2.7575 loss: 1.7341 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7341 2023/04/14 02:22:09 - mmengine - INFO - Epoch(train) [31][1020/1879] lr: 2.0000e-02 eta: 13:25:31 time: 0.3524 data_time: 0.0235 memory: 6717 grad_norm: 2.9514 loss: 1.7012 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.7012 2023/04/14 02:22:17 - mmengine - INFO - Epoch(train) [31][1040/1879] lr: 2.0000e-02 eta: 13:25:24 time: 0.3810 data_time: 0.0399 memory: 6717 grad_norm: 2.7625 loss: 1.6453 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6453 2023/04/14 02:22:24 - mmengine - INFO - Epoch(train) [31][1060/1879] lr: 2.0000e-02 eta: 13:25:18 time: 0.3830 data_time: 0.1910 memory: 6717 grad_norm: 2.7108 loss: 1.5886 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.5886 2023/04/14 02:22:31 - mmengine - INFO - Epoch(train) [31][1080/1879] lr: 2.0000e-02 eta: 13:25:09 time: 0.3521 data_time: 0.1925 memory: 6717 grad_norm: 2.7845 loss: 1.7893 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.7893 2023/04/14 02:22:39 - mmengine - INFO - Epoch(train) [31][1100/1879] lr: 2.0000e-02 eta: 13:25:03 time: 0.3900 data_time: 0.2456 memory: 6717 grad_norm: 2.7019 loss: 1.4416 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.4416 2023/04/14 02:22:46 - mmengine - INFO - Epoch(train) [31][1120/1879] lr: 2.0000e-02 eta: 13:24:54 time: 0.3381 data_time: 0.1123 memory: 6717 grad_norm: 2.7737 loss: 1.5774 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5774 2023/04/14 02:22:53 - mmengine - INFO - Epoch(train) [31][1140/1879] lr: 2.0000e-02 eta: 13:24:47 time: 0.3721 data_time: 0.1857 memory: 6717 grad_norm: 2.8079 loss: 1.6877 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6877 2023/04/14 02:23:00 - mmengine - INFO - Epoch(train) [31][1160/1879] lr: 2.0000e-02 eta: 13:24:38 time: 0.3497 data_time: 0.1091 memory: 6717 grad_norm: 2.8220 loss: 1.7185 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7185 2023/04/14 02:23:07 - mmengine - INFO - Epoch(train) [31][1180/1879] lr: 2.0000e-02 eta: 13:24:30 time: 0.3432 data_time: 0.1240 memory: 6717 grad_norm: 2.7321 loss: 1.7090 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7090 2023/04/14 02:23:16 - mmengine - INFO - Epoch(train) [31][1200/1879] lr: 2.0000e-02 eta: 13:24:25 time: 0.4337 data_time: 0.0272 memory: 6717 grad_norm: 2.7268 loss: 1.8389 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8389 2023/04/14 02:23:23 - mmengine - INFO - Epoch(train) [31][1220/1879] lr: 2.0000e-02 eta: 13:24:16 time: 0.3363 data_time: 0.0147 memory: 6717 grad_norm: 2.7631 loss: 1.4757 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4757 2023/04/14 02:23:31 - mmengine - INFO - Epoch(train) [31][1240/1879] lr: 2.0000e-02 eta: 13:24:11 time: 0.4169 data_time: 0.0138 memory: 6717 grad_norm: 2.7797 loss: 1.7985 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7985 2023/04/14 02:23:38 - mmengine - INFO - Epoch(train) [31][1260/1879] lr: 2.0000e-02 eta: 13:24:02 time: 0.3445 data_time: 0.0148 memory: 6717 grad_norm: 2.6971 loss: 1.6781 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6781 2023/04/14 02:23:46 - mmengine - INFO - Epoch(train) [31][1280/1879] lr: 2.0000e-02 eta: 13:23:56 time: 0.3854 data_time: 0.0146 memory: 6717 grad_norm: 2.7418 loss: 1.7192 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7192 2023/04/14 02:23:52 - mmengine - INFO - Epoch(train) [31][1300/1879] lr: 2.0000e-02 eta: 13:23:46 time: 0.3225 data_time: 0.0148 memory: 6717 grad_norm: 2.6765 loss: 1.7721 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7721 2023/04/14 02:24:00 - mmengine - INFO - Epoch(train) [31][1320/1879] lr: 2.0000e-02 eta: 13:23:40 time: 0.4049 data_time: 0.0139 memory: 6717 grad_norm: 2.6941 loss: 1.9529 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9529 2023/04/14 02:24:06 - mmengine - INFO - Epoch(train) [31][1340/1879] lr: 2.0000e-02 eta: 13:23:30 time: 0.3020 data_time: 0.0145 memory: 6717 grad_norm: 2.6833 loss: 1.7092 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7092 2023/04/14 02:24:14 - mmengine - INFO - Epoch(train) [31][1360/1879] lr: 2.0000e-02 eta: 13:23:24 time: 0.3972 data_time: 0.0880 memory: 6717 grad_norm: 2.7993 loss: 1.3226 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3226 2023/04/14 02:24:20 - mmengine - INFO - Epoch(train) [31][1380/1879] lr: 2.0000e-02 eta: 13:23:14 time: 0.3135 data_time: 0.1387 memory: 6717 grad_norm: 2.7410 loss: 1.7469 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.7469 2023/04/14 02:24:29 - mmengine - INFO - Epoch(train) [31][1400/1879] lr: 2.0000e-02 eta: 13:23:08 time: 0.4213 data_time: 0.2388 memory: 6717 grad_norm: 2.7453 loss: 1.7641 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.7641 2023/04/14 02:24:36 - mmengine - INFO - Epoch(train) [31][1420/1879] lr: 2.0000e-02 eta: 13:23:00 time: 0.3474 data_time: 0.1485 memory: 6717 grad_norm: 2.6973 loss: 1.6209 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6209 2023/04/14 02:24:44 - mmengine - INFO - Epoch(train) [31][1440/1879] lr: 2.0000e-02 eta: 13:22:54 time: 0.3977 data_time: 0.1245 memory: 6717 grad_norm: 2.6829 loss: 1.7141 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7141 2023/04/14 02:24:50 - mmengine - INFO - Epoch(train) [31][1460/1879] lr: 2.0000e-02 eta: 13:22:44 time: 0.3196 data_time: 0.0353 memory: 6717 grad_norm: 2.6951 loss: 1.6547 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6547 2023/04/14 02:24:58 - mmengine - INFO - Epoch(train) [31][1480/1879] lr: 2.0000e-02 eta: 13:22:38 time: 0.3906 data_time: 0.0142 memory: 6717 grad_norm: 2.7329 loss: 1.7184 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7184 2023/04/14 02:25:05 - mmengine - INFO - Epoch(train) [31][1500/1879] lr: 2.0000e-02 eta: 13:22:29 time: 0.3410 data_time: 0.0298 memory: 6717 grad_norm: 2.7475 loss: 1.6253 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6253 2023/04/14 02:25:13 - mmengine - INFO - Epoch(train) [31][1520/1879] lr: 2.0000e-02 eta: 13:22:22 time: 0.3902 data_time: 0.0142 memory: 6717 grad_norm: 2.7251 loss: 1.6452 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.6452 2023/04/14 02:25:19 - mmengine - INFO - Epoch(train) [31][1540/1879] lr: 2.0000e-02 eta: 13:22:14 time: 0.3363 data_time: 0.0659 memory: 6717 grad_norm: 2.6378 loss: 1.6627 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6627 2023/04/14 02:25:28 - mmengine - INFO - Epoch(train) [31][1560/1879] lr: 2.0000e-02 eta: 13:22:09 time: 0.4260 data_time: 0.0690 memory: 6717 grad_norm: 2.7109 loss: 1.6567 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6567 2023/04/14 02:25:34 - mmengine - INFO - Epoch(train) [31][1580/1879] lr: 2.0000e-02 eta: 13:22:00 time: 0.3326 data_time: 0.0128 memory: 6717 grad_norm: 2.6642 loss: 1.6679 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.6679 2023/04/14 02:25:43 - mmengine - INFO - Epoch(train) [31][1600/1879] lr: 2.0000e-02 eta: 13:21:55 time: 0.4383 data_time: 0.0140 memory: 6717 grad_norm: 2.7444 loss: 1.6433 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6433 2023/04/14 02:25:50 - mmengine - INFO - Epoch(train) [31][1620/1879] lr: 2.0000e-02 eta: 13:21:46 time: 0.3278 data_time: 0.0145 memory: 6717 grad_norm: 2.7382 loss: 1.4287 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4287 2023/04/14 02:25:55 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 02:25:58 - mmengine - INFO - Epoch(train) [31][1640/1879] lr: 2.0000e-02 eta: 13:21:41 time: 0.4208 data_time: 0.0140 memory: 6717 grad_norm: 2.6422 loss: 1.5806 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5806 2023/04/14 02:26:05 - mmengine - INFO - Epoch(train) [31][1660/1879] lr: 2.0000e-02 eta: 13:21:32 time: 0.3320 data_time: 0.0144 memory: 6717 grad_norm: 2.6784 loss: 1.5812 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5812 2023/04/14 02:26:13 - mmengine - INFO - Epoch(train) [31][1680/1879] lr: 2.0000e-02 eta: 13:21:26 time: 0.4049 data_time: 0.0143 memory: 6717 grad_norm: 2.7410 loss: 1.7658 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7658 2023/04/14 02:26:20 - mmengine - INFO - Epoch(train) [31][1700/1879] lr: 2.0000e-02 eta: 13:21:17 time: 0.3381 data_time: 0.0164 memory: 6717 grad_norm: 2.6792 loss: 1.9126 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.9126 2023/04/14 02:26:28 - mmengine - INFO - Epoch(train) [31][1720/1879] lr: 2.0000e-02 eta: 13:21:11 time: 0.4144 data_time: 0.0145 memory: 6717 grad_norm: 2.7748 loss: 1.7342 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.7342 2023/04/14 02:26:35 - mmengine - INFO - Epoch(train) [31][1740/1879] lr: 2.0000e-02 eta: 13:21:03 time: 0.3390 data_time: 0.0126 memory: 6717 grad_norm: 2.7551 loss: 1.7334 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.7334 2023/04/14 02:26:44 - mmengine - INFO - Epoch(train) [31][1760/1879] lr: 2.0000e-02 eta: 13:20:59 time: 0.4440 data_time: 0.0138 memory: 6717 grad_norm: 2.7281 loss: 1.7553 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7553 2023/04/14 02:26:50 - mmengine - INFO - Epoch(train) [31][1780/1879] lr: 2.0000e-02 eta: 13:20:49 time: 0.3334 data_time: 0.0141 memory: 6717 grad_norm: 2.6514 loss: 1.6336 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6336 2023/04/14 02:26:58 - mmengine - INFO - Epoch(train) [31][1800/1879] lr: 2.0000e-02 eta: 13:20:43 time: 0.3846 data_time: 0.0144 memory: 6717 grad_norm: 2.7300 loss: 1.8393 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 1.8393 2023/04/14 02:27:04 - mmengine - INFO - Epoch(train) [31][1820/1879] lr: 2.0000e-02 eta: 13:20:33 time: 0.3132 data_time: 0.0143 memory: 6717 grad_norm: 2.7554 loss: 1.7282 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.7282 2023/04/14 02:27:12 - mmengine - INFO - Epoch(train) [31][1840/1879] lr: 2.0000e-02 eta: 13:20:27 time: 0.4056 data_time: 0.0153 memory: 6717 grad_norm: 2.8605 loss: 1.7497 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7497 2023/04/14 02:27:18 - mmengine - INFO - Epoch(train) [31][1860/1879] lr: 2.0000e-02 eta: 13:20:17 time: 0.3037 data_time: 0.0122 memory: 6717 grad_norm: 2.7211 loss: 1.8197 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8197 2023/04/14 02:27:27 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 02:27:27 - mmengine - INFO - Epoch(train) [31][1879/1879] lr: 2.0000e-02 eta: 13:20:12 time: 0.4083 data_time: 0.0123 memory: 6717 grad_norm: 2.7645 loss: 1.6876 top1_acc: 0.5714 top5_acc: 1.0000 loss_cls: 1.6876 2023/04/14 02:27:41 - mmengine - INFO - Epoch(val) [31][ 20/155] eta: 0:01:34 time: 0.6997 data_time: 0.6670 memory: 1391 2023/04/14 02:27:46 - mmengine - INFO - Epoch(val) [31][ 40/155] eta: 0:00:57 time: 0.2975 data_time: 0.2646 memory: 1391 2023/04/14 02:27:55 - mmengine - INFO - Epoch(val) [31][ 60/155] eta: 0:00:45 time: 0.4422 data_time: 0.4093 memory: 1391 2023/04/14 02:28:01 - mmengine - INFO - Epoch(val) [31][ 80/155] eta: 0:00:32 time: 0.3039 data_time: 0.2711 memory: 1391 2023/04/14 02:28:10 - mmengine - INFO - Epoch(val) [31][100/155] eta: 0:00:24 time: 0.4415 data_time: 0.4049 memory: 1391 2023/04/14 02:28:17 - mmengine - INFO - Epoch(val) [31][120/155] eta: 0:00:14 time: 0.3330 data_time: 0.3003 memory: 1391 2023/04/14 02:28:26 - mmengine - INFO - Epoch(val) [31][140/155] eta: 0:00:06 time: 0.4785 data_time: 0.4459 memory: 1391 2023/04/14 02:28:33 - mmengine - INFO - Epoch(val) [31][155/155] acc/top1: 0.5961 acc/top5: 0.8308 acc/mean1: 0.5960 data_time: 0.4080 time: 0.4401 2023/04/14 02:28:43 - mmengine - INFO - Epoch(train) [32][ 20/1879] lr: 2.0000e-02 eta: 13:20:09 time: 0.4729 data_time: 0.2419 memory: 6717 grad_norm: 2.7576 loss: 1.6090 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6090 2023/04/14 02:28:50 - mmengine - INFO - Epoch(train) [32][ 40/1879] lr: 2.0000e-02 eta: 13:20:00 time: 0.3321 data_time: 0.1559 memory: 6717 grad_norm: 2.7029 loss: 1.6516 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6516 2023/04/14 02:28:58 - mmengine - INFO - Epoch(train) [32][ 60/1879] lr: 2.0000e-02 eta: 13:19:54 time: 0.4034 data_time: 0.1377 memory: 6717 grad_norm: 2.7795 loss: 1.6594 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6594 2023/04/14 02:29:05 - mmengine - INFO - Epoch(train) [32][ 80/1879] lr: 2.0000e-02 eta: 13:19:45 time: 0.3447 data_time: 0.1046 memory: 6717 grad_norm: 2.6629 loss: 1.5567 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5567 2023/04/14 02:29:13 - mmengine - INFO - Epoch(train) [32][ 100/1879] lr: 2.0000e-02 eta: 13:19:40 time: 0.4246 data_time: 0.1156 memory: 6717 grad_norm: 2.8058 loss: 1.5199 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5199 2023/04/14 02:29:19 - mmengine - INFO - Epoch(train) [32][ 120/1879] lr: 2.0000e-02 eta: 13:19:30 time: 0.3051 data_time: 0.1299 memory: 6717 grad_norm: 2.6738 loss: 1.7426 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7426 2023/04/14 02:29:28 - mmengine - INFO - Epoch(train) [32][ 140/1879] lr: 2.0000e-02 eta: 13:19:25 time: 0.4288 data_time: 0.2287 memory: 6717 grad_norm: 2.8616 loss: 1.7093 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7093 2023/04/14 02:29:34 - mmengine - INFO - Epoch(train) [32][ 160/1879] lr: 2.0000e-02 eta: 13:19:16 time: 0.3366 data_time: 0.1150 memory: 6717 grad_norm: 2.6414 loss: 1.6538 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6538 2023/04/14 02:29:43 - mmengine - INFO - Epoch(train) [32][ 180/1879] lr: 2.0000e-02 eta: 13:19:11 time: 0.4239 data_time: 0.0614 memory: 6717 grad_norm: 2.8013 loss: 1.7428 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7428 2023/04/14 02:29:49 - mmengine - INFO - Epoch(train) [32][ 200/1879] lr: 2.0000e-02 eta: 13:19:01 time: 0.3114 data_time: 0.0289 memory: 6717 grad_norm: 2.7253 loss: 1.6434 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6434 2023/04/14 02:29:58 - mmengine - INFO - Epoch(train) [32][ 220/1879] lr: 2.0000e-02 eta: 13:18:56 time: 0.4166 data_time: 0.0251 memory: 6717 grad_norm: 2.6534 loss: 1.4322 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4322 2023/04/14 02:30:04 - mmengine - INFO - Epoch(train) [32][ 240/1879] lr: 2.0000e-02 eta: 13:18:46 time: 0.3234 data_time: 0.0138 memory: 6717 grad_norm: 2.7336 loss: 1.6889 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6889 2023/04/14 02:30:13 - mmengine - INFO - Epoch(train) [32][ 260/1879] lr: 2.0000e-02 eta: 13:18:42 time: 0.4298 data_time: 0.0148 memory: 6717 grad_norm: 2.7411 loss: 1.6717 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6717 2023/04/14 02:30:19 - mmengine - INFO - Epoch(train) [32][ 280/1879] lr: 2.0000e-02 eta: 13:18:32 time: 0.3206 data_time: 0.0148 memory: 6717 grad_norm: 2.6975 loss: 1.4331 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4331 2023/04/14 02:30:27 - mmengine - INFO - Epoch(train) [32][ 300/1879] lr: 2.0000e-02 eta: 13:18:26 time: 0.4110 data_time: 0.0159 memory: 6717 grad_norm: 2.6961 loss: 1.8492 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8492 2023/04/14 02:30:34 - mmengine - INFO - Epoch(train) [32][ 320/1879] lr: 2.0000e-02 eta: 13:18:17 time: 0.3324 data_time: 0.0138 memory: 6717 grad_norm: 2.6857 loss: 1.7836 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7836 2023/04/14 02:30:42 - mmengine - INFO - Epoch(train) [32][ 340/1879] lr: 2.0000e-02 eta: 13:18:11 time: 0.3888 data_time: 0.0144 memory: 6717 grad_norm: 2.7100 loss: 1.5129 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5129 2023/04/14 02:30:52 - mmengine - INFO - Epoch(train) [32][ 360/1879] lr: 2.0000e-02 eta: 13:18:11 time: 0.5344 data_time: 0.0132 memory: 6717 grad_norm: 2.7462 loss: 1.6798 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6798 2023/04/14 02:30:59 - mmengine - INFO - Epoch(train) [32][ 380/1879] lr: 2.0000e-02 eta: 13:18:02 time: 0.3529 data_time: 0.0149 memory: 6717 grad_norm: 2.6970 loss: 1.6005 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6005 2023/04/14 02:31:07 - mmengine - INFO - Epoch(train) [32][ 400/1879] lr: 2.0000e-02 eta: 13:17:56 time: 0.3872 data_time: 0.0137 memory: 6717 grad_norm: 2.7063 loss: 1.5993 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5993 2023/04/14 02:31:13 - mmengine - INFO - Epoch(train) [32][ 420/1879] lr: 2.0000e-02 eta: 13:17:45 time: 0.3025 data_time: 0.0165 memory: 6717 grad_norm: 2.6986 loss: 1.5337 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.5337 2023/04/14 02:31:22 - mmengine - INFO - Epoch(train) [32][ 440/1879] lr: 2.0000e-02 eta: 13:17:40 time: 0.4181 data_time: 0.0142 memory: 6717 grad_norm: 2.8455 loss: 1.7967 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.7967 2023/04/14 02:31:27 - mmengine - INFO - Epoch(train) [32][ 460/1879] lr: 2.0000e-02 eta: 13:17:29 time: 0.2892 data_time: 0.0199 memory: 6717 grad_norm: 2.6530 loss: 1.5589 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5589 2023/04/14 02:31:35 - mmengine - INFO - Epoch(train) [32][ 480/1879] lr: 2.0000e-02 eta: 13:17:23 time: 0.4010 data_time: 0.0146 memory: 6717 grad_norm: 2.7715 loss: 1.5515 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5515 2023/04/14 02:31:42 - mmengine - INFO - Epoch(train) [32][ 500/1879] lr: 2.0000e-02 eta: 13:17:14 time: 0.3319 data_time: 0.0145 memory: 6717 grad_norm: 2.7295 loss: 1.5764 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.5764 2023/04/14 02:31:50 - mmengine - INFO - Epoch(train) [32][ 520/1879] lr: 2.0000e-02 eta: 13:17:07 time: 0.3754 data_time: 0.0158 memory: 6717 grad_norm: 2.7203 loss: 1.9143 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.9143 2023/04/14 02:31:57 - mmengine - INFO - Epoch(train) [32][ 540/1879] lr: 2.0000e-02 eta: 13:16:59 time: 0.3579 data_time: 0.0128 memory: 6717 grad_norm: 2.6927 loss: 1.6422 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6422 2023/04/14 02:32:04 - mmengine - INFO - Epoch(train) [32][ 560/1879] lr: 2.0000e-02 eta: 13:16:51 time: 0.3730 data_time: 0.0585 memory: 6717 grad_norm: 2.8052 loss: 1.8648 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8648 2023/04/14 02:32:11 - mmengine - INFO - Epoch(train) [32][ 580/1879] lr: 2.0000e-02 eta: 13:16:43 time: 0.3517 data_time: 0.0333 memory: 6717 grad_norm: 2.6422 loss: 1.9183 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.9183 2023/04/14 02:32:19 - mmengine - INFO - Epoch(train) [32][ 600/1879] lr: 2.0000e-02 eta: 13:16:37 time: 0.4073 data_time: 0.0159 memory: 6717 grad_norm: 2.8498 loss: 1.7197 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7197 2023/04/14 02:32:26 - mmengine - INFO - Epoch(train) [32][ 620/1879] lr: 2.0000e-02 eta: 13:16:29 time: 0.3502 data_time: 0.0142 memory: 6717 grad_norm: 2.7373 loss: 1.5419 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.5419 2023/04/14 02:32:34 - mmengine - INFO - Epoch(train) [32][ 640/1879] lr: 2.0000e-02 eta: 13:16:23 time: 0.3953 data_time: 0.0132 memory: 6717 grad_norm: 2.7111 loss: 1.5860 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5860 2023/04/14 02:32:42 - mmengine - INFO - Epoch(train) [32][ 660/1879] lr: 2.0000e-02 eta: 13:16:16 time: 0.3789 data_time: 0.0157 memory: 6717 grad_norm: 2.7627 loss: 1.6338 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6338 2023/04/14 02:32:48 - mmengine - INFO - Epoch(train) [32][ 680/1879] lr: 2.0000e-02 eta: 13:16:06 time: 0.3282 data_time: 0.0136 memory: 6717 grad_norm: 2.6968 loss: 1.6136 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6136 2023/04/14 02:32:57 - mmengine - INFO - Epoch(train) [32][ 700/1879] lr: 2.0000e-02 eta: 13:16:02 time: 0.4326 data_time: 0.0163 memory: 6717 grad_norm: 2.6848 loss: 1.6491 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6491 2023/04/14 02:33:03 - mmengine - INFO - Epoch(train) [32][ 720/1879] lr: 2.0000e-02 eta: 13:15:52 time: 0.3169 data_time: 0.0151 memory: 6717 grad_norm: 2.8076 loss: 1.4194 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4194 2023/04/14 02:33:12 - mmengine - INFO - Epoch(train) [32][ 740/1879] lr: 2.0000e-02 eta: 13:15:47 time: 0.4261 data_time: 0.0160 memory: 6717 grad_norm: 2.7151 loss: 1.7674 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7674 2023/04/14 02:33:15 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 02:33:18 - mmengine - INFO - Epoch(train) [32][ 760/1879] lr: 2.0000e-02 eta: 13:15:36 time: 0.2798 data_time: 0.0151 memory: 6717 grad_norm: 2.7531 loss: 1.7071 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7071 2023/04/14 02:33:26 - mmengine - INFO - Epoch(train) [32][ 780/1879] lr: 2.0000e-02 eta: 13:15:30 time: 0.4166 data_time: 0.0151 memory: 6717 grad_norm: 2.7070 loss: 1.6928 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6928 2023/04/14 02:33:33 - mmengine - INFO - Epoch(train) [32][ 800/1879] lr: 2.0000e-02 eta: 13:15:21 time: 0.3325 data_time: 0.0148 memory: 6717 grad_norm: 2.7404 loss: 1.7369 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7369 2023/04/14 02:33:41 - mmengine - INFO - Epoch(train) [32][ 820/1879] lr: 2.0000e-02 eta: 13:15:16 time: 0.4173 data_time: 0.0125 memory: 6717 grad_norm: 2.6897 loss: 1.8154 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8154 2023/04/14 02:33:48 - mmengine - INFO - Epoch(train) [32][ 840/1879] lr: 2.0000e-02 eta: 13:15:07 time: 0.3337 data_time: 0.0141 memory: 6717 grad_norm: 2.7437 loss: 1.7025 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7025 2023/04/14 02:33:56 - mmengine - INFO - Epoch(train) [32][ 860/1879] lr: 2.0000e-02 eta: 13:15:01 time: 0.4001 data_time: 0.0143 memory: 6717 grad_norm: 2.7171 loss: 1.7898 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7898 2023/04/14 02:34:03 - mmengine - INFO - Epoch(train) [32][ 880/1879] lr: 2.0000e-02 eta: 13:14:53 time: 0.3509 data_time: 0.0124 memory: 6717 grad_norm: 2.7908 loss: 1.6869 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.6869 2023/04/14 02:34:11 - mmengine - INFO - Epoch(train) [32][ 900/1879] lr: 2.0000e-02 eta: 13:14:47 time: 0.4189 data_time: 0.0130 memory: 6717 grad_norm: 2.7412 loss: 1.5397 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5397 2023/04/14 02:34:17 - mmengine - INFO - Epoch(train) [32][ 920/1879] lr: 2.0000e-02 eta: 13:14:37 time: 0.3144 data_time: 0.0141 memory: 6717 grad_norm: 2.7339 loss: 1.6034 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.6034 2023/04/14 02:34:25 - mmengine - INFO - Epoch(train) [32][ 940/1879] lr: 2.0000e-02 eta: 13:14:31 time: 0.4023 data_time: 0.0146 memory: 6717 grad_norm: 2.7423 loss: 1.4862 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4862 2023/04/14 02:34:32 - mmengine - INFO - Epoch(train) [32][ 960/1879] lr: 2.0000e-02 eta: 13:14:22 time: 0.3249 data_time: 0.0135 memory: 6717 grad_norm: 2.7581 loss: 1.7323 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7323 2023/04/14 02:34:40 - mmengine - INFO - Epoch(train) [32][ 980/1879] lr: 2.0000e-02 eta: 13:14:16 time: 0.3959 data_time: 0.0135 memory: 6717 grad_norm: 2.7417 loss: 1.6825 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.6825 2023/04/14 02:34:46 - mmengine - INFO - Epoch(train) [32][1000/1879] lr: 2.0000e-02 eta: 13:14:07 time: 0.3312 data_time: 0.0141 memory: 6717 grad_norm: 2.7637 loss: 1.8237 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8237 2023/04/14 02:34:54 - mmengine - INFO - Epoch(train) [32][1020/1879] lr: 2.0000e-02 eta: 13:14:00 time: 0.3883 data_time: 0.0140 memory: 6717 grad_norm: 2.7000 loss: 1.8529 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8529 2023/04/14 02:35:00 - mmengine - INFO - Epoch(train) [32][1040/1879] lr: 2.0000e-02 eta: 13:13:50 time: 0.3133 data_time: 0.0144 memory: 6717 grad_norm: 2.7446 loss: 1.6675 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6675 2023/04/14 02:35:09 - mmengine - INFO - Epoch(train) [32][1060/1879] lr: 2.0000e-02 eta: 13:13:46 time: 0.4523 data_time: 0.0150 memory: 6717 grad_norm: 2.7579 loss: 1.7089 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7089 2023/04/14 02:35:16 - mmengine - INFO - Epoch(train) [32][1080/1879] lr: 2.0000e-02 eta: 13:13:36 time: 0.3148 data_time: 0.0127 memory: 6717 grad_norm: 2.7559 loss: 1.7248 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7248 2023/04/14 02:35:24 - mmengine - INFO - Epoch(train) [32][1100/1879] lr: 2.0000e-02 eta: 13:13:30 time: 0.4015 data_time: 0.0132 memory: 6717 grad_norm: 2.6921 loss: 1.7464 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7464 2023/04/14 02:35:30 - mmengine - INFO - Epoch(train) [32][1120/1879] lr: 2.0000e-02 eta: 13:13:20 time: 0.3140 data_time: 0.0157 memory: 6717 grad_norm: 2.7372 loss: 1.6874 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6874 2023/04/14 02:35:38 - mmengine - INFO - Epoch(train) [32][1140/1879] lr: 2.0000e-02 eta: 13:13:15 time: 0.4089 data_time: 0.0129 memory: 6717 grad_norm: 2.7362 loss: 1.5573 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5573 2023/04/14 02:35:46 - mmengine - INFO - Epoch(train) [32][1160/1879] lr: 2.0000e-02 eta: 13:13:07 time: 0.3687 data_time: 0.0160 memory: 6717 grad_norm: 2.7135 loss: 1.6488 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6488 2023/04/14 02:35:53 - mmengine - INFO - Epoch(train) [32][1180/1879] lr: 2.0000e-02 eta: 13:12:59 time: 0.3508 data_time: 0.0127 memory: 6717 grad_norm: 2.6715 loss: 1.5841 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.5841 2023/04/14 02:36:01 - mmengine - INFO - Epoch(train) [32][1200/1879] lr: 2.0000e-02 eta: 13:12:54 time: 0.4227 data_time: 0.0137 memory: 6717 grad_norm: 2.7195 loss: 1.6261 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6261 2023/04/14 02:36:07 - mmengine - INFO - Epoch(train) [32][1220/1879] lr: 2.0000e-02 eta: 13:12:44 time: 0.3151 data_time: 0.0132 memory: 6717 grad_norm: 2.6669 loss: 1.5508 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5508 2023/04/14 02:36:15 - mmengine - INFO - Epoch(train) [32][1240/1879] lr: 2.0000e-02 eta: 13:12:37 time: 0.3769 data_time: 0.0162 memory: 6717 grad_norm: 2.7447 loss: 1.6238 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6238 2023/04/14 02:36:23 - mmengine - INFO - Epoch(train) [32][1260/1879] lr: 2.0000e-02 eta: 13:12:31 time: 0.3950 data_time: 0.0123 memory: 6717 grad_norm: 2.7264 loss: 1.6880 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6880 2023/04/14 02:36:30 - mmengine - INFO - Epoch(train) [32][1280/1879] lr: 2.0000e-02 eta: 13:12:22 time: 0.3405 data_time: 0.0159 memory: 6717 grad_norm: 2.8192 loss: 1.6644 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6644 2023/04/14 02:36:38 - mmengine - INFO - Epoch(train) [32][1300/1879] lr: 2.0000e-02 eta: 13:12:16 time: 0.4117 data_time: 0.0128 memory: 6717 grad_norm: 2.6725 loss: 1.6654 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.6654 2023/04/14 02:36:45 - mmengine - INFO - Epoch(train) [32][1320/1879] lr: 2.0000e-02 eta: 13:12:08 time: 0.3540 data_time: 0.0140 memory: 6717 grad_norm: 2.7885 loss: 1.5822 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5822 2023/04/14 02:36:52 - mmengine - INFO - Epoch(train) [32][1340/1879] lr: 2.0000e-02 eta: 13:12:00 time: 0.3610 data_time: 0.0133 memory: 6717 grad_norm: 2.6841 loss: 1.7994 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7994 2023/04/14 02:37:01 - mmengine - INFO - Epoch(train) [32][1360/1879] lr: 2.0000e-02 eta: 13:11:56 time: 0.4350 data_time: 0.0142 memory: 6717 grad_norm: 2.7194 loss: 1.5143 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5143 2023/04/14 02:37:07 - mmengine - INFO - Epoch(train) [32][1380/1879] lr: 2.0000e-02 eta: 13:11:47 time: 0.3298 data_time: 0.0146 memory: 6717 grad_norm: 2.7131 loss: 1.7675 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7675 2023/04/14 02:37:16 - mmengine - INFO - Epoch(train) [32][1400/1879] lr: 2.0000e-02 eta: 13:11:41 time: 0.4194 data_time: 0.0162 memory: 6717 grad_norm: 2.6536 loss: 1.8395 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8395 2023/04/14 02:37:22 - mmengine - INFO - Epoch(train) [32][1420/1879] lr: 2.0000e-02 eta: 13:11:32 time: 0.3260 data_time: 0.0155 memory: 6717 grad_norm: 2.7666 loss: 1.7340 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7340 2023/04/14 02:37:31 - mmengine - INFO - Epoch(train) [32][1440/1879] lr: 2.0000e-02 eta: 13:11:27 time: 0.4168 data_time: 0.0159 memory: 6717 grad_norm: 2.7148 loss: 1.7526 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7526 2023/04/14 02:37:38 - mmengine - INFO - Epoch(train) [32][1460/1879] lr: 2.0000e-02 eta: 13:11:18 time: 0.3421 data_time: 0.0127 memory: 6717 grad_norm: 2.7446 loss: 1.8181 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8181 2023/04/14 02:37:46 - mmengine - INFO - Epoch(train) [32][1480/1879] lr: 2.0000e-02 eta: 13:11:12 time: 0.3995 data_time: 0.0169 memory: 6717 grad_norm: 2.7086 loss: 1.7376 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7376 2023/04/14 02:37:51 - mmengine - INFO - Epoch(train) [32][1500/1879] lr: 2.0000e-02 eta: 13:11:01 time: 0.2814 data_time: 0.0152 memory: 6717 grad_norm: 2.8048 loss: 1.7289 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.7289 2023/04/14 02:37:59 - mmengine - INFO - Epoch(train) [32][1520/1879] lr: 2.0000e-02 eta: 13:10:54 time: 0.3960 data_time: 0.0163 memory: 6717 grad_norm: 2.6882 loss: 1.6485 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.6485 2023/04/14 02:38:06 - mmengine - INFO - Epoch(train) [32][1540/1879] lr: 2.0000e-02 eta: 13:10:46 time: 0.3419 data_time: 0.0139 memory: 6717 grad_norm: 2.7223 loss: 1.5850 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5850 2023/04/14 02:38:14 - mmengine - INFO - Epoch(train) [32][1560/1879] lr: 2.0000e-02 eta: 13:10:39 time: 0.3975 data_time: 0.0157 memory: 6717 grad_norm: 2.7175 loss: 1.6821 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6821 2023/04/14 02:38:21 - mmengine - INFO - Epoch(train) [32][1580/1879] lr: 2.0000e-02 eta: 13:10:32 time: 0.3667 data_time: 0.0345 memory: 6717 grad_norm: 2.7588 loss: 1.5536 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5536 2023/04/14 02:38:29 - mmengine - INFO - Epoch(train) [32][1600/1879] lr: 2.0000e-02 eta: 13:10:25 time: 0.3756 data_time: 0.0157 memory: 6717 grad_norm: 2.7129 loss: 1.7616 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7616 2023/04/14 02:38:36 - mmengine - INFO - Epoch(train) [32][1620/1879] lr: 2.0000e-02 eta: 13:10:17 time: 0.3694 data_time: 0.0300 memory: 6717 grad_norm: 2.7296 loss: 1.8946 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.8946 2023/04/14 02:38:44 - mmengine - INFO - Epoch(train) [32][1640/1879] lr: 2.0000e-02 eta: 13:10:10 time: 0.3740 data_time: 0.0152 memory: 6717 grad_norm: 2.7175 loss: 1.8298 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8298 2023/04/14 02:38:51 - mmengine - INFO - Epoch(train) [32][1660/1879] lr: 2.0000e-02 eta: 13:10:01 time: 0.3459 data_time: 0.0135 memory: 6717 grad_norm: 2.6971 loss: 1.9352 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.9352 2023/04/14 02:38:58 - mmengine - INFO - Epoch(train) [32][1680/1879] lr: 2.0000e-02 eta: 13:09:55 time: 0.3872 data_time: 0.0661 memory: 6717 grad_norm: 2.6935 loss: 1.6474 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.6474 2023/04/14 02:39:06 - mmengine - INFO - Epoch(train) [32][1700/1879] lr: 2.0000e-02 eta: 13:09:48 time: 0.3903 data_time: 0.1553 memory: 6717 grad_norm: 2.7375 loss: 1.7369 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7369 2023/04/14 02:39:13 - mmengine - INFO - Epoch(train) [32][1720/1879] lr: 2.0000e-02 eta: 13:09:39 time: 0.3293 data_time: 0.0630 memory: 6717 grad_norm: 2.7361 loss: 1.5524 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.5524 2023/04/14 02:39:20 - mmengine - INFO - Epoch(train) [32][1740/1879] lr: 2.0000e-02 eta: 13:09:32 time: 0.3808 data_time: 0.1265 memory: 6717 grad_norm: 2.6239 loss: 1.7162 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7162 2023/04/14 02:39:24 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 02:39:27 - mmengine - INFO - Epoch(train) [32][1760/1879] lr: 2.0000e-02 eta: 13:09:24 time: 0.3585 data_time: 0.1000 memory: 6717 grad_norm: 2.6948 loss: 1.7772 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7772 2023/04/14 02:39:34 - mmengine - INFO - Epoch(train) [32][1780/1879] lr: 2.0000e-02 eta: 13:09:15 time: 0.3338 data_time: 0.1205 memory: 6717 grad_norm: 2.7682 loss: 1.9149 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.9149 2023/04/14 02:39:42 - mmengine - INFO - Epoch(train) [32][1800/1879] lr: 2.0000e-02 eta: 13:09:08 time: 0.3799 data_time: 0.1407 memory: 6717 grad_norm: 2.7297 loss: 1.5856 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5856 2023/04/14 02:39:49 - mmengine - INFO - Epoch(train) [32][1820/1879] lr: 2.0000e-02 eta: 13:09:00 time: 0.3531 data_time: 0.1132 memory: 6717 grad_norm: 2.7216 loss: 1.5623 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5623 2023/04/14 02:39:57 - mmengine - INFO - Epoch(train) [32][1840/1879] lr: 2.0000e-02 eta: 13:08:54 time: 0.4103 data_time: 0.1491 memory: 6717 grad_norm: 2.7127 loss: 1.5761 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5761 2023/04/14 02:40:04 - mmengine - INFO - Epoch(train) [32][1860/1879] lr: 2.0000e-02 eta: 13:08:45 time: 0.3341 data_time: 0.0218 memory: 6717 grad_norm: 2.7977 loss: 1.6493 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6493 2023/04/14 02:40:09 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 02:40:09 - mmengine - INFO - Epoch(train) [32][1879/1879] lr: 2.0000e-02 eta: 13:08:35 time: 0.2819 data_time: 0.0280 memory: 6717 grad_norm: 2.7357 loss: 1.8123 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.8123 2023/04/14 02:40:18 - mmengine - INFO - Epoch(val) [32][ 20/155] eta: 0:01:00 time: 0.4505 data_time: 0.4169 memory: 1391 2023/04/14 02:40:25 - mmengine - INFO - Epoch(val) [32][ 40/155] eta: 0:00:44 time: 0.3289 data_time: 0.2963 memory: 1391 2023/04/14 02:40:33 - mmengine - INFO - Epoch(val) [32][ 60/155] eta: 0:00:38 time: 0.4331 data_time: 0.3995 memory: 1391 2023/04/14 02:40:40 - mmengine - INFO - Epoch(val) [32][ 80/155] eta: 0:00:28 time: 0.3169 data_time: 0.2844 memory: 1391 2023/04/14 02:40:49 - mmengine - INFO - Epoch(val) [32][100/155] eta: 0:00:21 time: 0.4570 data_time: 0.4237 memory: 1391 2023/04/14 02:40:55 - mmengine - INFO - Epoch(val) [32][120/155] eta: 0:00:13 time: 0.3025 data_time: 0.2699 memory: 1391 2023/04/14 02:41:05 - mmengine - INFO - Epoch(val) [32][140/155] eta: 0:00:05 time: 0.4841 data_time: 0.4509 memory: 1391 2023/04/14 02:41:12 - mmengine - INFO - Epoch(val) [32][155/155] acc/top1: 0.6025 acc/top5: 0.8368 acc/mean1: 0.6023 data_time: 0.4199 time: 0.4523 2023/04/14 02:41:22 - mmengine - INFO - Epoch(train) [33][ 20/1879] lr: 2.0000e-02 eta: 13:08:33 time: 0.4840 data_time: 0.2871 memory: 6717 grad_norm: 2.7736 loss: 1.6841 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6841 2023/04/14 02:41:29 - mmengine - INFO - Epoch(train) [33][ 40/1879] lr: 2.0000e-02 eta: 13:08:24 time: 0.3509 data_time: 0.1137 memory: 6717 grad_norm: 2.7244 loss: 1.5764 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5764 2023/04/14 02:41:37 - mmengine - INFO - Epoch(train) [33][ 60/1879] lr: 2.0000e-02 eta: 13:08:19 time: 0.4094 data_time: 0.1541 memory: 6717 grad_norm: 2.6764 loss: 1.6222 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6222 2023/04/14 02:41:43 - mmengine - INFO - Epoch(train) [33][ 80/1879] lr: 2.0000e-02 eta: 13:08:09 time: 0.3293 data_time: 0.1570 memory: 6717 grad_norm: 2.7594 loss: 1.6655 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6655 2023/04/14 02:41:52 - mmengine - INFO - Epoch(train) [33][ 100/1879] lr: 2.0000e-02 eta: 13:08:04 time: 0.4053 data_time: 0.2704 memory: 6717 grad_norm: 2.6886 loss: 1.8342 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8342 2023/04/14 02:41:58 - mmengine - INFO - Epoch(train) [33][ 120/1879] lr: 2.0000e-02 eta: 13:07:54 time: 0.3123 data_time: 0.1793 memory: 6717 grad_norm: 2.7905 loss: 1.5279 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.5279 2023/04/14 02:42:06 - mmengine - INFO - Epoch(train) [33][ 140/1879] lr: 2.0000e-02 eta: 13:07:48 time: 0.4193 data_time: 0.2808 memory: 6717 grad_norm: 2.7292 loss: 1.7712 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7712 2023/04/14 02:42:13 - mmengine - INFO - Epoch(train) [33][ 160/1879] lr: 2.0000e-02 eta: 13:07:39 time: 0.3358 data_time: 0.1989 memory: 6717 grad_norm: 2.8516 loss: 1.5604 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5604 2023/04/14 02:42:21 - mmengine - INFO - Epoch(train) [33][ 180/1879] lr: 2.0000e-02 eta: 13:07:33 time: 0.3856 data_time: 0.2455 memory: 6717 grad_norm: 2.6803 loss: 1.5883 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5883 2023/04/14 02:42:27 - mmengine - INFO - Epoch(train) [33][ 200/1879] lr: 2.0000e-02 eta: 13:07:23 time: 0.3250 data_time: 0.1379 memory: 6717 grad_norm: 2.7313 loss: 1.7431 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7431 2023/04/14 02:42:35 - mmengine - INFO - Epoch(train) [33][ 220/1879] lr: 2.0000e-02 eta: 13:07:18 time: 0.4168 data_time: 0.1238 memory: 6717 grad_norm: 2.7944 loss: 1.7253 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7253 2023/04/14 02:42:42 - mmengine - INFO - Epoch(train) [33][ 240/1879] lr: 2.0000e-02 eta: 13:07:08 time: 0.3181 data_time: 0.0760 memory: 6717 grad_norm: 2.7729 loss: 1.6344 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6344 2023/04/14 02:42:51 - mmengine - INFO - Epoch(train) [33][ 260/1879] lr: 2.0000e-02 eta: 13:07:04 time: 0.4486 data_time: 0.1061 memory: 6717 grad_norm: 2.7214 loss: 1.7185 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7185 2023/04/14 02:42:57 - mmengine - INFO - Epoch(train) [33][ 280/1879] lr: 2.0000e-02 eta: 13:06:54 time: 0.3041 data_time: 0.0461 memory: 6717 grad_norm: 2.7357 loss: 1.8455 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8455 2023/04/14 02:43:05 - mmengine - INFO - Epoch(train) [33][ 300/1879] lr: 2.0000e-02 eta: 13:06:47 time: 0.3861 data_time: 0.0803 memory: 6717 grad_norm: 2.7519 loss: 1.6619 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6619 2023/04/14 02:43:11 - mmengine - INFO - Epoch(train) [33][ 320/1879] lr: 2.0000e-02 eta: 13:06:39 time: 0.3401 data_time: 0.1072 memory: 6717 grad_norm: 2.7597 loss: 1.6590 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6590 2023/04/14 02:43:19 - mmengine - INFO - Epoch(train) [33][ 340/1879] lr: 2.0000e-02 eta: 13:06:32 time: 0.3871 data_time: 0.1660 memory: 6717 grad_norm: 2.7312 loss: 1.7073 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7073 2023/04/14 02:43:26 - mmengine - INFO - Epoch(train) [33][ 360/1879] lr: 2.0000e-02 eta: 13:06:23 time: 0.3466 data_time: 0.1177 memory: 6717 grad_norm: 2.6536 loss: 1.6269 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.6269 2023/04/14 02:43:34 - mmengine - INFO - Epoch(train) [33][ 380/1879] lr: 2.0000e-02 eta: 13:06:18 time: 0.4065 data_time: 0.0134 memory: 6717 grad_norm: 2.6941 loss: 1.5394 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5394 2023/04/14 02:43:41 - mmengine - INFO - Epoch(train) [33][ 400/1879] lr: 2.0000e-02 eta: 13:06:09 time: 0.3372 data_time: 0.0126 memory: 6717 grad_norm: 2.7415 loss: 1.5160 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5160 2023/04/14 02:43:49 - mmengine - INFO - Epoch(train) [33][ 420/1879] lr: 2.0000e-02 eta: 13:06:03 time: 0.4141 data_time: 0.0155 memory: 6717 grad_norm: 2.7908 loss: 1.6061 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6061 2023/04/14 02:43:56 - mmengine - INFO - Epoch(train) [33][ 440/1879] lr: 2.0000e-02 eta: 13:05:54 time: 0.3219 data_time: 0.0136 memory: 6717 grad_norm: 2.6830 loss: 1.6362 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6362 2023/04/14 02:44:04 - mmengine - INFO - Epoch(train) [33][ 460/1879] lr: 2.0000e-02 eta: 13:05:48 time: 0.4156 data_time: 0.0199 memory: 6717 grad_norm: 2.7186 loss: 1.5934 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5934 2023/04/14 02:44:11 - mmengine - INFO - Epoch(train) [33][ 480/1879] lr: 2.0000e-02 eta: 13:05:39 time: 0.3278 data_time: 0.0130 memory: 6717 grad_norm: 2.7420 loss: 1.6705 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6705 2023/04/14 02:44:19 - mmengine - INFO - Epoch(train) [33][ 500/1879] lr: 2.0000e-02 eta: 13:05:33 time: 0.4102 data_time: 0.0158 memory: 6717 grad_norm: 2.6747 loss: 1.7020 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.7020 2023/04/14 02:44:25 - mmengine - INFO - Epoch(train) [33][ 520/1879] lr: 2.0000e-02 eta: 13:05:23 time: 0.2994 data_time: 0.0132 memory: 6717 grad_norm: 2.7654 loss: 1.5605 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.5605 2023/04/14 02:44:33 - mmengine - INFO - Epoch(train) [33][ 540/1879] lr: 2.0000e-02 eta: 13:05:17 time: 0.4048 data_time: 0.0157 memory: 6717 grad_norm: 2.7377 loss: 1.7623 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7623 2023/04/14 02:44:39 - mmengine - INFO - Epoch(train) [33][ 560/1879] lr: 2.0000e-02 eta: 13:05:08 time: 0.3230 data_time: 0.0133 memory: 6717 grad_norm: 2.7960 loss: 1.6441 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6441 2023/04/14 02:44:48 - mmengine - INFO - Epoch(train) [33][ 580/1879] lr: 2.0000e-02 eta: 13:05:03 time: 0.4325 data_time: 0.0257 memory: 6717 grad_norm: 2.7531 loss: 1.5630 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.5630 2023/04/14 02:44:54 - mmengine - INFO - Epoch(train) [33][ 600/1879] lr: 2.0000e-02 eta: 13:04:52 time: 0.3006 data_time: 0.0190 memory: 6717 grad_norm: 2.8038 loss: 1.6748 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6748 2023/04/14 02:45:02 - mmengine - INFO - Epoch(train) [33][ 620/1879] lr: 2.0000e-02 eta: 13:04:47 time: 0.4260 data_time: 0.0189 memory: 6717 grad_norm: 2.7792 loss: 1.7390 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7390 2023/04/14 02:45:09 - mmengine - INFO - Epoch(train) [33][ 640/1879] lr: 2.0000e-02 eta: 13:04:38 time: 0.3171 data_time: 0.0633 memory: 6717 grad_norm: 2.7059 loss: 1.6517 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6517 2023/04/14 02:45:18 - mmengine - INFO - Epoch(train) [33][ 660/1879] lr: 2.0000e-02 eta: 13:04:33 time: 0.4427 data_time: 0.0665 memory: 6717 grad_norm: 2.7372 loss: 1.5897 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5897 2023/04/14 02:45:24 - mmengine - INFO - Epoch(train) [33][ 680/1879] lr: 2.0000e-02 eta: 13:04:24 time: 0.3347 data_time: 0.0127 memory: 6717 grad_norm: 2.7565 loss: 1.6001 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6001 2023/04/14 02:45:33 - mmengine - INFO - Epoch(train) [33][ 700/1879] lr: 2.0000e-02 eta: 13:04:20 time: 0.4314 data_time: 0.0156 memory: 6717 grad_norm: 2.6773 loss: 1.7261 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7261 2023/04/14 02:45:40 - mmengine - INFO - Epoch(train) [33][ 720/1879] lr: 2.0000e-02 eta: 13:04:10 time: 0.3285 data_time: 0.0139 memory: 6717 grad_norm: 2.7081 loss: 1.6327 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6327 2023/04/14 02:45:48 - mmengine - INFO - Epoch(train) [33][ 740/1879] lr: 2.0000e-02 eta: 13:04:05 time: 0.4145 data_time: 0.0144 memory: 6717 grad_norm: 2.8198 loss: 1.7314 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7314 2023/04/14 02:45:54 - mmengine - INFO - Epoch(train) [33][ 760/1879] lr: 2.0000e-02 eta: 13:03:55 time: 0.3119 data_time: 0.0152 memory: 6717 grad_norm: 2.6376 loss: 1.6607 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6607 2023/04/14 02:46:03 - mmengine - INFO - Epoch(train) [33][ 780/1879] lr: 2.0000e-02 eta: 13:03:50 time: 0.4207 data_time: 0.0153 memory: 6717 grad_norm: 2.7016 loss: 1.7177 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7177 2023/04/14 02:46:09 - mmengine - INFO - Epoch(train) [33][ 800/1879] lr: 2.0000e-02 eta: 13:03:41 time: 0.3268 data_time: 0.0126 memory: 6717 grad_norm: 2.7209 loss: 1.4878 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.4878 2023/04/14 02:46:17 - mmengine - INFO - Epoch(train) [33][ 820/1879] lr: 2.0000e-02 eta: 13:03:34 time: 0.3919 data_time: 0.0148 memory: 6717 grad_norm: 2.7533 loss: 1.5978 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5978 2023/04/14 02:46:23 - mmengine - INFO - Epoch(train) [33][ 840/1879] lr: 2.0000e-02 eta: 13:03:24 time: 0.3169 data_time: 0.0141 memory: 6717 grad_norm: 2.7288 loss: 1.5646 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.5646 2023/04/14 02:46:32 - mmengine - INFO - Epoch(train) [33][ 860/1879] lr: 2.0000e-02 eta: 13:03:19 time: 0.4191 data_time: 0.0142 memory: 6717 grad_norm: 2.7294 loss: 1.4815 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4815 2023/04/14 02:46:36 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 02:46:39 - mmengine - INFO - Epoch(train) [33][ 880/1879] lr: 2.0000e-02 eta: 13:03:11 time: 0.3488 data_time: 0.0140 memory: 6717 grad_norm: 2.6501 loss: 1.4396 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4396 2023/04/14 02:46:47 - mmengine - INFO - Epoch(train) [33][ 900/1879] lr: 2.0000e-02 eta: 13:03:05 time: 0.4038 data_time: 0.0153 memory: 6717 grad_norm: 2.7524 loss: 1.6847 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.6847 2023/04/14 02:46:53 - mmengine - INFO - Epoch(train) [33][ 920/1879] lr: 2.0000e-02 eta: 13:02:54 time: 0.3020 data_time: 0.0132 memory: 6717 grad_norm: 2.7135 loss: 1.7979 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7979 2023/04/14 02:47:01 - mmengine - INFO - Epoch(train) [33][ 940/1879] lr: 2.0000e-02 eta: 13:02:50 time: 0.4347 data_time: 0.0171 memory: 6717 grad_norm: 2.7039 loss: 1.6835 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.6835 2023/04/14 02:47:07 - mmengine - INFO - Epoch(train) [33][ 960/1879] lr: 2.0000e-02 eta: 13:02:39 time: 0.2997 data_time: 0.0444 memory: 6717 grad_norm: 2.7784 loss: 1.7723 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7723 2023/04/14 02:47:16 - mmengine - INFO - Epoch(train) [33][ 980/1879] lr: 2.0000e-02 eta: 13:02:33 time: 0.4066 data_time: 0.0691 memory: 6717 grad_norm: 2.6846 loss: 1.5774 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5774 2023/04/14 02:47:22 - mmengine - INFO - Epoch(train) [33][1000/1879] lr: 2.0000e-02 eta: 13:02:24 time: 0.3271 data_time: 0.0284 memory: 6717 grad_norm: 2.7085 loss: 1.4664 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.4664 2023/04/14 02:47:31 - mmengine - INFO - Epoch(train) [33][1020/1879] lr: 2.0000e-02 eta: 13:02:20 time: 0.4403 data_time: 0.0162 memory: 6717 grad_norm: 2.7406 loss: 1.7191 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7191 2023/04/14 02:47:37 - mmengine - INFO - Epoch(train) [33][1040/1879] lr: 2.0000e-02 eta: 13:02:10 time: 0.3099 data_time: 0.0158 memory: 6717 grad_norm: 2.7895 loss: 1.7527 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7527 2023/04/14 02:47:45 - mmengine - INFO - Epoch(train) [33][1060/1879] lr: 2.0000e-02 eta: 13:02:04 time: 0.4104 data_time: 0.0157 memory: 6717 grad_norm: 2.7661 loss: 1.4154 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4154 2023/04/14 02:47:51 - mmengine - INFO - Epoch(train) [33][1080/1879] lr: 2.0000e-02 eta: 13:01:54 time: 0.2970 data_time: 0.0140 memory: 6717 grad_norm: 2.7582 loss: 1.6845 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.6845 2023/04/14 02:48:00 - mmengine - INFO - Epoch(train) [33][1100/1879] lr: 2.0000e-02 eta: 13:01:49 time: 0.4297 data_time: 0.1816 memory: 6717 grad_norm: 2.7887 loss: 1.7648 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7648 2023/04/14 02:48:07 - mmengine - INFO - Epoch(train) [33][1120/1879] lr: 2.0000e-02 eta: 13:01:40 time: 0.3363 data_time: 0.1957 memory: 6717 grad_norm: 2.6467 loss: 1.7610 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.7610 2023/04/14 02:48:15 - mmengine - INFO - Epoch(train) [33][1140/1879] lr: 2.0000e-02 eta: 13:01:35 time: 0.4302 data_time: 0.2946 memory: 6717 grad_norm: 2.7348 loss: 1.7736 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7736 2023/04/14 02:48:22 - mmengine - INFO - Epoch(train) [33][1160/1879] lr: 2.0000e-02 eta: 13:01:27 time: 0.3497 data_time: 0.2111 memory: 6717 grad_norm: 2.7589 loss: 1.6527 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6527 2023/04/14 02:48:30 - mmengine - INFO - Epoch(train) [33][1180/1879] lr: 2.0000e-02 eta: 13:01:21 time: 0.4151 data_time: 0.2746 memory: 6717 grad_norm: 2.7089 loss: 1.4950 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4950 2023/04/14 02:48:37 - mmengine - INFO - Epoch(train) [33][1200/1879] lr: 2.0000e-02 eta: 13:01:12 time: 0.3162 data_time: 0.1769 memory: 6717 grad_norm: 2.7418 loss: 1.6206 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6206 2023/04/14 02:48:45 - mmengine - INFO - Epoch(train) [33][1220/1879] lr: 2.0000e-02 eta: 13:01:06 time: 0.4263 data_time: 0.2855 memory: 6717 grad_norm: 2.7192 loss: 1.6415 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6415 2023/04/14 02:48:52 - mmengine - INFO - Epoch(train) [33][1240/1879] lr: 2.0000e-02 eta: 13:00:57 time: 0.3183 data_time: 0.1791 memory: 6717 grad_norm: 2.7808 loss: 1.5857 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5857 2023/04/14 02:49:00 - mmengine - INFO - Epoch(train) [33][1260/1879] lr: 2.0000e-02 eta: 13:00:52 time: 0.4323 data_time: 0.2932 memory: 6717 grad_norm: 2.7383 loss: 1.6673 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6673 2023/04/14 02:49:07 - mmengine - INFO - Epoch(train) [33][1280/1879] lr: 2.0000e-02 eta: 13:00:44 time: 0.3574 data_time: 0.2176 memory: 6717 grad_norm: 2.7260 loss: 1.9041 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9041 2023/04/14 02:49:16 - mmengine - INFO - Epoch(train) [33][1300/1879] lr: 2.0000e-02 eta: 13:00:39 time: 0.4274 data_time: 0.2920 memory: 6717 grad_norm: 2.7249 loss: 1.6239 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6239 2023/04/14 02:49:22 - mmengine - INFO - Epoch(train) [33][1320/1879] lr: 2.0000e-02 eta: 13:00:29 time: 0.3106 data_time: 0.1721 memory: 6717 grad_norm: 2.6668 loss: 1.5625 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5625 2023/04/14 02:49:30 - mmengine - INFO - Epoch(train) [33][1340/1879] lr: 2.0000e-02 eta: 13:00:23 time: 0.4026 data_time: 0.2621 memory: 6717 grad_norm: 2.7772 loss: 1.8454 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.8454 2023/04/14 02:49:37 - mmengine - INFO - Epoch(train) [33][1360/1879] lr: 2.0000e-02 eta: 13:00:13 time: 0.3108 data_time: 0.1722 memory: 6717 grad_norm: 2.7253 loss: 1.6049 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6049 2023/04/14 02:49:45 - mmengine - INFO - Epoch(train) [33][1380/1879] lr: 2.0000e-02 eta: 13:00:09 time: 0.4428 data_time: 0.2998 memory: 6717 grad_norm: 2.7493 loss: 1.6351 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6351 2023/04/14 02:49:52 - mmengine - INFO - Epoch(train) [33][1400/1879] lr: 2.0000e-02 eta: 12:59:59 time: 0.3144 data_time: 0.1735 memory: 6717 grad_norm: 2.7353 loss: 1.7601 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7601 2023/04/14 02:50:01 - mmengine - INFO - Epoch(train) [33][1420/1879] lr: 2.0000e-02 eta: 12:59:55 time: 0.4463 data_time: 0.3039 memory: 6717 grad_norm: 2.7178 loss: 1.5004 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.5004 2023/04/14 02:50:07 - mmengine - INFO - Epoch(train) [33][1440/1879] lr: 2.0000e-02 eta: 12:59:45 time: 0.3180 data_time: 0.1764 memory: 6717 grad_norm: 2.7904 loss: 1.6722 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6722 2023/04/14 02:50:15 - mmengine - INFO - Epoch(train) [33][1460/1879] lr: 2.0000e-02 eta: 12:59:39 time: 0.4026 data_time: 0.2608 memory: 6717 grad_norm: 2.7407 loss: 1.5561 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5561 2023/04/14 02:50:21 - mmengine - INFO - Epoch(train) [33][1480/1879] lr: 2.0000e-02 eta: 12:59:29 time: 0.3025 data_time: 0.1591 memory: 6717 grad_norm: 2.6498 loss: 1.5848 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5848 2023/04/14 02:50:29 - mmengine - INFO - Epoch(train) [33][1500/1879] lr: 2.0000e-02 eta: 12:59:23 time: 0.4108 data_time: 0.2673 memory: 6717 grad_norm: 2.7698 loss: 1.6561 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6561 2023/04/14 02:50:36 - mmengine - INFO - Epoch(train) [33][1520/1879] lr: 2.0000e-02 eta: 12:59:14 time: 0.3277 data_time: 0.1854 memory: 6717 grad_norm: 2.6768 loss: 1.6351 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.6351 2023/04/14 02:50:44 - mmengine - INFO - Epoch(train) [33][1540/1879] lr: 2.0000e-02 eta: 12:59:09 time: 0.4304 data_time: 0.2864 memory: 6717 grad_norm: 2.7778 loss: 1.7679 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7679 2023/04/14 02:50:51 - mmengine - INFO - Epoch(train) [33][1560/1879] lr: 2.0000e-02 eta: 12:59:01 time: 0.3483 data_time: 0.2080 memory: 6717 grad_norm: 2.6665 loss: 1.6181 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.6181 2023/04/14 02:50:59 - mmengine - INFO - Epoch(train) [33][1580/1879] lr: 2.0000e-02 eta: 12:58:54 time: 0.3878 data_time: 0.2441 memory: 6717 grad_norm: 2.7544 loss: 1.8689 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8689 2023/04/14 02:51:06 - mmengine - INFO - Epoch(train) [33][1600/1879] lr: 2.0000e-02 eta: 12:58:45 time: 0.3197 data_time: 0.1782 memory: 6717 grad_norm: 2.7652 loss: 1.8668 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8668 2023/04/14 02:51:13 - mmengine - INFO - Epoch(train) [33][1620/1879] lr: 2.0000e-02 eta: 12:58:38 time: 0.3891 data_time: 0.2424 memory: 6717 grad_norm: 2.6558 loss: 1.7776 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.7776 2023/04/14 02:51:23 - mmengine - INFO - Epoch(train) [33][1640/1879] lr: 2.0000e-02 eta: 12:58:35 time: 0.4687 data_time: 0.0844 memory: 6717 grad_norm: 2.7699 loss: 1.7835 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7835 2023/04/14 02:51:31 - mmengine - INFO - Epoch(train) [33][1660/1879] lr: 2.0000e-02 eta: 12:58:28 time: 0.3952 data_time: 0.0145 memory: 6717 grad_norm: 2.6752 loss: 1.7488 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7488 2023/04/14 02:51:38 - mmengine - INFO - Epoch(train) [33][1680/1879] lr: 2.0000e-02 eta: 12:58:20 time: 0.3598 data_time: 0.0116 memory: 6717 grad_norm: 2.7191 loss: 1.5883 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5883 2023/04/14 02:51:46 - mmengine - INFO - Epoch(train) [33][1700/1879] lr: 2.0000e-02 eta: 12:58:14 time: 0.3913 data_time: 0.0146 memory: 6717 grad_norm: 2.7230 loss: 1.6985 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.6985 2023/04/14 02:51:52 - mmengine - INFO - Epoch(train) [33][1720/1879] lr: 2.0000e-02 eta: 12:58:04 time: 0.3084 data_time: 0.0143 memory: 6717 grad_norm: 2.7567 loss: 1.7048 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7048 2023/04/14 02:52:00 - mmengine - INFO - Epoch(train) [33][1740/1879] lr: 2.0000e-02 eta: 12:57:58 time: 0.4044 data_time: 0.0145 memory: 6717 grad_norm: 2.6998 loss: 1.5801 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5801 2023/04/14 02:52:06 - mmengine - INFO - Epoch(train) [33][1760/1879] lr: 2.0000e-02 eta: 12:57:49 time: 0.3240 data_time: 0.0145 memory: 6717 grad_norm: 2.7763 loss: 1.5609 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5609 2023/04/14 02:52:15 - mmengine - INFO - Epoch(train) [33][1780/1879] lr: 2.0000e-02 eta: 12:57:44 time: 0.4376 data_time: 0.0138 memory: 6717 grad_norm: 2.6806 loss: 1.5191 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5191 2023/04/14 02:52:22 - mmengine - INFO - Epoch(train) [33][1800/1879] lr: 2.0000e-02 eta: 12:57:35 time: 0.3302 data_time: 0.0155 memory: 6717 grad_norm: 2.6800 loss: 1.6484 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6484 2023/04/14 02:52:29 - mmengine - INFO - Epoch(train) [33][1820/1879] lr: 2.0000e-02 eta: 12:57:27 time: 0.3690 data_time: 0.0146 memory: 6717 grad_norm: 2.7907 loss: 1.5939 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5939 2023/04/14 02:52:37 - mmengine - INFO - Epoch(train) [33][1840/1879] lr: 2.0000e-02 eta: 12:57:20 time: 0.3744 data_time: 0.0133 memory: 6717 grad_norm: 2.7105 loss: 1.7060 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7060 2023/04/14 02:52:44 - mmengine - INFO - Epoch(train) [33][1860/1879] lr: 2.0000e-02 eta: 12:57:13 time: 0.3870 data_time: 0.0151 memory: 6717 grad_norm: 2.7349 loss: 1.7440 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7440 2023/04/14 02:52:48 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 02:52:50 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 02:52:50 - mmengine - INFO - Epoch(train) [33][1879/1879] lr: 2.0000e-02 eta: 12:57:04 time: 0.3091 data_time: 0.0134 memory: 6717 grad_norm: 2.7803 loss: 1.6924 top1_acc: 0.4286 top5_acc: 0.7143 loss_cls: 1.6924 2023/04/14 02:52:50 - mmengine - INFO - Saving checkpoint at 33 epochs 2023/04/14 02:53:00 - mmengine - INFO - Epoch(val) [33][ 20/155] eta: 0:01:02 time: 0.4616 data_time: 0.4284 memory: 1391 2023/04/14 02:53:06 - mmengine - INFO - Epoch(val) [33][ 40/155] eta: 0:00:43 time: 0.2932 data_time: 0.2603 memory: 1391 2023/04/14 02:53:15 - mmengine - INFO - Epoch(val) [33][ 60/155] eta: 0:00:38 time: 0.4571 data_time: 0.4239 memory: 1391 2023/04/14 02:53:22 - mmengine - INFO - Epoch(val) [33][ 80/155] eta: 0:00:28 time: 0.3160 data_time: 0.2824 memory: 1391 2023/04/14 02:53:31 - mmengine - INFO - Epoch(val) [33][100/155] eta: 0:00:21 time: 0.4551 data_time: 0.4223 memory: 1391 2023/04/14 02:53:37 - mmengine - INFO - Epoch(val) [33][120/155] eta: 0:00:13 time: 0.2988 data_time: 0.2651 memory: 1391 2023/04/14 02:53:46 - mmengine - INFO - Epoch(val) [33][140/155] eta: 0:00:05 time: 0.4832 data_time: 0.4506 memory: 1391 2023/04/14 02:53:53 - mmengine - INFO - Epoch(val) [33][155/155] acc/top1: 0.6068 acc/top5: 0.8371 acc/mean1: 0.6068 data_time: 0.4175 time: 0.4496 2023/04/14 02:53:53 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_30.pth is removed 2023/04/14 02:53:54 - mmengine - INFO - The best checkpoint with 0.6068 acc/top1 at 33 epoch is saved to best_acc_top1_epoch_33.pth. 2023/04/14 02:54:03 - mmengine - INFO - Epoch(train) [34][ 20/1879] lr: 2.0000e-02 eta: 12:57:01 time: 0.4650 data_time: 0.3282 memory: 6717 grad_norm: 2.8077 loss: 1.6699 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.6699 2023/04/14 02:54:10 - mmengine - INFO - Epoch(train) [34][ 40/1879] lr: 2.0000e-02 eta: 12:56:52 time: 0.3308 data_time: 0.1870 memory: 6717 grad_norm: 2.7147 loss: 1.7001 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.7001 2023/04/14 02:54:18 - mmengine - INFO - Epoch(train) [34][ 60/1879] lr: 2.0000e-02 eta: 12:56:46 time: 0.4022 data_time: 0.1671 memory: 6717 grad_norm: 2.7580 loss: 1.6473 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6473 2023/04/14 02:54:24 - mmengine - INFO - Epoch(train) [34][ 80/1879] lr: 2.0000e-02 eta: 12:56:36 time: 0.3184 data_time: 0.1281 memory: 6717 grad_norm: 2.7777 loss: 1.8667 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8667 2023/04/14 02:54:33 - mmengine - INFO - Epoch(train) [34][ 100/1879] lr: 2.0000e-02 eta: 12:56:31 time: 0.4203 data_time: 0.1153 memory: 6717 grad_norm: 2.8301 loss: 1.6231 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6231 2023/04/14 02:54:39 - mmengine - INFO - Epoch(train) [34][ 120/1879] lr: 2.0000e-02 eta: 12:56:22 time: 0.3418 data_time: 0.0985 memory: 6717 grad_norm: 2.6916 loss: 1.3701 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3701 2023/04/14 02:54:48 - mmengine - INFO - Epoch(train) [34][ 140/1879] lr: 2.0000e-02 eta: 12:56:17 time: 0.4132 data_time: 0.0139 memory: 6717 grad_norm: 2.7352 loss: 1.6173 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.6173 2023/04/14 02:54:54 - mmengine - INFO - Epoch(train) [34][ 160/1879] lr: 2.0000e-02 eta: 12:56:07 time: 0.3069 data_time: 0.0133 memory: 6717 grad_norm: 2.6920 loss: 1.6094 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6094 2023/04/14 02:55:02 - mmengine - INFO - Epoch(train) [34][ 180/1879] lr: 2.0000e-02 eta: 12:56:01 time: 0.4063 data_time: 0.0149 memory: 6717 grad_norm: 2.7346 loss: 1.7391 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.7391 2023/04/14 02:55:08 - mmengine - INFO - Epoch(train) [34][ 200/1879] lr: 2.0000e-02 eta: 12:55:51 time: 0.3256 data_time: 0.0139 memory: 6717 grad_norm: 2.6896 loss: 1.6236 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6236 2023/04/14 02:55:16 - mmengine - INFO - Epoch(train) [34][ 220/1879] lr: 2.0000e-02 eta: 12:55:45 time: 0.3957 data_time: 0.0213 memory: 6717 grad_norm: 2.7333 loss: 1.7886 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7886 2023/04/14 02:55:24 - mmengine - INFO - Epoch(train) [34][ 240/1879] lr: 2.0000e-02 eta: 12:55:38 time: 0.3809 data_time: 0.1327 memory: 6717 grad_norm: 2.7233 loss: 1.4665 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.4665 2023/04/14 02:55:31 - mmengine - INFO - Epoch(train) [34][ 260/1879] lr: 2.0000e-02 eta: 12:55:31 time: 0.3708 data_time: 0.1042 memory: 6717 grad_norm: 2.6873 loss: 1.5045 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5045 2023/04/14 02:55:38 - mmengine - INFO - Epoch(train) [34][ 280/1879] lr: 2.0000e-02 eta: 12:55:22 time: 0.3381 data_time: 0.0653 memory: 6717 grad_norm: 2.7338 loss: 1.7287 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7287 2023/04/14 02:55:47 - mmengine - INFO - Epoch(train) [34][ 300/1879] lr: 2.0000e-02 eta: 12:55:17 time: 0.4284 data_time: 0.0151 memory: 6717 grad_norm: 2.6649 loss: 1.8247 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8247 2023/04/14 02:55:54 - mmengine - INFO - Epoch(train) [34][ 320/1879] lr: 2.0000e-02 eta: 12:55:08 time: 0.3450 data_time: 0.0137 memory: 6717 grad_norm: 2.7910 loss: 1.4334 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4334 2023/04/14 02:56:02 - mmengine - INFO - Epoch(train) [34][ 340/1879] lr: 2.0000e-02 eta: 12:55:03 time: 0.4106 data_time: 0.0142 memory: 6717 grad_norm: 2.7218 loss: 1.6438 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6438 2023/04/14 02:56:08 - mmengine - INFO - Epoch(train) [34][ 360/1879] lr: 2.0000e-02 eta: 12:54:53 time: 0.3144 data_time: 0.0150 memory: 6717 grad_norm: 2.8177 loss: 1.7374 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7374 2023/04/14 02:56:17 - mmengine - INFO - Epoch(train) [34][ 380/1879] lr: 2.0000e-02 eta: 12:54:48 time: 0.4223 data_time: 0.0143 memory: 6717 grad_norm: 2.7190 loss: 1.7859 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7859 2023/04/14 02:56:23 - mmengine - INFO - Epoch(train) [34][ 400/1879] lr: 2.0000e-02 eta: 12:54:38 time: 0.3113 data_time: 0.0143 memory: 6717 grad_norm: 2.7489 loss: 1.6868 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6868 2023/04/14 02:56:31 - mmengine - INFO - Epoch(train) [34][ 420/1879] lr: 2.0000e-02 eta: 12:54:32 time: 0.4167 data_time: 0.0138 memory: 6717 grad_norm: 2.6731 loss: 1.6499 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6499 2023/04/14 02:56:37 - mmengine - INFO - Epoch(train) [34][ 440/1879] lr: 2.0000e-02 eta: 12:54:22 time: 0.3097 data_time: 0.0167 memory: 6717 grad_norm: 2.6846 loss: 1.8108 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8108 2023/04/14 02:56:46 - mmengine - INFO - Epoch(train) [34][ 460/1879] lr: 2.0000e-02 eta: 12:54:18 time: 0.4341 data_time: 0.0148 memory: 6717 grad_norm: 2.7391 loss: 1.6204 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6204 2023/04/14 02:56:53 - mmengine - INFO - Epoch(train) [34][ 480/1879] lr: 2.0000e-02 eta: 12:54:09 time: 0.3478 data_time: 0.0124 memory: 6717 grad_norm: 2.6940 loss: 1.7721 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7721 2023/04/14 02:57:01 - mmengine - INFO - Epoch(train) [34][ 500/1879] lr: 2.0000e-02 eta: 12:54:03 time: 0.4020 data_time: 0.0134 memory: 6717 grad_norm: 2.6612 loss: 1.5780 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5780 2023/04/14 02:57:08 - mmengine - INFO - Epoch(train) [34][ 520/1879] lr: 2.0000e-02 eta: 12:53:54 time: 0.3317 data_time: 0.0153 memory: 6717 grad_norm: 2.7215 loss: 1.6780 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6780 2023/04/14 02:57:16 - mmengine - INFO - Epoch(train) [34][ 540/1879] lr: 2.0000e-02 eta: 12:53:49 time: 0.4215 data_time: 0.0140 memory: 6717 grad_norm: 2.7444 loss: 1.7937 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.7937 2023/04/14 02:57:23 - mmengine - INFO - Epoch(train) [34][ 560/1879] lr: 2.0000e-02 eta: 12:53:40 time: 0.3340 data_time: 0.0131 memory: 6717 grad_norm: 2.6972 loss: 1.5276 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5276 2023/04/14 02:57:31 - mmengine - INFO - Epoch(train) [34][ 580/1879] lr: 2.0000e-02 eta: 12:53:34 time: 0.3977 data_time: 0.0139 memory: 6717 grad_norm: 2.8113 loss: 1.4308 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4308 2023/04/14 02:57:38 - mmengine - INFO - Epoch(train) [34][ 600/1879] lr: 2.0000e-02 eta: 12:53:26 time: 0.3536 data_time: 0.0160 memory: 6717 grad_norm: 2.7544 loss: 1.6164 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6164 2023/04/14 02:57:46 - mmengine - INFO - Epoch(train) [34][ 620/1879] lr: 2.0000e-02 eta: 12:53:19 time: 0.3962 data_time: 0.0133 memory: 6717 grad_norm: 2.7209 loss: 1.5621 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5621 2023/04/14 02:57:52 - mmengine - INFO - Epoch(train) [34][ 640/1879] lr: 2.0000e-02 eta: 12:53:10 time: 0.3343 data_time: 0.0147 memory: 6717 grad_norm: 2.7324 loss: 1.7124 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7124 2023/04/14 02:58:01 - mmengine - INFO - Epoch(train) [34][ 660/1879] lr: 2.0000e-02 eta: 12:53:05 time: 0.4285 data_time: 0.0146 memory: 6717 grad_norm: 2.7433 loss: 1.6854 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6854 2023/04/14 02:58:07 - mmengine - INFO - Epoch(train) [34][ 680/1879] lr: 2.0000e-02 eta: 12:52:55 time: 0.3041 data_time: 0.0140 memory: 6717 grad_norm: 2.6365 loss: 1.5439 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.5439 2023/04/14 02:58:15 - mmengine - INFO - Epoch(train) [34][ 700/1879] lr: 2.0000e-02 eta: 12:52:50 time: 0.4166 data_time: 0.0136 memory: 6717 grad_norm: 2.7012 loss: 1.7648 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.7648 2023/04/14 02:58:22 - mmengine - INFO - Epoch(train) [34][ 720/1879] lr: 2.0000e-02 eta: 12:52:40 time: 0.3171 data_time: 0.0142 memory: 6717 grad_norm: 2.7514 loss: 1.6750 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6750 2023/04/14 02:58:30 - mmengine - INFO - Epoch(train) [34][ 740/1879] lr: 2.0000e-02 eta: 12:52:33 time: 0.3848 data_time: 0.0137 memory: 6717 grad_norm: 2.7225 loss: 1.6798 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6798 2023/04/14 02:58:36 - mmengine - INFO - Epoch(train) [34][ 760/1879] lr: 2.0000e-02 eta: 12:52:24 time: 0.3247 data_time: 0.0148 memory: 6717 grad_norm: 2.6726 loss: 1.6541 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6541 2023/04/14 02:58:45 - mmengine - INFO - Epoch(train) [34][ 780/1879] lr: 2.0000e-02 eta: 12:52:19 time: 0.4401 data_time: 0.0144 memory: 6717 grad_norm: 2.6710 loss: 1.6810 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6810 2023/04/14 02:58:51 - mmengine - INFO - Epoch(train) [34][ 800/1879] lr: 2.0000e-02 eta: 12:52:11 time: 0.3344 data_time: 0.0151 memory: 6717 grad_norm: 2.7145 loss: 1.7546 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7546 2023/04/14 02:58:59 - mmengine - INFO - Epoch(train) [34][ 820/1879] lr: 2.0000e-02 eta: 12:52:03 time: 0.3645 data_time: 0.0189 memory: 6717 grad_norm: 2.6555 loss: 1.4614 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4614 2023/04/14 02:59:06 - mmengine - INFO - Epoch(train) [34][ 840/1879] lr: 2.0000e-02 eta: 12:51:54 time: 0.3356 data_time: 0.1297 memory: 6717 grad_norm: 2.7618 loss: 1.6789 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.6789 2023/04/14 02:59:13 - mmengine - INFO - Epoch(train) [34][ 860/1879] lr: 2.0000e-02 eta: 12:51:47 time: 0.3790 data_time: 0.0831 memory: 6717 grad_norm: 2.7372 loss: 1.6253 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6253 2023/04/14 02:59:20 - mmengine - INFO - Epoch(train) [34][ 880/1879] lr: 2.0000e-02 eta: 12:51:38 time: 0.3399 data_time: 0.0583 memory: 6717 grad_norm: 2.7451 loss: 1.6143 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6143 2023/04/14 02:59:28 - mmengine - INFO - Epoch(train) [34][ 900/1879] lr: 2.0000e-02 eta: 12:51:32 time: 0.4054 data_time: 0.0167 memory: 6717 grad_norm: 2.7053 loss: 1.5932 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.5932 2023/04/14 02:59:34 - mmengine - INFO - Epoch(train) [34][ 920/1879] lr: 2.0000e-02 eta: 12:51:23 time: 0.3248 data_time: 0.0389 memory: 6717 grad_norm: 2.7429 loss: 1.8547 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8547 2023/04/14 02:59:42 - mmengine - INFO - Epoch(train) [34][ 940/1879] lr: 2.0000e-02 eta: 12:51:17 time: 0.3992 data_time: 0.0792 memory: 6717 grad_norm: 2.7656 loss: 1.5427 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.5427 2023/04/14 02:59:50 - mmengine - INFO - Epoch(train) [34][ 960/1879] lr: 2.0000e-02 eta: 12:51:10 time: 0.3862 data_time: 0.1277 memory: 6717 grad_norm: 2.7069 loss: 1.7116 top1_acc: 0.3125 top5_acc: 0.9375 loss_cls: 1.7116 2023/04/14 02:59:57 - mmengine - INFO - Epoch(train) [34][ 980/1879] lr: 2.0000e-02 eta: 12:51:02 time: 0.3626 data_time: 0.1309 memory: 6717 grad_norm: 2.6737 loss: 1.6793 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6793 2023/04/14 03:00:02 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 03:00:05 - mmengine - INFO - Epoch(train) [34][1000/1879] lr: 2.0000e-02 eta: 12:50:56 time: 0.3931 data_time: 0.2076 memory: 6717 grad_norm: 2.6416 loss: 1.8506 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8506 2023/04/14 03:00:12 - mmengine - INFO - Epoch(train) [34][1020/1879] lr: 2.0000e-02 eta: 12:50:48 time: 0.3500 data_time: 0.1056 memory: 6717 grad_norm: 2.7527 loss: 1.7031 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7031 2023/04/14 03:00:21 - mmengine - INFO - Epoch(train) [34][1040/1879] lr: 2.0000e-02 eta: 12:50:43 time: 0.4384 data_time: 0.2879 memory: 6717 grad_norm: 2.6802 loss: 1.8191 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8191 2023/04/14 03:00:27 - mmengine - INFO - Epoch(train) [34][1060/1879] lr: 2.0000e-02 eta: 12:50:33 time: 0.2947 data_time: 0.1499 memory: 6717 grad_norm: 2.7554 loss: 1.8215 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8215 2023/04/14 03:00:35 - mmengine - INFO - Epoch(train) [34][1080/1879] lr: 2.0000e-02 eta: 12:50:26 time: 0.3852 data_time: 0.1972 memory: 6717 grad_norm: 2.7236 loss: 1.8631 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.8631 2023/04/14 03:00:42 - mmengine - INFO - Epoch(train) [34][1100/1879] lr: 2.0000e-02 eta: 12:50:17 time: 0.3415 data_time: 0.0694 memory: 6717 grad_norm: 2.7637 loss: 1.7118 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7118 2023/04/14 03:00:48 - mmengine - INFO - Epoch(train) [34][1120/1879] lr: 2.0000e-02 eta: 12:50:09 time: 0.3456 data_time: 0.0831 memory: 6717 grad_norm: 2.6446 loss: 1.7221 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7221 2023/04/14 03:00:56 - mmengine - INFO - Epoch(train) [34][1140/1879] lr: 2.0000e-02 eta: 12:50:01 time: 0.3675 data_time: 0.0127 memory: 6717 grad_norm: 2.7007 loss: 1.5965 top1_acc: 0.4375 top5_acc: 1.0000 loss_cls: 1.5965 2023/04/14 03:01:04 - mmengine - INFO - Epoch(train) [34][1160/1879] lr: 2.0000e-02 eta: 12:49:55 time: 0.3923 data_time: 0.0167 memory: 6717 grad_norm: 2.7363 loss: 1.7993 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7993 2023/04/14 03:01:11 - mmengine - INFO - Epoch(train) [34][1180/1879] lr: 2.0000e-02 eta: 12:49:48 time: 0.3854 data_time: 0.0133 memory: 6717 grad_norm: 2.7035 loss: 1.6628 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6628 2023/04/14 03:01:18 - mmengine - INFO - Epoch(train) [34][1200/1879] lr: 2.0000e-02 eta: 12:49:39 time: 0.3329 data_time: 0.0152 memory: 6717 grad_norm: 2.7242 loss: 1.8141 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.8141 2023/04/14 03:01:26 - mmengine - INFO - Epoch(train) [34][1220/1879] lr: 2.0000e-02 eta: 12:49:33 time: 0.4066 data_time: 0.0144 memory: 6717 grad_norm: 2.7671 loss: 1.5845 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 1.5845 2023/04/14 03:01:33 - mmengine - INFO - Epoch(train) [34][1240/1879] lr: 2.0000e-02 eta: 12:49:24 time: 0.3191 data_time: 0.0134 memory: 6717 grad_norm: 2.7250 loss: 1.7320 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7320 2023/04/14 03:01:41 - mmengine - INFO - Epoch(train) [34][1260/1879] lr: 2.0000e-02 eta: 12:49:18 time: 0.4153 data_time: 0.0150 memory: 6717 grad_norm: 2.7204 loss: 1.5135 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.5135 2023/04/14 03:01:47 - mmengine - INFO - Epoch(train) [34][1280/1879] lr: 2.0000e-02 eta: 12:49:09 time: 0.3207 data_time: 0.0134 memory: 6717 grad_norm: 2.7560 loss: 1.6044 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6044 2023/04/14 03:01:55 - mmengine - INFO - Epoch(train) [34][1300/1879] lr: 2.0000e-02 eta: 12:49:02 time: 0.3957 data_time: 0.0267 memory: 6717 grad_norm: 2.6750 loss: 1.7722 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7722 2023/04/14 03:02:02 - mmengine - INFO - Epoch(train) [34][1320/1879] lr: 2.0000e-02 eta: 12:48:54 time: 0.3358 data_time: 0.0186 memory: 6717 grad_norm: 2.7311 loss: 1.4142 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4142 2023/04/14 03:02:10 - mmengine - INFO - Epoch(train) [34][1340/1879] lr: 2.0000e-02 eta: 12:48:48 time: 0.4165 data_time: 0.0145 memory: 6717 grad_norm: 2.7396 loss: 1.5488 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5488 2023/04/14 03:02:16 - mmengine - INFO - Epoch(train) [34][1360/1879] lr: 2.0000e-02 eta: 12:48:38 time: 0.3129 data_time: 0.0139 memory: 6717 grad_norm: 2.6973 loss: 1.6034 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.6034 2023/04/14 03:02:24 - mmengine - INFO - Epoch(train) [34][1380/1879] lr: 2.0000e-02 eta: 12:48:32 time: 0.3980 data_time: 0.0150 memory: 6717 grad_norm: 2.7675 loss: 1.6680 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 1.6680 2023/04/14 03:02:31 - mmengine - INFO - Epoch(train) [34][1400/1879] lr: 2.0000e-02 eta: 12:48:23 time: 0.3405 data_time: 0.0188 memory: 6717 grad_norm: 2.6971 loss: 1.4821 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4821 2023/04/14 03:02:39 - mmengine - INFO - Epoch(train) [34][1420/1879] lr: 2.0000e-02 eta: 12:48:17 time: 0.3844 data_time: 0.0143 memory: 6717 grad_norm: 2.6966 loss: 1.6760 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.6760 2023/04/14 03:02:46 - mmengine - INFO - Epoch(train) [34][1440/1879] lr: 2.0000e-02 eta: 12:48:08 time: 0.3452 data_time: 0.0171 memory: 6717 grad_norm: 2.6573 loss: 1.5879 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.5879 2023/04/14 03:02:54 - mmengine - INFO - Epoch(train) [34][1460/1879] lr: 2.0000e-02 eta: 12:48:03 time: 0.4173 data_time: 0.0149 memory: 6717 grad_norm: 2.7875 loss: 1.6247 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6247 2023/04/14 03:03:01 - mmengine - INFO - Epoch(train) [34][1480/1879] lr: 2.0000e-02 eta: 12:47:54 time: 0.3293 data_time: 0.0135 memory: 6717 grad_norm: 2.8021 loss: 1.5868 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5868 2023/04/14 03:03:10 - mmengine - INFO - Epoch(train) [34][1500/1879] lr: 2.0000e-02 eta: 12:47:49 time: 0.4542 data_time: 0.0236 memory: 6717 grad_norm: 2.6781 loss: 1.6135 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6135 2023/04/14 03:03:16 - mmengine - INFO - Epoch(train) [34][1520/1879] lr: 2.0000e-02 eta: 12:47:40 time: 0.3289 data_time: 0.0126 memory: 6717 grad_norm: 2.6618 loss: 1.8435 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8435 2023/04/14 03:03:24 - mmengine - INFO - Epoch(train) [34][1540/1879] lr: 2.0000e-02 eta: 12:47:34 time: 0.3973 data_time: 0.0145 memory: 6717 grad_norm: 2.7619 loss: 1.5355 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5355 2023/04/14 03:03:31 - mmengine - INFO - Epoch(train) [34][1560/1879] lr: 2.0000e-02 eta: 12:47:24 time: 0.3091 data_time: 0.0136 memory: 6717 grad_norm: 2.7219 loss: 1.6374 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6374 2023/04/14 03:03:39 - mmengine - INFO - Epoch(train) [34][1580/1879] lr: 2.0000e-02 eta: 12:47:19 time: 0.4197 data_time: 0.0152 memory: 6717 grad_norm: 2.7370 loss: 1.8507 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8507 2023/04/14 03:03:46 - mmengine - INFO - Epoch(train) [34][1600/1879] lr: 2.0000e-02 eta: 12:47:10 time: 0.3253 data_time: 0.0127 memory: 6717 grad_norm: 2.8005 loss: 1.7099 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7099 2023/04/14 03:03:53 - mmengine - INFO - Epoch(train) [34][1620/1879] lr: 2.0000e-02 eta: 12:47:03 time: 0.3904 data_time: 0.0160 memory: 6717 grad_norm: 2.7805 loss: 1.5082 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5082 2023/04/14 03:04:00 - mmengine - INFO - Epoch(train) [34][1640/1879] lr: 2.0000e-02 eta: 12:46:54 time: 0.3324 data_time: 0.0136 memory: 6717 grad_norm: 2.6467 loss: 1.6465 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6465 2023/04/14 03:04:08 - mmengine - INFO - Epoch(train) [34][1660/1879] lr: 2.0000e-02 eta: 12:46:48 time: 0.4015 data_time: 0.0373 memory: 6717 grad_norm: 2.7524 loss: 1.6572 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.6572 2023/04/14 03:04:15 - mmengine - INFO - Epoch(train) [34][1680/1879] lr: 2.0000e-02 eta: 12:46:39 time: 0.3398 data_time: 0.0372 memory: 6717 grad_norm: 2.7057 loss: 1.4812 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4812 2023/04/14 03:04:23 - mmengine - INFO - Epoch(train) [34][1700/1879] lr: 2.0000e-02 eta: 12:46:34 time: 0.4189 data_time: 0.0162 memory: 6717 grad_norm: 2.7137 loss: 1.4447 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4447 2023/04/14 03:04:29 - mmengine - INFO - Epoch(train) [34][1720/1879] lr: 2.0000e-02 eta: 12:46:24 time: 0.3098 data_time: 0.0321 memory: 6717 grad_norm: 2.7095 loss: 1.5376 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5376 2023/04/14 03:04:37 - mmengine - INFO - Epoch(train) [34][1740/1879] lr: 2.0000e-02 eta: 12:46:18 time: 0.3925 data_time: 0.1397 memory: 6717 grad_norm: 2.7050 loss: 1.4953 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4953 2023/04/14 03:04:44 - mmengine - INFO - Epoch(train) [34][1760/1879] lr: 2.0000e-02 eta: 12:46:09 time: 0.3493 data_time: 0.1294 memory: 6717 grad_norm: 2.7530 loss: 1.6132 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6132 2023/04/14 03:04:53 - mmengine - INFO - Epoch(train) [34][1780/1879] lr: 2.0000e-02 eta: 12:46:04 time: 0.4245 data_time: 0.1393 memory: 6717 grad_norm: 2.7970 loss: 1.6618 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6618 2023/04/14 03:04:59 - mmengine - INFO - Epoch(train) [34][1800/1879] lr: 2.0000e-02 eta: 12:45:55 time: 0.3388 data_time: 0.1157 memory: 6717 grad_norm: 2.6716 loss: 1.6198 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 1.6198 2023/04/14 03:05:08 - mmengine - INFO - Epoch(train) [34][1820/1879] lr: 2.0000e-02 eta: 12:45:50 time: 0.4200 data_time: 0.1276 memory: 6717 grad_norm: 2.7409 loss: 1.6069 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6069 2023/04/14 03:05:14 - mmengine - INFO - Epoch(train) [34][1840/1879] lr: 2.0000e-02 eta: 12:45:40 time: 0.3095 data_time: 0.0376 memory: 6717 grad_norm: 2.7374 loss: 1.7566 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.7566 2023/04/14 03:05:22 - mmengine - INFO - Epoch(train) [34][1860/1879] lr: 2.0000e-02 eta: 12:45:34 time: 0.4020 data_time: 0.1064 memory: 6717 grad_norm: 2.7586 loss: 1.4718 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.4718 2023/04/14 03:05:29 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 03:05:29 - mmengine - INFO - Epoch(train) [34][1879/1879] lr: 2.0000e-02 eta: 12:45:26 time: 0.3324 data_time: 0.0259 memory: 6717 grad_norm: 2.7358 loss: 1.9739 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.9739 2023/04/14 03:05:38 - mmengine - INFO - Epoch(val) [34][ 20/155] eta: 0:01:02 time: 0.4628 data_time: 0.4294 memory: 1391 2023/04/14 03:05:44 - mmengine - INFO - Epoch(val) [34][ 40/155] eta: 0:00:44 time: 0.3171 data_time: 0.2835 memory: 1391 2023/04/14 03:05:53 - mmengine - INFO - Epoch(val) [34][ 60/155] eta: 0:00:38 time: 0.4325 data_time: 0.3983 memory: 1391 2023/04/14 03:05:59 - mmengine - INFO - Epoch(val) [34][ 80/155] eta: 0:00:28 time: 0.3138 data_time: 0.2806 memory: 1391 2023/04/14 03:06:08 - mmengine - INFO - Epoch(val) [34][100/155] eta: 0:00:21 time: 0.4581 data_time: 0.4248 memory: 1391 2023/04/14 03:06:14 - mmengine - INFO - Epoch(val) [34][120/155] eta: 0:00:13 time: 0.2971 data_time: 0.2638 memory: 1391 2023/04/14 03:06:23 - mmengine - INFO - Epoch(val) [34][140/155] eta: 0:00:05 time: 0.4433 data_time: 0.4099 memory: 1391 2023/04/14 03:06:30 - mmengine - INFO - Epoch(val) [34][155/155] acc/top1: 0.6105 acc/top5: 0.8386 acc/mean1: 0.6106 data_time: 0.3670 time: 0.3996 2023/04/14 03:06:30 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_33.pth is removed 2023/04/14 03:06:31 - mmengine - INFO - The best checkpoint with 0.6105 acc/top1 at 34 epoch is saved to best_acc_top1_epoch_34.pth. 2023/04/14 03:06:40 - mmengine - INFO - Epoch(train) [35][ 20/1879] lr: 2.0000e-02 eta: 12:45:23 time: 0.4851 data_time: 0.3307 memory: 6717 grad_norm: 2.6718 loss: 1.7352 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7352 2023/04/14 03:06:47 - mmengine - INFO - Epoch(train) [35][ 40/1879] lr: 2.0000e-02 eta: 12:45:15 time: 0.3495 data_time: 0.1173 memory: 6717 grad_norm: 2.7067 loss: 1.7067 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7067 2023/04/14 03:06:55 - mmengine - INFO - Epoch(train) [35][ 60/1879] lr: 2.0000e-02 eta: 12:45:08 time: 0.3986 data_time: 0.0411 memory: 6717 grad_norm: 2.6768 loss: 1.5540 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5540 2023/04/14 03:07:02 - mmengine - INFO - Epoch(train) [35][ 80/1879] lr: 2.0000e-02 eta: 12:44:59 time: 0.3186 data_time: 0.0134 memory: 6717 grad_norm: 2.7213 loss: 1.5176 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5176 2023/04/14 03:07:09 - mmengine - INFO - Epoch(train) [35][ 100/1879] lr: 2.0000e-02 eta: 12:44:52 time: 0.3867 data_time: 0.0168 memory: 6717 grad_norm: 2.6931 loss: 1.4943 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4943 2023/04/14 03:07:15 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 03:07:17 - mmengine - INFO - Epoch(train) [35][ 120/1879] lr: 2.0000e-02 eta: 12:44:45 time: 0.3738 data_time: 0.0211 memory: 6717 grad_norm: 2.7549 loss: 1.6531 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6531 2023/04/14 03:07:25 - mmengine - INFO - Epoch(train) [35][ 140/1879] lr: 2.0000e-02 eta: 12:44:38 time: 0.3936 data_time: 0.0157 memory: 6717 grad_norm: 2.7768 loss: 1.6589 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6589 2023/04/14 03:07:32 - mmengine - INFO - Epoch(train) [35][ 160/1879] lr: 2.0000e-02 eta: 12:44:30 time: 0.3537 data_time: 0.0130 memory: 6717 grad_norm: 2.7332 loss: 1.4614 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4614 2023/04/14 03:07:40 - mmengine - INFO - Epoch(train) [35][ 180/1879] lr: 2.0000e-02 eta: 12:44:24 time: 0.3901 data_time: 0.0159 memory: 6717 grad_norm: 2.7054 loss: 1.6219 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6219 2023/04/14 03:07:46 - mmengine - INFO - Epoch(train) [35][ 200/1879] lr: 2.0000e-02 eta: 12:44:14 time: 0.3137 data_time: 0.0138 memory: 6717 grad_norm: 2.6916 loss: 1.7500 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.7500 2023/04/14 03:07:55 - mmengine - INFO - Epoch(train) [35][ 220/1879] lr: 2.0000e-02 eta: 12:44:10 time: 0.4575 data_time: 0.0150 memory: 6717 grad_norm: 2.7686 loss: 1.5499 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5499 2023/04/14 03:08:02 - mmengine - INFO - Epoch(train) [35][ 240/1879] lr: 2.0000e-02 eta: 12:44:01 time: 0.3332 data_time: 0.0132 memory: 6717 grad_norm: 2.7500 loss: 1.5719 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.5719 2023/04/14 03:08:10 - mmengine - INFO - Epoch(train) [35][ 260/1879] lr: 2.0000e-02 eta: 12:43:55 time: 0.3978 data_time: 0.0144 memory: 6717 grad_norm: 2.7282 loss: 1.5531 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.5531 2023/04/14 03:08:16 - mmengine - INFO - Epoch(train) [35][ 280/1879] lr: 2.0000e-02 eta: 12:43:45 time: 0.3056 data_time: 0.0160 memory: 6717 grad_norm: 2.6959 loss: 1.6735 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.6735 2023/04/14 03:08:24 - mmengine - INFO - Epoch(train) [35][ 300/1879] lr: 2.0000e-02 eta: 12:43:39 time: 0.3974 data_time: 0.0138 memory: 6717 grad_norm: 2.7139 loss: 1.6579 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.6579 2023/04/14 03:08:30 - mmengine - INFO - Epoch(train) [35][ 320/1879] lr: 2.0000e-02 eta: 12:43:30 time: 0.3300 data_time: 0.0134 memory: 6717 grad_norm: 2.8106 loss: 1.7131 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.7131 2023/04/14 03:08:39 - mmengine - INFO - Epoch(train) [35][ 340/1879] lr: 2.0000e-02 eta: 12:43:24 time: 0.4208 data_time: 0.0143 memory: 6717 grad_norm: 2.7564 loss: 1.8012 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.8012 2023/04/14 03:08:45 - mmengine - INFO - Epoch(train) [35][ 360/1879] lr: 2.0000e-02 eta: 12:43:15 time: 0.3192 data_time: 0.0143 memory: 6717 grad_norm: 2.7073 loss: 1.6648 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6648 2023/04/14 03:08:53 - mmengine - INFO - Epoch(train) [35][ 380/1879] lr: 2.0000e-02 eta: 12:43:09 time: 0.4094 data_time: 0.0150 memory: 6717 grad_norm: 2.8019 loss: 1.7161 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7161 2023/04/14 03:09:01 - mmengine - INFO - Epoch(train) [35][ 400/1879] lr: 2.0000e-02 eta: 12:43:01 time: 0.3606 data_time: 0.0131 memory: 6717 grad_norm: 2.7302 loss: 1.7733 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7733 2023/04/14 03:09:09 - mmengine - INFO - Epoch(train) [35][ 420/1879] lr: 2.0000e-02 eta: 12:42:56 time: 0.4214 data_time: 0.0154 memory: 6717 grad_norm: 2.6747 loss: 1.8661 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8661 2023/04/14 03:09:16 - mmengine - INFO - Epoch(train) [35][ 440/1879] lr: 2.0000e-02 eta: 12:42:47 time: 0.3364 data_time: 0.0157 memory: 6717 grad_norm: 2.7829 loss: 1.5195 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5195 2023/04/14 03:09:23 - mmengine - INFO - Epoch(train) [35][ 460/1879] lr: 2.0000e-02 eta: 12:42:40 time: 0.3824 data_time: 0.0141 memory: 6717 grad_norm: 2.7530 loss: 1.5303 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5303 2023/04/14 03:09:30 - mmengine - INFO - Epoch(train) [35][ 480/1879] lr: 2.0000e-02 eta: 12:42:31 time: 0.3345 data_time: 0.0151 memory: 6717 grad_norm: 2.7469 loss: 1.7514 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7514 2023/04/14 03:09:38 - mmengine - INFO - Epoch(train) [35][ 500/1879] lr: 2.0000e-02 eta: 12:42:25 time: 0.3966 data_time: 0.0149 memory: 6717 grad_norm: 2.6990 loss: 1.8015 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8015 2023/04/14 03:09:44 - mmengine - INFO - Epoch(train) [35][ 520/1879] lr: 2.0000e-02 eta: 12:42:15 time: 0.3159 data_time: 0.0142 memory: 6717 grad_norm: 2.7751 loss: 1.6799 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.6799 2023/04/14 03:09:53 - mmengine - INFO - Epoch(train) [35][ 540/1879] lr: 2.0000e-02 eta: 12:42:09 time: 0.4099 data_time: 0.0138 memory: 6717 grad_norm: 2.7294 loss: 1.7258 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7258 2023/04/14 03:09:59 - mmengine - INFO - Epoch(train) [35][ 560/1879] lr: 2.0000e-02 eta: 12:42:00 time: 0.3111 data_time: 0.0149 memory: 6717 grad_norm: 2.7652 loss: 1.6824 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6824 2023/04/14 03:10:07 - mmengine - INFO - Epoch(train) [35][ 580/1879] lr: 2.0000e-02 eta: 12:41:54 time: 0.4145 data_time: 0.0145 memory: 6717 grad_norm: 2.7814 loss: 1.6982 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.6982 2023/04/14 03:10:13 - mmengine - INFO - Epoch(train) [35][ 600/1879] lr: 2.0000e-02 eta: 12:41:44 time: 0.2979 data_time: 0.0150 memory: 6717 grad_norm: 2.7550 loss: 1.5411 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5411 2023/04/14 03:10:22 - mmengine - INFO - Epoch(train) [35][ 620/1879] lr: 2.0000e-02 eta: 12:41:39 time: 0.4386 data_time: 0.0152 memory: 6717 grad_norm: 2.7942 loss: 1.6339 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6339 2023/04/14 03:10:28 - mmengine - INFO - Epoch(train) [35][ 640/1879] lr: 2.0000e-02 eta: 12:41:30 time: 0.3174 data_time: 0.0136 memory: 6717 grad_norm: 2.8272 loss: 1.7717 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7717 2023/04/14 03:10:37 - mmengine - INFO - Epoch(train) [35][ 660/1879] lr: 2.0000e-02 eta: 12:41:24 time: 0.4217 data_time: 0.0163 memory: 6717 grad_norm: 2.7165 loss: 1.5739 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5739 2023/04/14 03:10:43 - mmengine - INFO - Epoch(train) [35][ 680/1879] lr: 2.0000e-02 eta: 12:41:15 time: 0.3214 data_time: 0.0134 memory: 6717 grad_norm: 2.7492 loss: 1.5914 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5914 2023/04/14 03:10:51 - mmengine - INFO - Epoch(train) [35][ 700/1879] lr: 2.0000e-02 eta: 12:41:08 time: 0.3917 data_time: 0.0292 memory: 6717 grad_norm: 2.6918 loss: 1.7199 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7199 2023/04/14 03:10:58 - mmengine - INFO - Epoch(train) [35][ 720/1879] lr: 2.0000e-02 eta: 12:41:00 time: 0.3471 data_time: 0.0355 memory: 6717 grad_norm: 2.7104 loss: 1.5674 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5674 2023/04/14 03:11:05 - mmengine - INFO - Epoch(train) [35][ 740/1879] lr: 2.0000e-02 eta: 12:40:52 time: 0.3585 data_time: 0.1286 memory: 6717 grad_norm: 2.7894 loss: 1.6781 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6781 2023/04/14 03:11:12 - mmengine - INFO - Epoch(train) [35][ 760/1879] lr: 2.0000e-02 eta: 12:40:44 time: 0.3446 data_time: 0.0808 memory: 6717 grad_norm: 2.7406 loss: 1.6805 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.6805 2023/04/14 03:11:20 - mmengine - INFO - Epoch(train) [35][ 780/1879] lr: 2.0000e-02 eta: 12:40:38 time: 0.3992 data_time: 0.0945 memory: 6717 grad_norm: 2.6918 loss: 1.6934 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6934 2023/04/14 03:11:27 - mmengine - INFO - Epoch(train) [35][ 800/1879] lr: 2.0000e-02 eta: 12:40:29 time: 0.3315 data_time: 0.0670 memory: 6717 grad_norm: 2.6509 loss: 1.7147 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7147 2023/04/14 03:11:35 - mmengine - INFO - Epoch(train) [35][ 820/1879] lr: 2.0000e-02 eta: 12:40:23 time: 0.4149 data_time: 0.1161 memory: 6717 grad_norm: 2.7403 loss: 1.6854 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6854 2023/04/14 03:11:42 - mmengine - INFO - Epoch(train) [35][ 840/1879] lr: 2.0000e-02 eta: 12:40:15 time: 0.3543 data_time: 0.0234 memory: 6717 grad_norm: 2.6674 loss: 1.6986 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6986 2023/04/14 03:11:50 - mmengine - INFO - Epoch(train) [35][ 860/1879] lr: 2.0000e-02 eta: 12:40:09 time: 0.4098 data_time: 0.0381 memory: 6717 grad_norm: 2.6899 loss: 1.6767 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.6767 2023/04/14 03:11:57 - mmengine - INFO - Epoch(train) [35][ 880/1879] lr: 2.0000e-02 eta: 12:40:00 time: 0.3289 data_time: 0.0271 memory: 6717 grad_norm: 2.8220 loss: 1.5580 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.5580 2023/04/14 03:12:05 - mmengine - INFO - Epoch(train) [35][ 900/1879] lr: 2.0000e-02 eta: 12:39:54 time: 0.4041 data_time: 0.0155 memory: 6717 grad_norm: 2.7242 loss: 1.6657 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6657 2023/04/14 03:12:12 - mmengine - INFO - Epoch(train) [35][ 920/1879] lr: 2.0000e-02 eta: 12:39:45 time: 0.3364 data_time: 0.0133 memory: 6717 grad_norm: 2.7770 loss: 1.7494 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7494 2023/04/14 03:12:20 - mmengine - INFO - Epoch(train) [35][ 940/1879] lr: 2.0000e-02 eta: 12:39:40 time: 0.4176 data_time: 0.0153 memory: 6717 grad_norm: 2.7565 loss: 1.5841 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5841 2023/04/14 03:12:26 - mmengine - INFO - Epoch(train) [35][ 960/1879] lr: 2.0000e-02 eta: 12:39:30 time: 0.3265 data_time: 0.0134 memory: 6717 grad_norm: 2.7414 loss: 1.7091 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.7091 2023/04/14 03:12:34 - mmengine - INFO - Epoch(train) [35][ 980/1879] lr: 2.0000e-02 eta: 12:39:24 time: 0.3929 data_time: 0.0142 memory: 6717 grad_norm: 2.6868 loss: 1.4708 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4708 2023/04/14 03:12:41 - mmengine - INFO - Epoch(train) [35][1000/1879] lr: 2.0000e-02 eta: 12:39:14 time: 0.3157 data_time: 0.0148 memory: 6717 grad_norm: 2.7115 loss: 1.7076 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7076 2023/04/14 03:12:49 - mmengine - INFO - Epoch(train) [35][1020/1879] lr: 2.0000e-02 eta: 12:39:09 time: 0.4181 data_time: 0.0158 memory: 6717 grad_norm: 2.7265 loss: 1.8124 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8124 2023/04/14 03:12:56 - mmengine - INFO - Epoch(train) [35][1040/1879] lr: 2.0000e-02 eta: 12:39:00 time: 0.3370 data_time: 0.0146 memory: 6717 grad_norm: 2.6927 loss: 1.4784 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4784 2023/04/14 03:13:04 - mmengine - INFO - Epoch(train) [35][1060/1879] lr: 2.0000e-02 eta: 12:38:54 time: 0.4128 data_time: 0.0145 memory: 6717 grad_norm: 2.7037 loss: 1.7384 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.7384 2023/04/14 03:13:11 - mmengine - INFO - Epoch(train) [35][1080/1879] lr: 2.0000e-02 eta: 12:38:46 time: 0.3380 data_time: 0.0134 memory: 6717 grad_norm: 2.7378 loss: 1.7043 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7043 2023/04/14 03:13:18 - mmengine - INFO - Epoch(train) [35][1100/1879] lr: 2.0000e-02 eta: 12:38:38 time: 0.3669 data_time: 0.0142 memory: 6717 grad_norm: 2.6626 loss: 1.6295 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6295 2023/04/14 03:13:23 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 03:13:26 - mmengine - INFO - Epoch(train) [35][1120/1879] lr: 2.0000e-02 eta: 12:38:31 time: 0.3802 data_time: 0.0139 memory: 6717 grad_norm: 2.7372 loss: 1.6153 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6153 2023/04/14 03:13:33 - mmengine - INFO - Epoch(train) [35][1140/1879] lr: 2.0000e-02 eta: 12:38:23 time: 0.3526 data_time: 0.0144 memory: 6717 grad_norm: 2.7111 loss: 1.6016 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6016 2023/04/14 03:13:40 - mmengine - INFO - Epoch(train) [35][1160/1879] lr: 2.0000e-02 eta: 12:38:15 time: 0.3448 data_time: 0.0141 memory: 6717 grad_norm: 2.8461 loss: 1.5822 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5822 2023/04/14 03:13:48 - mmengine - INFO - Epoch(train) [35][1180/1879] lr: 2.0000e-02 eta: 12:38:09 time: 0.4090 data_time: 0.0145 memory: 6717 grad_norm: 2.6414 loss: 1.6419 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.6419 2023/04/14 03:13:54 - mmengine - INFO - Epoch(train) [35][1200/1879] lr: 2.0000e-02 eta: 12:37:58 time: 0.2905 data_time: 0.0137 memory: 6717 grad_norm: 2.6945 loss: 1.6934 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6934 2023/04/14 03:14:02 - mmengine - INFO - Epoch(train) [35][1220/1879] lr: 2.0000e-02 eta: 12:37:52 time: 0.4030 data_time: 0.0156 memory: 6717 grad_norm: 2.6407 loss: 1.7578 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7578 2023/04/14 03:14:09 - mmengine - INFO - Epoch(train) [35][1240/1879] lr: 2.0000e-02 eta: 12:37:44 time: 0.3616 data_time: 0.0128 memory: 6717 grad_norm: 2.7387 loss: 1.5251 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5251 2023/04/14 03:14:17 - mmengine - INFO - Epoch(train) [35][1260/1879] lr: 2.0000e-02 eta: 12:37:39 time: 0.4124 data_time: 0.0157 memory: 6717 grad_norm: 2.6688 loss: 1.5203 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5203 2023/04/14 03:14:25 - mmengine - INFO - Epoch(train) [35][1280/1879] lr: 2.0000e-02 eta: 12:37:32 time: 0.3806 data_time: 0.0157 memory: 6717 grad_norm: 2.7061 loss: 1.6082 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6082 2023/04/14 03:14:31 - mmengine - INFO - Epoch(train) [35][1300/1879] lr: 2.0000e-02 eta: 12:37:23 time: 0.3303 data_time: 0.0264 memory: 6717 grad_norm: 2.6575 loss: 1.7500 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7500 2023/04/14 03:14:39 - mmengine - INFO - Epoch(train) [35][1320/1879] lr: 2.0000e-02 eta: 12:37:16 time: 0.3908 data_time: 0.0786 memory: 6717 grad_norm: 2.7428 loss: 1.4462 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4462 2023/04/14 03:14:47 - mmengine - INFO - Epoch(train) [35][1340/1879] lr: 2.0000e-02 eta: 12:37:10 time: 0.4030 data_time: 0.1961 memory: 6717 grad_norm: 2.6932 loss: 1.6097 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6097 2023/04/14 03:14:54 - mmengine - INFO - Epoch(train) [35][1360/1879] lr: 2.0000e-02 eta: 12:37:01 time: 0.3271 data_time: 0.1662 memory: 6717 grad_norm: 2.7475 loss: 1.7215 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.7215 2023/04/14 03:15:01 - mmengine - INFO - Epoch(train) [35][1380/1879] lr: 2.0000e-02 eta: 12:36:53 time: 0.3697 data_time: 0.1577 memory: 6717 grad_norm: 2.6952 loss: 1.6291 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.6291 2023/04/14 03:15:09 - mmengine - INFO - Epoch(train) [35][1400/1879] lr: 2.0000e-02 eta: 12:36:47 time: 0.3907 data_time: 0.0717 memory: 6717 grad_norm: 2.6854 loss: 1.4214 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4214 2023/04/14 03:15:16 - mmengine - INFO - Epoch(train) [35][1420/1879] lr: 2.0000e-02 eta: 12:36:39 time: 0.3555 data_time: 0.0387 memory: 6717 grad_norm: 2.7179 loss: 1.6366 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6366 2023/04/14 03:15:24 - mmengine - INFO - Epoch(train) [35][1440/1879] lr: 2.0000e-02 eta: 12:36:32 time: 0.3873 data_time: 0.0134 memory: 6717 grad_norm: 2.7358 loss: 1.7328 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7328 2023/04/14 03:15:31 - mmengine - INFO - Epoch(train) [35][1460/1879] lr: 2.0000e-02 eta: 12:36:24 time: 0.3392 data_time: 0.0360 memory: 6717 grad_norm: 2.7005 loss: 1.6403 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6403 2023/04/14 03:15:38 - mmengine - INFO - Epoch(train) [35][1480/1879] lr: 2.0000e-02 eta: 12:36:15 time: 0.3428 data_time: 0.0427 memory: 6717 grad_norm: 2.7046 loss: 1.5895 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5895 2023/04/14 03:15:44 - mmengine - INFO - Epoch(train) [35][1500/1879] lr: 2.0000e-02 eta: 12:36:06 time: 0.3380 data_time: 0.0376 memory: 6717 grad_norm: 2.6583 loss: 1.5796 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.5796 2023/04/14 03:15:54 - mmengine - INFO - Epoch(train) [35][1520/1879] lr: 2.0000e-02 eta: 12:36:03 time: 0.4667 data_time: 0.0157 memory: 6717 grad_norm: 2.7714 loss: 1.7182 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7182 2023/04/14 03:16:00 - mmengine - INFO - Epoch(train) [35][1540/1879] lr: 2.0000e-02 eta: 12:35:53 time: 0.3083 data_time: 0.0151 memory: 6717 grad_norm: 2.8181 loss: 1.5627 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5627 2023/04/14 03:16:07 - mmengine - INFO - Epoch(train) [35][1560/1879] lr: 2.0000e-02 eta: 12:35:46 time: 0.3708 data_time: 0.0185 memory: 6717 grad_norm: 2.7004 loss: 1.5403 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.5403 2023/04/14 03:16:14 - mmengine - INFO - Epoch(train) [35][1580/1879] lr: 2.0000e-02 eta: 12:35:37 time: 0.3396 data_time: 0.0776 memory: 6717 grad_norm: 2.7236 loss: 1.6128 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6128 2023/04/14 03:16:23 - mmengine - INFO - Epoch(train) [35][1600/1879] lr: 2.0000e-02 eta: 12:35:32 time: 0.4303 data_time: 0.0402 memory: 6717 grad_norm: 2.7070 loss: 1.5315 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5315 2023/04/14 03:16:30 - mmengine - INFO - Epoch(train) [35][1620/1879] lr: 2.0000e-02 eta: 12:35:24 time: 0.3503 data_time: 0.0459 memory: 6717 grad_norm: 2.7511 loss: 1.7712 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7712 2023/04/14 03:16:37 - mmengine - INFO - Epoch(train) [35][1640/1879] lr: 2.0000e-02 eta: 12:35:16 time: 0.3703 data_time: 0.0759 memory: 6717 grad_norm: 2.8107 loss: 1.6368 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6368 2023/04/14 03:16:43 - mmengine - INFO - Epoch(train) [35][1660/1879] lr: 2.0000e-02 eta: 12:35:07 time: 0.3141 data_time: 0.0478 memory: 6717 grad_norm: 2.6851 loss: 1.6237 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6237 2023/04/14 03:16:51 - mmengine - INFO - Epoch(train) [35][1680/1879] lr: 2.0000e-02 eta: 12:34:59 time: 0.3736 data_time: 0.0525 memory: 6717 grad_norm: 2.7569 loss: 1.7940 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7940 2023/04/14 03:16:58 - mmengine - INFO - Epoch(train) [35][1700/1879] lr: 2.0000e-02 eta: 12:34:52 time: 0.3642 data_time: 0.0622 memory: 6717 grad_norm: 2.6452 loss: 1.6579 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6579 2023/04/14 03:17:06 - mmengine - INFO - Epoch(train) [35][1720/1879] lr: 2.0000e-02 eta: 12:34:46 time: 0.4109 data_time: 0.0119 memory: 6717 grad_norm: 2.7590 loss: 1.5773 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5773 2023/04/14 03:17:13 - mmengine - INFO - Epoch(train) [35][1740/1879] lr: 2.0000e-02 eta: 12:34:37 time: 0.3332 data_time: 0.0279 memory: 6717 grad_norm: 2.6890 loss: 1.7268 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7268 2023/04/14 03:17:21 - mmengine - INFO - Epoch(train) [35][1760/1879] lr: 2.0000e-02 eta: 12:34:30 time: 0.3791 data_time: 0.0128 memory: 6717 grad_norm: 2.7454 loss: 1.7566 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7566 2023/04/14 03:17:28 - mmengine - INFO - Epoch(train) [35][1780/1879] lr: 2.0000e-02 eta: 12:34:23 time: 0.3898 data_time: 0.0156 memory: 6717 grad_norm: 2.7255 loss: 1.5486 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.5486 2023/04/14 03:17:35 - mmengine - INFO - Epoch(train) [35][1800/1879] lr: 2.0000e-02 eta: 12:34:15 time: 0.3410 data_time: 0.0120 memory: 6717 grad_norm: 2.6772 loss: 1.5531 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.5531 2023/04/14 03:17:44 - mmengine - INFO - Epoch(train) [35][1820/1879] lr: 2.0000e-02 eta: 12:34:09 time: 0.4179 data_time: 0.0147 memory: 6717 grad_norm: 2.7334 loss: 1.6086 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6086 2023/04/14 03:17:50 - mmengine - INFO - Epoch(train) [35][1840/1879] lr: 2.0000e-02 eta: 12:34:01 time: 0.3434 data_time: 0.0134 memory: 6717 grad_norm: 2.7241 loss: 1.6571 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6571 2023/04/14 03:17:59 - mmengine - INFO - Epoch(train) [35][1860/1879] lr: 2.0000e-02 eta: 12:33:55 time: 0.4181 data_time: 0.0135 memory: 6717 grad_norm: 2.6693 loss: 1.9309 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.9309 2023/04/14 03:18:04 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 03:18:04 - mmengine - INFO - Epoch(train) [35][1879/1879] lr: 2.0000e-02 eta: 12:33:45 time: 0.2850 data_time: 0.0121 memory: 6717 grad_norm: 2.7152 loss: 1.6594 top1_acc: 0.2857 top5_acc: 0.7143 loss_cls: 1.6594 2023/04/14 03:18:15 - mmengine - INFO - Epoch(val) [35][ 20/155] eta: 0:01:10 time: 0.5242 data_time: 0.4912 memory: 1391 2023/04/14 03:18:24 - mmengine - INFO - Epoch(val) [35][ 40/155] eta: 0:00:55 time: 0.4485 data_time: 0.4152 memory: 1391 2023/04/14 03:18:29 - mmengine - INFO - Epoch(val) [35][ 60/155] eta: 0:00:39 time: 0.2843 data_time: 0.2516 memory: 1391 2023/04/14 03:18:38 - mmengine - INFO - Epoch(val) [35][ 80/155] eta: 0:00:31 time: 0.4217 data_time: 0.3889 memory: 1391 2023/04/14 03:18:44 - mmengine - INFO - Epoch(val) [35][100/155] eta: 0:00:21 time: 0.2897 data_time: 0.2567 memory: 1391 2023/04/14 03:18:52 - mmengine - INFO - Epoch(val) [35][120/155] eta: 0:00:13 time: 0.4020 data_time: 0.3695 memory: 1391 2023/04/14 03:18:59 - mmengine - INFO - Epoch(val) [35][140/155] eta: 0:00:05 time: 0.3388 data_time: 0.3052 memory: 1391 2023/04/14 03:19:09 - mmengine - INFO - Epoch(val) [35][155/155] acc/top1: 0.6036 acc/top5: 0.8351 acc/mean1: 0.6033 data_time: 0.3751 time: 0.4079 2023/04/14 03:19:19 - mmengine - INFO - Epoch(train) [36][ 20/1879] lr: 2.0000e-02 eta: 12:33:42 time: 0.4840 data_time: 0.2529 memory: 6717 grad_norm: 2.6935 loss: 1.8605 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.8605 2023/04/14 03:19:26 - mmengine - INFO - Epoch(train) [36][ 40/1879] lr: 2.0000e-02 eta: 12:33:33 time: 0.3229 data_time: 0.0462 memory: 6717 grad_norm: 2.8100 loss: 1.5434 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5434 2023/04/14 03:19:33 - mmengine - INFO - Epoch(train) [36][ 60/1879] lr: 2.0000e-02 eta: 12:33:26 time: 0.3769 data_time: 0.0975 memory: 6717 grad_norm: 2.7472 loss: 1.7424 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7424 2023/04/14 03:19:40 - mmengine - INFO - Epoch(train) [36][ 80/1879] lr: 2.0000e-02 eta: 12:33:17 time: 0.3287 data_time: 0.1137 memory: 6717 grad_norm: 2.7597 loss: 1.6550 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6550 2023/04/14 03:19:48 - mmengine - INFO - Epoch(train) [36][ 100/1879] lr: 2.0000e-02 eta: 12:33:12 time: 0.4271 data_time: 0.1064 memory: 6717 grad_norm: 2.6749 loss: 1.6736 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6736 2023/04/14 03:19:55 - mmengine - INFO - Epoch(train) [36][ 120/1879] lr: 2.0000e-02 eta: 12:33:04 time: 0.3521 data_time: 0.0922 memory: 6717 grad_norm: 2.6963 loss: 1.6217 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6217 2023/04/14 03:20:04 - mmengine - INFO - Epoch(train) [36][ 140/1879] lr: 2.0000e-02 eta: 12:32:59 time: 0.4348 data_time: 0.1879 memory: 6717 grad_norm: 2.7075 loss: 1.6839 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6839 2023/04/14 03:20:11 - mmengine - INFO - Epoch(train) [36][ 160/1879] lr: 2.0000e-02 eta: 12:32:50 time: 0.3415 data_time: 0.1872 memory: 6717 grad_norm: 2.7581 loss: 1.7929 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.7929 2023/04/14 03:20:19 - mmengine - INFO - Epoch(train) [36][ 180/1879] lr: 2.0000e-02 eta: 12:32:44 time: 0.3943 data_time: 0.2569 memory: 6717 grad_norm: 2.7201 loss: 1.6976 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6976 2023/04/14 03:20:24 - mmengine - INFO - Epoch(train) [36][ 200/1879] lr: 2.0000e-02 eta: 12:32:33 time: 0.2883 data_time: 0.1493 memory: 6717 grad_norm: 2.7080 loss: 1.7833 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7833 2023/04/14 03:20:32 - mmengine - INFO - Epoch(train) [36][ 220/1879] lr: 2.0000e-02 eta: 12:32:26 time: 0.3871 data_time: 0.1659 memory: 6717 grad_norm: 2.7373 loss: 1.5395 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5395 2023/04/14 03:20:38 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 03:20:39 - mmengine - INFO - Epoch(train) [36][ 240/1879] lr: 2.0000e-02 eta: 12:32:18 time: 0.3487 data_time: 0.0832 memory: 6717 grad_norm: 2.7418 loss: 1.8276 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8276 2023/04/14 03:20:47 - mmengine - INFO - Epoch(train) [36][ 260/1879] lr: 2.0000e-02 eta: 12:32:12 time: 0.3918 data_time: 0.1946 memory: 6717 grad_norm: 2.6919 loss: 1.8375 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.8375 2023/04/14 03:20:54 - mmengine - INFO - Epoch(train) [36][ 280/1879] lr: 2.0000e-02 eta: 12:32:03 time: 0.3496 data_time: 0.1369 memory: 6717 grad_norm: 2.7513 loss: 1.5869 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5869 2023/04/14 03:21:02 - mmengine - INFO - Epoch(train) [36][ 300/1879] lr: 2.0000e-02 eta: 12:31:57 time: 0.3918 data_time: 0.1791 memory: 6717 grad_norm: 2.6239 loss: 1.5541 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.5541 2023/04/14 03:21:09 - mmengine - INFO - Epoch(train) [36][ 320/1879] lr: 2.0000e-02 eta: 12:31:49 time: 0.3541 data_time: 0.0848 memory: 6717 grad_norm: 2.7523 loss: 1.8777 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.8777 2023/04/14 03:21:16 - mmengine - INFO - Epoch(train) [36][ 340/1879] lr: 2.0000e-02 eta: 12:31:41 time: 0.3588 data_time: 0.0849 memory: 6717 grad_norm: 2.7115 loss: 1.5230 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5230 2023/04/14 03:21:46 - mmengine - INFO - Epoch(train) [36][ 360/1879] lr: 2.0000e-02 eta: 12:32:15 time: 1.5080 data_time: 0.0113 memory: 6717 grad_norm: 2.7003 loss: 1.6515 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6515 2023/04/14 03:21:53 - mmengine - INFO - Epoch(train) [36][ 380/1879] lr: 2.0000e-02 eta: 12:32:07 time: 0.3389 data_time: 0.0146 memory: 6717 grad_norm: 2.7162 loss: 1.5611 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5611 2023/04/14 03:22:02 - mmengine - INFO - Epoch(train) [36][ 400/1879] lr: 2.0000e-02 eta: 12:32:02 time: 0.4471 data_time: 0.0141 memory: 6717 grad_norm: 2.7126 loss: 1.6632 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.6632 2023/04/14 03:22:08 - mmengine - INFO - Epoch(train) [36][ 420/1879] lr: 2.0000e-02 eta: 12:31:52 time: 0.2960 data_time: 0.0133 memory: 6717 grad_norm: 2.6610 loss: 1.7585 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7585 2023/04/14 03:22:17 - mmengine - INFO - Epoch(train) [36][ 440/1879] lr: 2.0000e-02 eta: 12:31:47 time: 0.4281 data_time: 0.0162 memory: 6717 grad_norm: 2.6203 loss: 1.6179 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.6179 2023/04/14 03:22:23 - mmengine - INFO - Epoch(train) [36][ 460/1879] lr: 2.0000e-02 eta: 12:31:37 time: 0.3122 data_time: 0.0134 memory: 6717 grad_norm: 2.7247 loss: 1.7369 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7369 2023/04/14 03:22:31 - mmengine - INFO - Epoch(train) [36][ 480/1879] lr: 2.0000e-02 eta: 12:31:31 time: 0.4069 data_time: 0.0148 memory: 6717 grad_norm: 2.6957 loss: 1.5911 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5911 2023/04/14 03:22:37 - mmengine - INFO - Epoch(train) [36][ 500/1879] lr: 2.0000e-02 eta: 12:31:21 time: 0.2999 data_time: 0.0130 memory: 6717 grad_norm: 2.6522 loss: 1.7195 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7195 2023/04/14 03:22:45 - mmengine - INFO - Epoch(train) [36][ 520/1879] lr: 2.0000e-02 eta: 12:31:15 time: 0.4064 data_time: 0.0143 memory: 6717 grad_norm: 2.8290 loss: 1.5471 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5471 2023/04/14 03:22:52 - mmengine - INFO - Epoch(train) [36][ 540/1879] lr: 2.0000e-02 eta: 12:31:06 time: 0.3280 data_time: 0.0137 memory: 6717 grad_norm: 2.7889 loss: 1.7401 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7401 2023/04/14 03:22:59 - mmengine - INFO - Epoch(train) [36][ 560/1879] lr: 2.0000e-02 eta: 12:30:59 time: 0.3808 data_time: 0.0152 memory: 6717 grad_norm: 2.6380 loss: 1.4563 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4563 2023/04/14 03:23:07 - mmengine - INFO - Epoch(train) [36][ 580/1879] lr: 2.0000e-02 eta: 12:30:51 time: 0.3671 data_time: 0.0136 memory: 6717 grad_norm: 2.7253 loss: 1.4799 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4799 2023/04/14 03:23:14 - mmengine - INFO - Epoch(train) [36][ 600/1879] lr: 2.0000e-02 eta: 12:30:44 time: 0.3706 data_time: 0.0148 memory: 6717 grad_norm: 2.7639 loss: 1.5918 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5918 2023/04/14 03:23:21 - mmengine - INFO - Epoch(train) [36][ 620/1879] lr: 2.0000e-02 eta: 12:30:36 time: 0.3632 data_time: 0.0138 memory: 6717 grad_norm: 2.7685 loss: 1.6019 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6019 2023/04/14 03:23:28 - mmengine - INFO - Epoch(train) [36][ 640/1879] lr: 2.0000e-02 eta: 12:30:28 time: 0.3531 data_time: 0.0331 memory: 6717 grad_norm: 2.6678 loss: 1.5431 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5431 2023/04/14 03:23:35 - mmengine - INFO - Epoch(train) [36][ 660/1879] lr: 2.0000e-02 eta: 12:30:20 time: 0.3471 data_time: 0.0305 memory: 6717 grad_norm: 2.7480 loss: 1.6095 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6095 2023/04/14 03:23:44 - mmengine - INFO - Epoch(train) [36][ 680/1879] lr: 2.0000e-02 eta: 12:30:15 time: 0.4334 data_time: 0.0428 memory: 6717 grad_norm: 2.6731 loss: 1.4606 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4606 2023/04/14 03:23:50 - mmengine - INFO - Epoch(train) [36][ 700/1879] lr: 2.0000e-02 eta: 12:30:05 time: 0.3090 data_time: 0.0556 memory: 6717 grad_norm: 2.7665 loss: 1.5493 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5493 2023/04/14 03:23:59 - mmengine - INFO - Epoch(train) [36][ 720/1879] lr: 2.0000e-02 eta: 12:30:00 time: 0.4379 data_time: 0.0152 memory: 6717 grad_norm: 2.8449 loss: 1.5869 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5869 2023/04/14 03:24:06 - mmengine - INFO - Epoch(train) [36][ 740/1879] lr: 2.0000e-02 eta: 12:29:53 time: 0.3696 data_time: 0.0130 memory: 6717 grad_norm: 2.7201 loss: 1.6780 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6780 2023/04/14 03:24:14 - mmengine - INFO - Epoch(train) [36][ 760/1879] lr: 2.0000e-02 eta: 12:29:46 time: 0.3838 data_time: 0.0139 memory: 6717 grad_norm: 2.7577 loss: 1.7883 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7883 2023/04/14 03:24:20 - mmengine - INFO - Epoch(train) [36][ 780/1879] lr: 2.0000e-02 eta: 12:29:35 time: 0.2874 data_time: 0.0138 memory: 6717 grad_norm: 2.7293 loss: 1.5312 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.5312 2023/04/14 03:24:28 - mmengine - INFO - Epoch(train) [36][ 800/1879] lr: 2.0000e-02 eta: 12:29:29 time: 0.3961 data_time: 0.0331 memory: 6717 grad_norm: 2.7408 loss: 1.6386 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6386 2023/04/14 03:24:34 - mmengine - INFO - Epoch(train) [36][ 820/1879] lr: 2.0000e-02 eta: 12:29:20 time: 0.3366 data_time: 0.0229 memory: 6717 grad_norm: 2.7310 loss: 1.7282 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7282 2023/04/14 03:24:43 - mmengine - INFO - Epoch(train) [36][ 840/1879] lr: 2.0000e-02 eta: 12:29:14 time: 0.4117 data_time: 0.0178 memory: 6717 grad_norm: 2.7343 loss: 1.5731 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5731 2023/04/14 03:24:50 - mmengine - INFO - Epoch(train) [36][ 860/1879] lr: 2.0000e-02 eta: 12:29:06 time: 0.3466 data_time: 0.0131 memory: 6717 grad_norm: 2.7332 loss: 1.4147 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4147 2023/04/14 03:24:57 - mmengine - INFO - Epoch(train) [36][ 880/1879] lr: 2.0000e-02 eta: 12:28:59 time: 0.3878 data_time: 0.0154 memory: 6717 grad_norm: 2.6716 loss: 1.6003 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6003 2023/04/14 03:25:03 - mmengine - INFO - Epoch(train) [36][ 900/1879] lr: 2.0000e-02 eta: 12:28:50 time: 0.3114 data_time: 0.0128 memory: 6717 grad_norm: 2.6697 loss: 1.5858 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.5858 2023/04/14 03:25:12 - mmengine - INFO - Epoch(train) [36][ 920/1879] lr: 2.0000e-02 eta: 12:28:44 time: 0.4093 data_time: 0.0409 memory: 6717 grad_norm: 2.6879 loss: 1.6518 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6518 2023/04/14 03:25:18 - mmengine - INFO - Epoch(train) [36][ 940/1879] lr: 2.0000e-02 eta: 12:28:35 time: 0.3197 data_time: 0.0732 memory: 6717 grad_norm: 2.7035 loss: 1.5483 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5483 2023/04/14 03:25:26 - mmengine - INFO - Epoch(train) [36][ 960/1879] lr: 2.0000e-02 eta: 12:28:28 time: 0.4049 data_time: 0.0978 memory: 6717 grad_norm: 2.6934 loss: 1.6768 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6768 2023/04/14 03:25:33 - mmengine - INFO - Epoch(train) [36][ 980/1879] lr: 2.0000e-02 eta: 12:28:20 time: 0.3337 data_time: 0.1056 memory: 6717 grad_norm: 2.6926 loss: 1.5946 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5946 2023/04/14 03:25:41 - mmengine - INFO - Epoch(train) [36][1000/1879] lr: 2.0000e-02 eta: 12:28:13 time: 0.3997 data_time: 0.1442 memory: 6717 grad_norm: 2.7253 loss: 1.7405 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7405 2023/04/14 03:25:47 - mmengine - INFO - Epoch(train) [36][1020/1879] lr: 2.0000e-02 eta: 12:28:04 time: 0.3146 data_time: 0.0568 memory: 6717 grad_norm: 2.7536 loss: 1.7362 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.7362 2023/04/14 03:25:56 - mmengine - INFO - Epoch(train) [36][1040/1879] lr: 2.0000e-02 eta: 12:27:58 time: 0.4204 data_time: 0.1024 memory: 6717 grad_norm: 2.7320 loss: 1.6200 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6200 2023/04/14 03:26:02 - mmengine - INFO - Epoch(train) [36][1060/1879] lr: 2.0000e-02 eta: 12:27:49 time: 0.3211 data_time: 0.1163 memory: 6717 grad_norm: 2.7315 loss: 1.7167 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7167 2023/04/14 03:26:10 - mmengine - INFO - Epoch(train) [36][1080/1879] lr: 2.0000e-02 eta: 12:27:44 time: 0.4255 data_time: 0.1249 memory: 6717 grad_norm: 2.7329 loss: 1.6438 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6438 2023/04/14 03:26:17 - mmengine - INFO - Epoch(train) [36][1100/1879] lr: 2.0000e-02 eta: 12:27:34 time: 0.3156 data_time: 0.0702 memory: 6717 grad_norm: 2.7482 loss: 1.7691 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.7691 2023/04/14 03:26:25 - mmengine - INFO - Epoch(train) [36][1120/1879] lr: 2.0000e-02 eta: 12:27:27 time: 0.3876 data_time: 0.0384 memory: 6717 grad_norm: 2.6557 loss: 1.7755 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7755 2023/04/14 03:26:31 - mmengine - INFO - Epoch(train) [36][1140/1879] lr: 2.0000e-02 eta: 12:27:19 time: 0.3312 data_time: 0.0245 memory: 6717 grad_norm: 2.6603 loss: 1.6733 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6733 2023/04/14 03:26:40 - mmengine - INFO - Epoch(train) [36][1160/1879] lr: 2.0000e-02 eta: 12:27:13 time: 0.4233 data_time: 0.0420 memory: 6717 grad_norm: 2.7044 loss: 1.7973 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7973 2023/04/14 03:26:46 - mmengine - INFO - Epoch(train) [36][1180/1879] lr: 2.0000e-02 eta: 12:27:05 time: 0.3421 data_time: 0.0386 memory: 6717 grad_norm: 2.6861 loss: 1.5827 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.5827 2023/04/14 03:26:55 - mmengine - INFO - Epoch(train) [36][1200/1879] lr: 2.0000e-02 eta: 12:27:00 time: 0.4436 data_time: 0.1495 memory: 6717 grad_norm: 2.7781 loss: 1.5155 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5155 2023/04/14 03:27:02 - mmengine - INFO - Epoch(train) [36][1220/1879] lr: 2.0000e-02 eta: 12:26:51 time: 0.3436 data_time: 0.1749 memory: 6717 grad_norm: 2.7098 loss: 1.6463 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.6463 2023/04/14 03:27:09 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 03:27:10 - mmengine - INFO - Epoch(train) [36][1240/1879] lr: 2.0000e-02 eta: 12:26:45 time: 0.4090 data_time: 0.2656 memory: 6717 grad_norm: 2.6910 loss: 1.5845 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5845 2023/04/14 03:27:17 - mmengine - INFO - Epoch(train) [36][1260/1879] lr: 2.0000e-02 eta: 12:26:37 time: 0.3327 data_time: 0.1925 memory: 6717 grad_norm: 2.7605 loss: 1.5028 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5028 2023/04/14 03:27:25 - mmengine - INFO - Epoch(train) [36][1280/1879] lr: 2.0000e-02 eta: 12:26:29 time: 0.3711 data_time: 0.2161 memory: 6717 grad_norm: 2.6684 loss: 1.3523 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.3523 2023/04/14 03:27:31 - mmengine - INFO - Epoch(train) [36][1300/1879] lr: 2.0000e-02 eta: 12:26:21 time: 0.3400 data_time: 0.1451 memory: 6717 grad_norm: 2.7618 loss: 1.5895 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5895 2023/04/14 03:27:39 - mmengine - INFO - Epoch(train) [36][1320/1879] lr: 2.0000e-02 eta: 12:26:14 time: 0.3879 data_time: 0.2276 memory: 6717 grad_norm: 2.6766 loss: 1.6795 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6795 2023/04/14 03:27:47 - mmengine - INFO - Epoch(train) [36][1340/1879] lr: 2.0000e-02 eta: 12:26:07 time: 0.3756 data_time: 0.0421 memory: 6717 grad_norm: 2.7582 loss: 1.6419 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6419 2023/04/14 03:27:54 - mmengine - INFO - Epoch(train) [36][1360/1879] lr: 2.0000e-02 eta: 12:25:59 time: 0.3636 data_time: 0.0180 memory: 6717 grad_norm: 2.7911 loss: 1.7066 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7066 2023/04/14 03:28:02 - mmengine - INFO - Epoch(train) [36][1380/1879] lr: 2.0000e-02 eta: 12:25:52 time: 0.3884 data_time: 0.0127 memory: 6717 grad_norm: 2.7668 loss: 1.8766 top1_acc: 0.2500 top5_acc: 0.5625 loss_cls: 1.8766 2023/04/14 03:28:08 - mmengine - INFO - Epoch(train) [36][1400/1879] lr: 2.0000e-02 eta: 12:25:43 time: 0.3251 data_time: 0.0162 memory: 6717 grad_norm: 2.6067 loss: 1.6528 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6528 2023/04/14 03:28:16 - mmengine - INFO - Epoch(train) [36][1420/1879] lr: 2.0000e-02 eta: 12:25:37 time: 0.4175 data_time: 0.0131 memory: 6717 grad_norm: 2.7033 loss: 1.5716 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.5716 2023/04/14 03:28:23 - mmengine - INFO - Epoch(train) [36][1440/1879] lr: 2.0000e-02 eta: 12:25:28 time: 0.3073 data_time: 0.0303 memory: 6717 grad_norm: 2.7816 loss: 1.6486 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6486 2023/04/14 03:28:31 - mmengine - INFO - Epoch(train) [36][1460/1879] lr: 2.0000e-02 eta: 12:25:22 time: 0.4049 data_time: 0.0329 memory: 6717 grad_norm: 2.6299 loss: 1.7783 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7783 2023/04/14 03:28:38 - mmengine - INFO - Epoch(train) [36][1480/1879] lr: 2.0000e-02 eta: 12:25:13 time: 0.3482 data_time: 0.0156 memory: 6717 grad_norm: 2.6787 loss: 1.7667 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7667 2023/04/14 03:28:46 - mmengine - INFO - Epoch(train) [36][1500/1879] lr: 2.0000e-02 eta: 12:25:07 time: 0.3905 data_time: 0.0126 memory: 6717 grad_norm: 2.6476 loss: 1.7208 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.7208 2023/04/14 03:28:52 - mmengine - INFO - Epoch(train) [36][1520/1879] lr: 2.0000e-02 eta: 12:24:58 time: 0.3353 data_time: 0.0276 memory: 6717 grad_norm: 2.6823 loss: 1.7469 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7469 2023/04/14 03:29:00 - mmengine - INFO - Epoch(train) [36][1540/1879] lr: 2.0000e-02 eta: 12:24:51 time: 0.3834 data_time: 0.0141 memory: 6717 grad_norm: 2.7178 loss: 1.8887 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.8887 2023/04/14 03:29:07 - mmengine - INFO - Epoch(train) [36][1560/1879] lr: 2.0000e-02 eta: 12:24:43 time: 0.3390 data_time: 0.0148 memory: 6717 grad_norm: 2.6602 loss: 1.5801 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.5801 2023/04/14 03:29:14 - mmengine - INFO - Epoch(train) [36][1580/1879] lr: 2.0000e-02 eta: 12:24:36 time: 0.3872 data_time: 0.0138 memory: 6717 grad_norm: 2.6532 loss: 1.6904 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6904 2023/04/14 03:29:22 - mmengine - INFO - Epoch(train) [36][1600/1879] lr: 2.0000e-02 eta: 12:24:29 time: 0.3956 data_time: 0.0145 memory: 6717 grad_norm: 2.6461 loss: 1.6092 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6092 2023/04/14 03:29:30 - mmengine - INFO - Epoch(train) [36][1620/1879] lr: 2.0000e-02 eta: 12:24:22 time: 0.3746 data_time: 0.0140 memory: 6717 grad_norm: 2.7238 loss: 1.6279 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6279 2023/04/14 03:29:37 - mmengine - INFO - Epoch(train) [36][1640/1879] lr: 2.0000e-02 eta: 12:24:14 time: 0.3599 data_time: 0.0147 memory: 6717 grad_norm: 2.6810 loss: 1.6105 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6105 2023/04/14 03:29:45 - mmengine - INFO - Epoch(train) [36][1660/1879] lr: 2.0000e-02 eta: 12:24:07 time: 0.3770 data_time: 0.0139 memory: 6717 grad_norm: 2.7473 loss: 1.4766 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4766 2023/04/14 03:29:51 - mmengine - INFO - Epoch(train) [36][1680/1879] lr: 2.0000e-02 eta: 12:23:59 time: 0.3446 data_time: 0.0182 memory: 6717 grad_norm: 2.6822 loss: 1.7432 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.7432 2023/04/14 03:30:00 - mmengine - INFO - Epoch(train) [36][1700/1879] lr: 2.0000e-02 eta: 12:23:53 time: 0.4316 data_time: 0.0149 memory: 6717 grad_norm: 2.6619 loss: 1.8186 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 1.8186 2023/04/14 03:30:07 - mmengine - INFO - Epoch(train) [36][1720/1879] lr: 2.0000e-02 eta: 12:23:45 time: 0.3328 data_time: 0.0152 memory: 6717 grad_norm: 2.6839 loss: 1.6702 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6702 2023/04/14 03:30:15 - mmengine - INFO - Epoch(train) [36][1740/1879] lr: 2.0000e-02 eta: 12:23:39 time: 0.4271 data_time: 0.0131 memory: 6717 grad_norm: 2.7039 loss: 1.6595 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6595 2023/04/14 03:30:22 - mmengine - INFO - Epoch(train) [36][1760/1879] lr: 2.0000e-02 eta: 12:23:30 time: 0.3220 data_time: 0.0153 memory: 6717 grad_norm: 2.6464 loss: 1.6309 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6309 2023/04/14 03:30:30 - mmengine - INFO - Epoch(train) [36][1780/1879] lr: 2.0000e-02 eta: 12:23:24 time: 0.4082 data_time: 0.0135 memory: 6717 grad_norm: 2.7408 loss: 1.4721 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.4721 2023/04/14 03:30:36 - mmengine - INFO - Epoch(train) [36][1800/1879] lr: 2.0000e-02 eta: 12:23:15 time: 0.3258 data_time: 0.0142 memory: 6717 grad_norm: 2.6985 loss: 1.8954 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.8954 2023/04/14 03:30:45 - mmengine - INFO - Epoch(train) [36][1820/1879] lr: 2.0000e-02 eta: 12:23:09 time: 0.4046 data_time: 0.0140 memory: 6717 grad_norm: 2.7377 loss: 1.5922 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5922 2023/04/14 03:30:51 - mmengine - INFO - Epoch(train) [36][1840/1879] lr: 2.0000e-02 eta: 12:22:59 time: 0.3119 data_time: 0.0136 memory: 6717 grad_norm: 2.7461 loss: 1.7655 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7655 2023/04/14 03:30:59 - mmengine - INFO - Epoch(train) [36][1860/1879] lr: 2.0000e-02 eta: 12:22:53 time: 0.3933 data_time: 0.0153 memory: 6717 grad_norm: 2.7204 loss: 1.5546 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5546 2023/04/14 03:31:04 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 03:31:04 - mmengine - INFO - Epoch(train) [36][1879/1879] lr: 2.0000e-02 eta: 12:22:43 time: 0.2928 data_time: 0.0125 memory: 6717 grad_norm: 2.7333 loss: 1.7610 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.7610 2023/04/14 03:31:04 - mmengine - INFO - Saving checkpoint at 36 epochs 2023/04/14 03:31:14 - mmengine - INFO - Epoch(val) [36][ 20/155] eta: 0:01:02 time: 0.4604 data_time: 0.4264 memory: 1391 2023/04/14 03:31:21 - mmengine - INFO - Epoch(val) [36][ 40/155] eta: 0:00:44 time: 0.3209 data_time: 0.2877 memory: 1391 2023/04/14 03:31:29 - mmengine - INFO - Epoch(val) [36][ 60/155] eta: 0:00:38 time: 0.4188 data_time: 0.3854 memory: 1391 2023/04/14 03:31:35 - mmengine - INFO - Epoch(val) [36][ 80/155] eta: 0:00:28 time: 0.3228 data_time: 0.2896 memory: 1391 2023/04/14 03:31:44 - mmengine - INFO - Epoch(val) [36][100/155] eta: 0:00:21 time: 0.4248 data_time: 0.3912 memory: 1391 2023/04/14 03:31:51 - mmengine - INFO - Epoch(val) [36][120/155] eta: 0:00:13 time: 0.3322 data_time: 0.2986 memory: 1391 2023/04/14 03:32:00 - mmengine - INFO - Epoch(val) [36][140/155] eta: 0:00:05 time: 0.4833 data_time: 0.4495 memory: 1391 2023/04/14 03:32:07 - mmengine - INFO - Epoch(val) [36][155/155] acc/top1: 0.6066 acc/top5: 0.8393 acc/mean1: 0.6066 data_time: 0.4154 time: 0.4481 2023/04/14 03:32:17 - mmengine - INFO - Epoch(train) [37][ 20/1879] lr: 2.0000e-02 eta: 12:22:41 time: 0.5065 data_time: 0.2946 memory: 6717 grad_norm: 2.6909 loss: 1.5907 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5907 2023/04/14 03:32:24 - mmengine - INFO - Epoch(train) [37][ 40/1879] lr: 2.0000e-02 eta: 12:22:32 time: 0.3329 data_time: 0.0502 memory: 6717 grad_norm: 2.6996 loss: 1.6978 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6978 2023/04/14 03:32:32 - mmengine - INFO - Epoch(train) [37][ 60/1879] lr: 2.0000e-02 eta: 12:22:25 time: 0.3876 data_time: 0.0182 memory: 6717 grad_norm: 2.7190 loss: 1.5503 top1_acc: 0.5625 top5_acc: 0.5625 loss_cls: 1.5503 2023/04/14 03:32:38 - mmengine - INFO - Epoch(train) [37][ 80/1879] lr: 2.0000e-02 eta: 12:22:16 time: 0.3237 data_time: 0.0173 memory: 6717 grad_norm: 2.7788 loss: 1.6069 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6069 2023/04/14 03:32:47 - mmengine - INFO - Epoch(train) [37][ 100/1879] lr: 2.0000e-02 eta: 12:22:10 time: 0.4190 data_time: 0.0444 memory: 6717 grad_norm: 2.7564 loss: 1.7310 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.7310 2023/04/14 03:32:53 - mmengine - INFO - Epoch(train) [37][ 120/1879] lr: 2.0000e-02 eta: 12:22:01 time: 0.3293 data_time: 0.0197 memory: 6717 grad_norm: 2.6652 loss: 1.4324 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4324 2023/04/14 03:33:01 - mmengine - INFO - Epoch(train) [37][ 140/1879] lr: 2.0000e-02 eta: 12:21:55 time: 0.3997 data_time: 0.0225 memory: 6717 grad_norm: 2.7394 loss: 1.4552 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4552 2023/04/14 03:33:08 - mmengine - INFO - Epoch(train) [37][ 160/1879] lr: 2.0000e-02 eta: 12:21:47 time: 0.3464 data_time: 0.0147 memory: 6717 grad_norm: 2.6282 loss: 1.5081 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.5081 2023/04/14 03:33:16 - mmengine - INFO - Epoch(train) [37][ 180/1879] lr: 2.0000e-02 eta: 12:21:40 time: 0.4000 data_time: 0.0162 memory: 6717 grad_norm: 2.7584 loss: 1.6038 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6038 2023/04/14 03:33:23 - mmengine - INFO - Epoch(train) [37][ 200/1879] lr: 2.0000e-02 eta: 12:21:32 time: 0.3543 data_time: 0.0129 memory: 6717 grad_norm: 2.6395 loss: 1.5536 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5536 2023/04/14 03:33:31 - mmengine - INFO - Epoch(train) [37][ 220/1879] lr: 2.0000e-02 eta: 12:21:25 time: 0.3750 data_time: 0.0144 memory: 6717 grad_norm: 2.6687 loss: 1.6165 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.6165 2023/04/14 03:33:38 - mmengine - INFO - Epoch(train) [37][ 240/1879] lr: 2.0000e-02 eta: 12:21:17 time: 0.3457 data_time: 0.0147 memory: 6717 grad_norm: 2.7080 loss: 1.5726 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.5726 2023/04/14 03:33:46 - mmengine - INFO - Epoch(train) [37][ 260/1879] lr: 2.0000e-02 eta: 12:21:11 time: 0.4164 data_time: 0.0145 memory: 6717 grad_norm: 2.6532 loss: 1.5941 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.5941 2023/04/14 03:33:53 - mmengine - INFO - Epoch(train) [37][ 280/1879] lr: 2.0000e-02 eta: 12:21:03 time: 0.3373 data_time: 0.0138 memory: 6717 grad_norm: 2.6882 loss: 1.6152 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6152 2023/04/14 03:34:02 - mmengine - INFO - Epoch(train) [37][ 300/1879] lr: 2.0000e-02 eta: 12:20:57 time: 0.4335 data_time: 0.0135 memory: 6717 grad_norm: 2.7066 loss: 1.4954 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4954 2023/04/14 03:34:07 - mmengine - INFO - Epoch(train) [37][ 320/1879] lr: 2.0000e-02 eta: 12:20:47 time: 0.2944 data_time: 0.0149 memory: 6717 grad_norm: 2.7557 loss: 1.5681 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.5681 2023/04/14 03:34:15 - mmengine - INFO - Epoch(train) [37][ 340/1879] lr: 2.0000e-02 eta: 12:20:41 time: 0.4009 data_time: 0.0133 memory: 6717 grad_norm: 2.7204 loss: 1.5958 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5958 2023/04/14 03:34:22 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 03:34:22 - mmengine - INFO - Epoch(train) [37][ 360/1879] lr: 2.0000e-02 eta: 12:20:33 time: 0.3489 data_time: 0.0149 memory: 6717 grad_norm: 2.7161 loss: 1.6612 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6612 2023/04/14 03:34:30 - mmengine - INFO - Epoch(train) [37][ 380/1879] lr: 2.0000e-02 eta: 12:20:26 time: 0.3848 data_time: 0.0134 memory: 6717 grad_norm: 2.7497 loss: 1.4006 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4006 2023/04/14 03:34:38 - mmengine - INFO - Epoch(train) [37][ 400/1879] lr: 2.0000e-02 eta: 12:20:18 time: 0.3718 data_time: 0.0153 memory: 6717 grad_norm: 2.6884 loss: 1.6292 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6292 2023/04/14 03:34:45 - mmengine - INFO - Epoch(train) [37][ 420/1879] lr: 2.0000e-02 eta: 12:20:11 time: 0.3760 data_time: 0.0149 memory: 6717 grad_norm: 2.7831 loss: 1.5893 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5893 2023/04/14 03:34:52 - mmengine - INFO - Epoch(train) [37][ 440/1879] lr: 2.0000e-02 eta: 12:20:03 time: 0.3460 data_time: 0.0152 memory: 6717 grad_norm: 2.8777 loss: 1.4008 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4008 2023/04/14 03:34:59 - mmengine - INFO - Epoch(train) [37][ 460/1879] lr: 2.0000e-02 eta: 12:19:56 time: 0.3727 data_time: 0.0132 memory: 6717 grad_norm: 2.7924 loss: 1.5930 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5930 2023/04/14 03:35:06 - mmengine - INFO - Epoch(train) [37][ 480/1879] lr: 2.0000e-02 eta: 12:19:47 time: 0.3373 data_time: 0.0139 memory: 6717 grad_norm: 2.7286 loss: 1.4746 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.4746 2023/04/14 03:35:15 - mmengine - INFO - Epoch(train) [37][ 500/1879] lr: 2.0000e-02 eta: 12:19:42 time: 0.4366 data_time: 0.0136 memory: 6717 grad_norm: 2.6735 loss: 1.7999 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.7999 2023/04/14 03:35:21 - mmengine - INFO - Epoch(train) [37][ 520/1879] lr: 2.0000e-02 eta: 12:19:33 time: 0.3224 data_time: 0.0148 memory: 6717 grad_norm: 2.6950 loss: 1.7071 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7071 2023/04/14 03:35:29 - mmengine - INFO - Epoch(train) [37][ 540/1879] lr: 2.0000e-02 eta: 12:19:26 time: 0.3765 data_time: 0.0136 memory: 6717 grad_norm: 2.7221 loss: 1.5707 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5707 2023/04/14 03:35:37 - mmengine - INFO - Epoch(train) [37][ 560/1879] lr: 2.0000e-02 eta: 12:19:19 time: 0.3975 data_time: 0.0149 memory: 6717 grad_norm: 2.7413 loss: 1.5255 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5255 2023/04/14 03:35:43 - mmengine - INFO - Epoch(train) [37][ 580/1879] lr: 2.0000e-02 eta: 12:19:10 time: 0.3287 data_time: 0.0142 memory: 6717 grad_norm: 2.6985 loss: 1.5599 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.5599 2023/04/14 03:35:51 - mmengine - INFO - Epoch(train) [37][ 600/1879] lr: 2.0000e-02 eta: 12:19:02 time: 0.3572 data_time: 0.0138 memory: 6717 grad_norm: 2.6751 loss: 1.7046 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7046 2023/04/14 03:35:58 - mmengine - INFO - Epoch(train) [37][ 620/1879] lr: 2.0000e-02 eta: 12:18:55 time: 0.3779 data_time: 0.0153 memory: 6717 grad_norm: 2.7704 loss: 1.6848 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6848 2023/04/14 03:36:05 - mmengine - INFO - Epoch(train) [37][ 640/1879] lr: 2.0000e-02 eta: 12:18:46 time: 0.3256 data_time: 0.0137 memory: 6717 grad_norm: 2.7367 loss: 1.6231 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6231 2023/04/14 03:36:12 - mmengine - INFO - Epoch(train) [37][ 660/1879] lr: 2.0000e-02 eta: 12:18:39 time: 0.3810 data_time: 0.0144 memory: 6717 grad_norm: 2.6998 loss: 1.4891 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4891 2023/04/14 03:36:19 - mmengine - INFO - Epoch(train) [37][ 680/1879] lr: 2.0000e-02 eta: 12:18:30 time: 0.3333 data_time: 0.0143 memory: 6717 grad_norm: 2.7859 loss: 1.6943 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6943 2023/04/14 03:36:27 - mmengine - INFO - Epoch(train) [37][ 700/1879] lr: 2.0000e-02 eta: 12:18:24 time: 0.4082 data_time: 0.0156 memory: 6717 grad_norm: 2.6736 loss: 1.7001 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7001 2023/04/14 03:36:34 - mmengine - INFO - Epoch(train) [37][ 720/1879] lr: 2.0000e-02 eta: 12:18:16 time: 0.3298 data_time: 0.0146 memory: 6717 grad_norm: 2.6987 loss: 1.7925 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7925 2023/04/14 03:36:41 - mmengine - INFO - Epoch(train) [37][ 740/1879] lr: 2.0000e-02 eta: 12:18:08 time: 0.3781 data_time: 0.0134 memory: 6717 grad_norm: 2.7473 loss: 1.6607 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6607 2023/04/14 03:36:50 - mmengine - INFO - Epoch(train) [37][ 760/1879] lr: 2.0000e-02 eta: 12:18:03 time: 0.4254 data_time: 0.0148 memory: 6717 grad_norm: 2.7263 loss: 1.6110 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6110 2023/04/14 03:36:57 - mmengine - INFO - Epoch(train) [37][ 780/1879] lr: 2.0000e-02 eta: 12:17:55 time: 0.3592 data_time: 0.0138 memory: 6717 grad_norm: 2.7808 loss: 1.6616 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6616 2023/04/14 03:37:05 - mmengine - INFO - Epoch(train) [37][ 800/1879] lr: 2.0000e-02 eta: 12:17:48 time: 0.3878 data_time: 0.0156 memory: 6717 grad_norm: 3.3075 loss: 1.7562 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7562 2023/04/14 03:37:12 - mmengine - INFO - Epoch(train) [37][ 820/1879] lr: 2.0000e-02 eta: 12:17:40 time: 0.3402 data_time: 0.0136 memory: 6717 grad_norm: 2.7199 loss: 1.6320 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.6320 2023/04/14 03:37:19 - mmengine - INFO - Epoch(train) [37][ 840/1879] lr: 2.0000e-02 eta: 12:17:32 time: 0.3638 data_time: 0.0147 memory: 6717 grad_norm: 2.7409 loss: 1.4290 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.4290 2023/04/14 03:37:26 - mmengine - INFO - Epoch(train) [37][ 860/1879] lr: 2.0000e-02 eta: 12:17:23 time: 0.3346 data_time: 0.0133 memory: 6717 grad_norm: 2.6576 loss: 1.4416 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4416 2023/04/14 03:37:34 - mmengine - INFO - Epoch(train) [37][ 880/1879] lr: 2.0000e-02 eta: 12:17:19 time: 0.4438 data_time: 0.0171 memory: 6717 grad_norm: 2.6380 loss: 1.5883 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5883 2023/04/14 03:37:41 - mmengine - INFO - Epoch(train) [37][ 900/1879] lr: 2.0000e-02 eta: 12:17:09 time: 0.3094 data_time: 0.0135 memory: 6717 grad_norm: 2.7096 loss: 1.5533 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5533 2023/04/14 03:37:49 - mmengine - INFO - Epoch(train) [37][ 920/1879] lr: 2.0000e-02 eta: 12:17:03 time: 0.3956 data_time: 0.0143 memory: 6717 grad_norm: 2.7395 loss: 1.7254 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.7254 2023/04/14 03:37:56 - mmengine - INFO - Epoch(train) [37][ 940/1879] lr: 2.0000e-02 eta: 12:16:56 time: 0.3814 data_time: 0.0145 memory: 6717 grad_norm: 2.6181 loss: 1.6932 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6932 2023/04/14 03:38:03 - mmengine - INFO - Epoch(train) [37][ 960/1879] lr: 2.0000e-02 eta: 12:16:47 time: 0.3342 data_time: 0.0131 memory: 6717 grad_norm: 2.7444 loss: 1.6155 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6155 2023/04/14 03:38:12 - mmengine - INFO - Epoch(train) [37][ 980/1879] lr: 2.0000e-02 eta: 12:16:42 time: 0.4396 data_time: 0.0144 memory: 6717 grad_norm: 2.7080 loss: 1.4705 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.4705 2023/04/14 03:38:18 - mmengine - INFO - Epoch(train) [37][1000/1879] lr: 2.0000e-02 eta: 12:16:33 time: 0.3178 data_time: 0.0141 memory: 6717 grad_norm: 2.7220 loss: 1.6106 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6106 2023/04/14 03:38:26 - mmengine - INFO - Epoch(train) [37][1020/1879] lr: 2.0000e-02 eta: 12:16:26 time: 0.3970 data_time: 0.0133 memory: 6717 grad_norm: 2.7240 loss: 1.5815 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.5815 2023/04/14 03:38:33 - mmengine - INFO - Epoch(train) [37][1040/1879] lr: 2.0000e-02 eta: 12:16:18 time: 0.3606 data_time: 0.0150 memory: 6717 grad_norm: 2.6700 loss: 1.6580 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.6580 2023/04/14 03:38:41 - mmengine - INFO - Epoch(train) [37][1060/1879] lr: 2.0000e-02 eta: 12:16:12 time: 0.3960 data_time: 0.0140 memory: 6717 grad_norm: 2.7076 loss: 1.6380 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6380 2023/04/14 03:38:48 - mmengine - INFO - Epoch(train) [37][1080/1879] lr: 2.0000e-02 eta: 12:16:04 time: 0.3605 data_time: 0.0152 memory: 6717 grad_norm: 2.6244 loss: 1.5593 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5593 2023/04/14 03:38:57 - mmengine - INFO - Epoch(train) [37][1100/1879] lr: 2.0000e-02 eta: 12:15:59 time: 0.4376 data_time: 0.0129 memory: 6717 grad_norm: 2.6711 loss: 1.7952 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7952 2023/04/14 03:39:04 - mmengine - INFO - Epoch(train) [37][1120/1879] lr: 2.0000e-02 eta: 12:15:51 time: 0.3615 data_time: 0.0147 memory: 6717 grad_norm: 2.6133 loss: 1.6085 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6085 2023/04/14 03:39:12 - mmengine - INFO - Epoch(train) [37][1140/1879] lr: 2.0000e-02 eta: 12:15:44 time: 0.3822 data_time: 0.0129 memory: 6717 grad_norm: 2.6876 loss: 1.5124 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5124 2023/04/14 03:39:18 - mmengine - INFO - Epoch(train) [37][1160/1879] lr: 2.0000e-02 eta: 12:15:35 time: 0.3284 data_time: 0.0142 memory: 6717 grad_norm: 2.7838 loss: 1.4867 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4867 2023/04/14 03:39:26 - mmengine - INFO - Epoch(train) [37][1180/1879] lr: 2.0000e-02 eta: 12:15:29 time: 0.3941 data_time: 0.0137 memory: 6717 grad_norm: 2.7237 loss: 1.7808 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7808 2023/04/14 03:39:33 - mmengine - INFO - Epoch(train) [37][1200/1879] lr: 2.0000e-02 eta: 12:15:20 time: 0.3252 data_time: 0.0155 memory: 6717 grad_norm: 2.6780 loss: 1.5657 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5657 2023/04/14 03:39:41 - mmengine - INFO - Epoch(train) [37][1220/1879] lr: 2.0000e-02 eta: 12:15:14 time: 0.4107 data_time: 0.0140 memory: 6717 grad_norm: 2.7084 loss: 1.4457 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4457 2023/04/14 03:39:49 - mmengine - INFO - Epoch(train) [37][1240/1879] lr: 2.0000e-02 eta: 12:15:06 time: 0.3747 data_time: 0.0156 memory: 6717 grad_norm: 2.6733 loss: 1.5805 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5805 2023/04/14 03:39:57 - mmengine - INFO - Epoch(train) [37][1260/1879] lr: 2.0000e-02 eta: 12:15:01 time: 0.4273 data_time: 0.0128 memory: 6717 grad_norm: 2.6381 loss: 1.5946 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5946 2023/04/14 03:40:04 - mmengine - INFO - Epoch(train) [37][1280/1879] lr: 2.0000e-02 eta: 12:14:52 time: 0.3259 data_time: 0.0135 memory: 6717 grad_norm: 2.7380 loss: 1.4909 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4909 2023/04/14 03:40:12 - mmengine - INFO - Epoch(train) [37][1300/1879] lr: 2.0000e-02 eta: 12:14:45 time: 0.3935 data_time: 0.0142 memory: 6717 grad_norm: 2.6817 loss: 1.4878 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.4878 2023/04/14 03:40:17 - mmengine - INFO - Epoch(train) [37][1320/1879] lr: 2.0000e-02 eta: 12:14:35 time: 0.2757 data_time: 0.0157 memory: 6717 grad_norm: 2.7252 loss: 1.6940 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.6940 2023/04/14 03:40:25 - mmengine - INFO - Epoch(train) [37][1340/1879] lr: 2.0000e-02 eta: 12:14:28 time: 0.3979 data_time: 0.0138 memory: 6717 grad_norm: 2.7211 loss: 1.7126 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7126 2023/04/14 03:40:31 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 03:40:32 - mmengine - INFO - Epoch(train) [37][1360/1879] lr: 2.0000e-02 eta: 12:14:20 time: 0.3461 data_time: 0.0160 memory: 6717 grad_norm: 2.8178 loss: 1.7832 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7832 2023/04/14 03:40:40 - mmengine - INFO - Epoch(train) [37][1380/1879] lr: 2.0000e-02 eta: 12:14:13 time: 0.3902 data_time: 0.0130 memory: 6717 grad_norm: 2.7094 loss: 1.5775 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5775 2023/04/14 03:40:47 - mmengine - INFO - Epoch(train) [37][1400/1879] lr: 2.0000e-02 eta: 12:14:05 time: 0.3371 data_time: 0.0155 memory: 6717 grad_norm: 2.7290 loss: 1.6395 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.6395 2023/04/14 03:40:55 - mmengine - INFO - Epoch(train) [37][1420/1879] lr: 2.0000e-02 eta: 12:13:59 time: 0.4309 data_time: 0.0127 memory: 6717 grad_norm: 2.6918 loss: 1.7526 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7526 2023/04/14 03:41:01 - mmengine - INFO - Epoch(train) [37][1440/1879] lr: 2.0000e-02 eta: 12:13:50 time: 0.3093 data_time: 0.0149 memory: 6717 grad_norm: 2.6662 loss: 1.5559 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5559 2023/04/14 03:41:09 - mmengine - INFO - Epoch(train) [37][1460/1879] lr: 2.0000e-02 eta: 12:13:44 time: 0.4061 data_time: 0.0135 memory: 6717 grad_norm: 2.6692 loss: 1.5418 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5418 2023/04/14 03:41:16 - mmengine - INFO - Epoch(train) [37][1480/1879] lr: 2.0000e-02 eta: 12:13:35 time: 0.3219 data_time: 0.0159 memory: 6717 grad_norm: 2.7688 loss: 1.7638 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7638 2023/04/14 03:41:24 - mmengine - INFO - Epoch(train) [37][1500/1879] lr: 2.0000e-02 eta: 12:13:29 time: 0.4304 data_time: 0.0136 memory: 6717 grad_norm: 2.6863 loss: 1.4517 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.4517 2023/04/14 03:41:31 - mmengine - INFO - Epoch(train) [37][1520/1879] lr: 2.0000e-02 eta: 12:13:20 time: 0.3123 data_time: 0.0147 memory: 6717 grad_norm: 2.7564 loss: 1.6326 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6326 2023/04/14 03:41:39 - mmengine - INFO - Epoch(train) [37][1540/1879] lr: 2.0000e-02 eta: 12:13:14 time: 0.4237 data_time: 0.0140 memory: 6717 grad_norm: 2.6928 loss: 1.6337 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6337 2023/04/14 03:41:46 - mmengine - INFO - Epoch(train) [37][1560/1879] lr: 2.0000e-02 eta: 12:13:06 time: 0.3453 data_time: 0.0140 memory: 6717 grad_norm: 2.6774 loss: 1.8706 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8706 2023/04/14 03:41:52 - mmengine - INFO - Epoch(train) [37][1580/1879] lr: 2.0000e-02 eta: 12:12:57 time: 0.3125 data_time: 0.0145 memory: 6717 grad_norm: 2.7613 loss: 1.6478 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6478 2023/04/14 03:42:00 - mmengine - INFO - Epoch(train) [37][1600/1879] lr: 2.0000e-02 eta: 12:12:50 time: 0.4008 data_time: 0.0153 memory: 6717 grad_norm: 2.6900 loss: 1.5744 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.5744 2023/04/14 03:42:08 - mmengine - INFO - Epoch(train) [37][1620/1879] lr: 2.0000e-02 eta: 12:12:42 time: 0.3565 data_time: 0.0138 memory: 6717 grad_norm: 2.7343 loss: 1.6630 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6630 2023/04/14 03:42:15 - mmengine - INFO - Epoch(train) [37][1640/1879] lr: 2.0000e-02 eta: 12:12:35 time: 0.3761 data_time: 0.0154 memory: 6717 grad_norm: 2.7706 loss: 1.7115 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7115 2023/04/14 03:42:22 - mmengine - INFO - Epoch(train) [37][1660/1879] lr: 2.0000e-02 eta: 12:12:27 time: 0.3502 data_time: 0.0240 memory: 6717 grad_norm: 2.6811 loss: 1.4983 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4983 2023/04/14 03:42:30 - mmengine - INFO - Epoch(train) [37][1680/1879] lr: 2.0000e-02 eta: 12:12:21 time: 0.4211 data_time: 0.0151 memory: 6717 grad_norm: 2.8825 loss: 1.6551 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6551 2023/04/14 03:42:39 - mmengine - INFO - Epoch(train) [37][1700/1879] lr: 2.0000e-02 eta: 12:12:15 time: 0.4069 data_time: 0.0138 memory: 6717 grad_norm: 2.6789 loss: 1.7442 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7442 2023/04/14 03:42:45 - mmengine - INFO - Epoch(train) [37][1720/1879] lr: 2.0000e-02 eta: 12:12:06 time: 0.3258 data_time: 0.0148 memory: 6717 grad_norm: 2.7165 loss: 1.6793 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.6793 2023/04/14 03:42:53 - mmengine - INFO - Epoch(train) [37][1740/1879] lr: 2.0000e-02 eta: 12:11:59 time: 0.3726 data_time: 0.0152 memory: 6717 grad_norm: 2.6973 loss: 1.5742 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5742 2023/04/14 03:43:00 - mmengine - INFO - Epoch(train) [37][1760/1879] lr: 2.0000e-02 eta: 12:11:52 time: 0.3818 data_time: 0.0143 memory: 6717 grad_norm: 2.7286 loss: 1.7968 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7968 2023/04/14 03:43:07 - mmengine - INFO - Epoch(train) [37][1780/1879] lr: 2.0000e-02 eta: 12:11:42 time: 0.3157 data_time: 0.0137 memory: 6717 grad_norm: 2.7437 loss: 1.4618 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4618 2023/04/14 03:43:14 - mmengine - INFO - Epoch(train) [37][1800/1879] lr: 2.0000e-02 eta: 12:11:36 time: 0.3971 data_time: 0.0210 memory: 6717 grad_norm: 2.7001 loss: 1.6387 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.6387 2023/04/14 03:43:21 - mmengine - INFO - Epoch(train) [37][1820/1879] lr: 2.0000e-02 eta: 12:11:27 time: 0.3254 data_time: 0.0145 memory: 6717 grad_norm: 2.7230 loss: 1.7184 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7184 2023/04/14 03:43:30 - mmengine - INFO - Epoch(train) [37][1840/1879] lr: 2.0000e-02 eta: 12:11:22 time: 0.4346 data_time: 0.0273 memory: 6717 grad_norm: 2.7208 loss: 1.8288 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.8288 2023/04/14 03:43:37 - mmengine - INFO - Epoch(train) [37][1860/1879] lr: 2.0000e-02 eta: 12:11:15 time: 0.3747 data_time: 0.0189 memory: 6717 grad_norm: 2.7051 loss: 1.6019 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6019 2023/04/14 03:43:43 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 03:43:43 - mmengine - INFO - Epoch(train) [37][1879/1879] lr: 2.0000e-02 eta: 12:11:06 time: 0.3027 data_time: 0.0148 memory: 6717 grad_norm: 2.7348 loss: 1.5568 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 1.5568 2023/04/14 03:43:52 - mmengine - INFO - Epoch(val) [37][ 20/155] eta: 0:00:58 time: 0.4324 data_time: 0.3991 memory: 1391 2023/04/14 03:43:59 - mmengine - INFO - Epoch(val) [37][ 40/155] eta: 0:00:44 time: 0.3421 data_time: 0.3093 memory: 1391 2023/04/14 03:44:06 - mmengine - INFO - Epoch(val) [37][ 60/155] eta: 0:00:36 time: 0.3734 data_time: 0.3391 memory: 1391 2023/04/14 03:44:14 - mmengine - INFO - Epoch(val) [37][ 80/155] eta: 0:00:28 time: 0.3750 data_time: 0.3414 memory: 1391 2023/04/14 03:44:22 - mmengine - INFO - Epoch(val) [37][100/155] eta: 0:00:21 time: 0.4242 data_time: 0.3908 memory: 1391 2023/04/14 03:44:28 - mmengine - INFO - Epoch(val) [37][120/155] eta: 0:00:13 time: 0.3112 data_time: 0.2779 memory: 1391 2023/04/14 03:44:36 - mmengine - INFO - Epoch(val) [37][140/155] eta: 0:00:05 time: 0.3759 data_time: 0.3426 memory: 1391 2023/04/14 03:44:45 - mmengine - INFO - Epoch(val) [37][155/155] acc/top1: 0.6121 acc/top5: 0.8399 acc/mean1: 0.6120 data_time: 0.3228 time: 0.3558 2023/04/14 03:44:45 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_34.pth is removed 2023/04/14 03:44:45 - mmengine - INFO - The best checkpoint with 0.6121 acc/top1 at 37 epoch is saved to best_acc_top1_epoch_37.pth. 2023/04/14 03:44:56 - mmengine - INFO - Epoch(train) [38][ 20/1879] lr: 2.0000e-02 eta: 12:11:03 time: 0.5207 data_time: 0.3292 memory: 6717 grad_norm: 2.7362 loss: 1.5534 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.5534 2023/04/14 03:45:02 - mmengine - INFO - Epoch(train) [38][ 40/1879] lr: 2.0000e-02 eta: 12:10:54 time: 0.3289 data_time: 0.0726 memory: 6717 grad_norm: 2.6714 loss: 1.5668 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.5668 2023/04/14 03:45:11 - mmengine - INFO - Epoch(train) [38][ 60/1879] lr: 2.0000e-02 eta: 12:10:49 time: 0.4381 data_time: 0.0648 memory: 6717 grad_norm: 2.7488 loss: 1.8263 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.8263 2023/04/14 03:45:17 - mmengine - INFO - Epoch(train) [38][ 80/1879] lr: 2.0000e-02 eta: 12:10:40 time: 0.3119 data_time: 0.0178 memory: 6717 grad_norm: 2.7444 loss: 1.5032 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.5032 2023/04/14 03:45:25 - mmengine - INFO - Epoch(train) [38][ 100/1879] lr: 2.0000e-02 eta: 12:10:34 time: 0.4141 data_time: 0.0765 memory: 6717 grad_norm: 2.7808 loss: 1.6432 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6432 2023/04/14 03:45:32 - mmengine - INFO - Epoch(train) [38][ 120/1879] lr: 2.0000e-02 eta: 12:10:25 time: 0.3150 data_time: 0.0693 memory: 6717 grad_norm: 2.6718 loss: 1.5809 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5809 2023/04/14 03:45:40 - mmengine - INFO - Epoch(train) [38][ 140/1879] lr: 2.0000e-02 eta: 12:10:19 time: 0.4297 data_time: 0.0453 memory: 6717 grad_norm: 2.6970 loss: 1.7627 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7627 2023/04/14 03:45:46 - mmengine - INFO - Epoch(train) [38][ 160/1879] lr: 2.0000e-02 eta: 12:10:10 time: 0.3050 data_time: 0.0144 memory: 6717 grad_norm: 2.7044 loss: 1.6337 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6337 2023/04/14 03:45:54 - mmengine - INFO - Epoch(train) [38][ 180/1879] lr: 2.0000e-02 eta: 12:10:03 time: 0.3935 data_time: 0.0186 memory: 6717 grad_norm: 2.6649 loss: 1.5659 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.5659 2023/04/14 03:46:01 - mmengine - INFO - Epoch(train) [38][ 200/1879] lr: 2.0000e-02 eta: 12:09:54 time: 0.3337 data_time: 0.0480 memory: 6717 grad_norm: 2.7180 loss: 1.5175 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5175 2023/04/14 03:46:09 - mmengine - INFO - Epoch(train) [38][ 220/1879] lr: 2.0000e-02 eta: 12:09:48 time: 0.4054 data_time: 0.0699 memory: 6717 grad_norm: 2.6868 loss: 1.4806 top1_acc: 0.3750 top5_acc: 0.9375 loss_cls: 1.4806 2023/04/14 03:46:16 - mmengine - INFO - Epoch(train) [38][ 240/1879] lr: 2.0000e-02 eta: 12:09:39 time: 0.3287 data_time: 0.0589 memory: 6717 grad_norm: 2.7333 loss: 1.5796 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.5796 2023/04/14 03:46:25 - mmengine - INFO - Epoch(train) [38][ 260/1879] lr: 2.0000e-02 eta: 12:09:34 time: 0.4428 data_time: 0.0272 memory: 6717 grad_norm: 2.6970 loss: 1.6750 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6750 2023/04/14 03:46:31 - mmengine - INFO - Epoch(train) [38][ 280/1879] lr: 2.0000e-02 eta: 12:09:25 time: 0.3160 data_time: 0.0144 memory: 6717 grad_norm: 2.7417 loss: 1.6782 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6782 2023/04/14 03:46:39 - mmengine - INFO - Epoch(train) [38][ 300/1879] lr: 2.0000e-02 eta: 12:09:19 time: 0.4118 data_time: 0.0131 memory: 6717 grad_norm: 2.6995 loss: 1.5452 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5452 2023/04/14 03:46:45 - mmengine - INFO - Epoch(train) [38][ 320/1879] lr: 2.0000e-02 eta: 12:09:10 time: 0.3147 data_time: 0.0409 memory: 6717 grad_norm: 2.7393 loss: 1.7045 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7045 2023/04/14 03:46:53 - mmengine - INFO - Epoch(train) [38][ 340/1879] lr: 2.0000e-02 eta: 12:09:02 time: 0.3724 data_time: 0.0898 memory: 6717 grad_norm: 2.7525 loss: 1.8458 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.8458 2023/04/14 03:47:00 - mmengine - INFO - Epoch(train) [38][ 360/1879] lr: 2.0000e-02 eta: 12:08:55 time: 0.3592 data_time: 0.0923 memory: 6717 grad_norm: 2.7716 loss: 1.6323 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.6323 2023/04/14 03:47:08 - mmengine - INFO - Epoch(train) [38][ 380/1879] lr: 2.0000e-02 eta: 12:08:49 time: 0.4136 data_time: 0.0856 memory: 6717 grad_norm: 2.6585 loss: 1.4691 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.4691 2023/04/14 03:47:16 - mmengine - INFO - Epoch(train) [38][ 400/1879] lr: 2.0000e-02 eta: 12:08:41 time: 0.3789 data_time: 0.1005 memory: 6717 grad_norm: 2.7191 loss: 1.4385 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4385 2023/04/14 03:47:23 - mmengine - INFO - Epoch(train) [38][ 420/1879] lr: 2.0000e-02 eta: 12:08:34 time: 0.3628 data_time: 0.0434 memory: 6717 grad_norm: 2.7088 loss: 1.7516 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.7516 2023/04/14 03:47:31 - mmengine - INFO - Epoch(train) [38][ 440/1879] lr: 2.0000e-02 eta: 12:08:26 time: 0.3671 data_time: 0.1147 memory: 6717 grad_norm: 2.7228 loss: 1.6764 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6764 2023/04/14 03:47:38 - mmengine - INFO - Epoch(train) [38][ 460/1879] lr: 2.0000e-02 eta: 12:08:19 time: 0.3612 data_time: 0.0891 memory: 6717 grad_norm: 2.7009 loss: 1.6727 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6727 2023/04/14 03:47:43 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 03:47:46 - mmengine - INFO - Epoch(train) [38][ 480/1879] lr: 2.0000e-02 eta: 12:08:13 time: 0.4119 data_time: 0.2554 memory: 6717 grad_norm: 2.6904 loss: 1.5812 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.5812 2023/04/14 03:47:52 - mmengine - INFO - Epoch(train) [38][ 500/1879] lr: 2.0000e-02 eta: 12:08:03 time: 0.3155 data_time: 0.1731 memory: 6717 grad_norm: 2.7100 loss: 1.6026 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6026 2023/04/14 03:47:59 - mmengine - INFO - Epoch(train) [38][ 520/1879] lr: 2.0000e-02 eta: 12:07:55 time: 0.3580 data_time: 0.1978 memory: 6717 grad_norm: 2.6968 loss: 1.6995 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.6995 2023/04/14 03:48:07 - mmengine - INFO - Epoch(train) [38][ 540/1879] lr: 2.0000e-02 eta: 12:07:48 time: 0.3621 data_time: 0.1369 memory: 6717 grad_norm: 2.6848 loss: 1.7539 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7539 2023/04/14 03:48:14 - mmengine - INFO - Epoch(train) [38][ 560/1879] lr: 2.0000e-02 eta: 12:07:41 time: 0.3888 data_time: 0.2101 memory: 6717 grad_norm: 2.7321 loss: 1.7470 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.7470 2023/04/14 03:48:22 - mmengine - INFO - Epoch(train) [38][ 580/1879] lr: 2.0000e-02 eta: 12:07:34 time: 0.3698 data_time: 0.0693 memory: 6717 grad_norm: 2.6479 loss: 1.8256 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8256 2023/04/14 03:48:29 - mmengine - INFO - Epoch(train) [38][ 600/1879] lr: 2.0000e-02 eta: 12:07:26 time: 0.3647 data_time: 0.1234 memory: 6717 grad_norm: 2.7971 loss: 1.7970 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7970 2023/04/14 03:48:37 - mmengine - INFO - Epoch(train) [38][ 620/1879] lr: 2.0000e-02 eta: 12:07:18 time: 0.3706 data_time: 0.0810 memory: 6717 grad_norm: 2.7044 loss: 1.5045 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5045 2023/04/14 03:48:44 - mmengine - INFO - Epoch(train) [38][ 640/1879] lr: 2.0000e-02 eta: 12:07:10 time: 0.3480 data_time: 0.1295 memory: 6717 grad_norm: 2.7806 loss: 1.6422 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6422 2023/04/14 03:48:51 - mmengine - INFO - Epoch(train) [38][ 660/1879] lr: 2.0000e-02 eta: 12:07:04 time: 0.3890 data_time: 0.0719 memory: 6717 grad_norm: 2.7434 loss: 1.7084 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.7084 2023/04/14 03:48:58 - mmengine - INFO - Epoch(train) [38][ 680/1879] lr: 2.0000e-02 eta: 12:06:55 time: 0.3388 data_time: 0.0641 memory: 6717 grad_norm: 2.7174 loss: 1.6467 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6467 2023/04/14 03:49:07 - mmengine - INFO - Epoch(train) [38][ 700/1879] lr: 2.0000e-02 eta: 12:06:49 time: 0.4210 data_time: 0.0125 memory: 6717 grad_norm: 2.6732 loss: 1.5124 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.5124 2023/04/14 03:49:13 - mmengine - INFO - Epoch(train) [38][ 720/1879] lr: 2.0000e-02 eta: 12:06:40 time: 0.3241 data_time: 0.0136 memory: 6717 grad_norm: 2.7088 loss: 1.6981 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 1.6981 2023/04/14 03:49:21 - mmengine - INFO - Epoch(train) [38][ 740/1879] lr: 2.0000e-02 eta: 12:06:33 time: 0.3809 data_time: 0.0140 memory: 6717 grad_norm: 2.7441 loss: 1.8538 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8538 2023/04/14 03:49:28 - mmengine - INFO - Epoch(train) [38][ 760/1879] lr: 2.0000e-02 eta: 12:06:26 time: 0.3882 data_time: 0.0145 memory: 6717 grad_norm: 2.7255 loss: 1.7282 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7282 2023/04/14 03:49:35 - mmengine - INFO - Epoch(train) [38][ 780/1879] lr: 2.0000e-02 eta: 12:06:17 time: 0.3164 data_time: 0.0127 memory: 6717 grad_norm: 2.7434 loss: 1.6074 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6074 2023/04/14 03:49:43 - mmengine - INFO - Epoch(train) [38][ 800/1879] lr: 2.0000e-02 eta: 12:06:12 time: 0.4206 data_time: 0.0149 memory: 6717 grad_norm: 2.7685 loss: 1.5676 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5676 2023/04/14 03:49:50 - mmengine - INFO - Epoch(train) [38][ 820/1879] lr: 2.0000e-02 eta: 12:06:03 time: 0.3406 data_time: 0.0125 memory: 6717 grad_norm: 2.7142 loss: 1.6725 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6725 2023/04/14 03:49:58 - mmengine - INFO - Epoch(train) [38][ 840/1879] lr: 2.0000e-02 eta: 12:05:57 time: 0.3965 data_time: 0.0131 memory: 6717 grad_norm: 2.7001 loss: 1.6712 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.6712 2023/04/14 03:50:04 - mmengine - INFO - Epoch(train) [38][ 860/1879] lr: 2.0000e-02 eta: 12:05:48 time: 0.3297 data_time: 0.0138 memory: 6717 grad_norm: 2.7671 loss: 1.9019 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.9019 2023/04/14 03:50:13 - mmengine - INFO - Epoch(train) [38][ 880/1879] lr: 2.0000e-02 eta: 12:05:42 time: 0.4104 data_time: 0.0135 memory: 6717 grad_norm: 2.7405 loss: 1.5647 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5647 2023/04/14 03:50:19 - mmengine - INFO - Epoch(train) [38][ 900/1879] lr: 2.0000e-02 eta: 12:05:33 time: 0.3353 data_time: 0.0143 memory: 6717 grad_norm: 2.6788 loss: 1.4647 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4647 2023/04/14 03:50:27 - mmengine - INFO - Epoch(train) [38][ 920/1879] lr: 2.0000e-02 eta: 12:05:26 time: 0.3660 data_time: 0.0135 memory: 6717 grad_norm: 2.7028 loss: 1.6098 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6098 2023/04/14 03:50:35 - mmengine - INFO - Epoch(train) [38][ 940/1879] lr: 2.0000e-02 eta: 12:05:19 time: 0.3951 data_time: 0.0141 memory: 6717 grad_norm: 2.6837 loss: 1.5981 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5981 2023/04/14 03:50:42 - mmengine - INFO - Epoch(train) [38][ 960/1879] lr: 2.0000e-02 eta: 12:05:11 time: 0.3528 data_time: 0.0152 memory: 6717 grad_norm: 2.6864 loss: 1.6049 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6049 2023/04/14 03:50:49 - mmengine - INFO - Epoch(train) [38][ 980/1879] lr: 2.0000e-02 eta: 12:05:03 time: 0.3493 data_time: 0.0133 memory: 6717 grad_norm: 2.8021 loss: 1.7599 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7599 2023/04/14 03:50:57 - mmengine - INFO - Epoch(train) [38][1000/1879] lr: 2.0000e-02 eta: 12:04:57 time: 0.4156 data_time: 0.0154 memory: 6717 grad_norm: 2.6840 loss: 1.7417 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7417 2023/04/14 03:51:03 - mmengine - INFO - Epoch(train) [38][1020/1879] lr: 2.0000e-02 eta: 12:04:48 time: 0.3151 data_time: 0.0133 memory: 6717 grad_norm: 2.6916 loss: 1.4513 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4513 2023/04/14 03:51:12 - mmengine - INFO - Epoch(train) [38][1040/1879] lr: 2.0000e-02 eta: 12:04:42 time: 0.4221 data_time: 0.0145 memory: 6717 grad_norm: 2.7180 loss: 1.9723 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.9723 2023/04/14 03:51:18 - mmengine - INFO - Epoch(train) [38][1060/1879] lr: 2.0000e-02 eta: 12:04:33 time: 0.3274 data_time: 0.0141 memory: 6717 grad_norm: 2.7692 loss: 1.6161 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.6161 2023/04/14 03:51:26 - mmengine - INFO - Epoch(train) [38][1080/1879] lr: 2.0000e-02 eta: 12:04:27 time: 0.4051 data_time: 0.0142 memory: 6717 grad_norm: 2.6854 loss: 1.5852 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5852 2023/04/14 03:51:33 - mmengine - INFO - Epoch(train) [38][1100/1879] lr: 2.0000e-02 eta: 12:04:18 time: 0.3149 data_time: 0.0142 memory: 6717 grad_norm: 2.6948 loss: 1.5863 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5863 2023/04/14 03:51:41 - mmengine - INFO - Epoch(train) [38][1120/1879] lr: 2.0000e-02 eta: 12:04:11 time: 0.3991 data_time: 0.0139 memory: 6717 grad_norm: 2.7203 loss: 1.6941 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6941 2023/04/14 03:51:48 - mmengine - INFO - Epoch(train) [38][1140/1879] lr: 2.0000e-02 eta: 12:04:03 time: 0.3517 data_time: 0.0143 memory: 6717 grad_norm: 2.7263 loss: 1.7299 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7299 2023/04/14 03:51:55 - mmengine - INFO - Epoch(train) [38][1160/1879] lr: 2.0000e-02 eta: 12:03:56 time: 0.3666 data_time: 0.0154 memory: 6717 grad_norm: 2.7736 loss: 1.7861 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7861 2023/04/14 03:52:02 - mmengine - INFO - Epoch(train) [38][1180/1879] lr: 2.0000e-02 eta: 12:03:47 time: 0.3297 data_time: 0.0190 memory: 6717 grad_norm: 2.6624 loss: 1.5185 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5185 2023/04/14 03:52:10 - mmengine - INFO - Epoch(train) [38][1200/1879] lr: 2.0000e-02 eta: 12:03:41 time: 0.4321 data_time: 0.0473 memory: 6717 grad_norm: 2.6578 loss: 1.7597 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7597 2023/04/14 03:52:17 - mmengine - INFO - Epoch(train) [38][1220/1879] lr: 2.0000e-02 eta: 12:03:32 time: 0.3191 data_time: 0.0126 memory: 6717 grad_norm: 2.7048 loss: 1.5706 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5706 2023/04/14 03:52:25 - mmengine - INFO - Epoch(train) [38][1240/1879] lr: 2.0000e-02 eta: 12:03:26 time: 0.4060 data_time: 0.0157 memory: 6717 grad_norm: 2.7104 loss: 1.6100 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.6100 2023/04/14 03:52:31 - mmengine - INFO - Epoch(train) [38][1260/1879] lr: 2.0000e-02 eta: 12:03:17 time: 0.3189 data_time: 0.0137 memory: 6717 grad_norm: 2.6676 loss: 1.6467 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6467 2023/04/14 03:52:40 - mmengine - INFO - Epoch(train) [38][1280/1879] lr: 2.0000e-02 eta: 12:03:11 time: 0.4251 data_time: 0.0144 memory: 6717 grad_norm: 2.7209 loss: 1.5501 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5501 2023/04/14 03:52:46 - mmengine - INFO - Epoch(train) [38][1300/1879] lr: 2.0000e-02 eta: 12:03:02 time: 0.3267 data_time: 0.0134 memory: 6717 grad_norm: 2.8279 loss: 1.6288 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6288 2023/04/14 03:52:55 - mmengine - INFO - Epoch(train) [38][1320/1879] lr: 2.0000e-02 eta: 12:02:56 time: 0.4138 data_time: 0.0151 memory: 6717 grad_norm: 2.6919 loss: 1.6421 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6421 2023/04/14 03:53:01 - mmengine - INFO - Epoch(train) [38][1340/1879] lr: 2.0000e-02 eta: 12:02:47 time: 0.3198 data_time: 0.0145 memory: 6717 grad_norm: 2.6710 loss: 1.4804 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4804 2023/04/14 03:53:09 - mmengine - INFO - Epoch(train) [38][1360/1879] lr: 2.0000e-02 eta: 12:02:42 time: 0.4182 data_time: 0.0152 memory: 6717 grad_norm: 2.7550 loss: 1.5624 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5624 2023/04/14 03:53:16 - mmengine - INFO - Epoch(train) [38][1380/1879] lr: 2.0000e-02 eta: 12:02:32 time: 0.3142 data_time: 0.0130 memory: 6717 grad_norm: 2.7726 loss: 1.6112 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6112 2023/04/14 03:53:24 - mmengine - INFO - Epoch(train) [38][1400/1879] lr: 2.0000e-02 eta: 12:02:26 time: 0.4059 data_time: 0.0133 memory: 6717 grad_norm: 2.7000 loss: 1.7791 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7791 2023/04/14 03:53:30 - mmengine - INFO - Epoch(train) [38][1420/1879] lr: 2.0000e-02 eta: 12:02:17 time: 0.3072 data_time: 0.0152 memory: 6717 grad_norm: 2.7289 loss: 1.5415 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5415 2023/04/14 03:53:38 - mmengine - INFO - Epoch(train) [38][1440/1879] lr: 2.0000e-02 eta: 12:02:11 time: 0.4261 data_time: 0.0129 memory: 6717 grad_norm: 2.6952 loss: 1.7185 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.7185 2023/04/14 03:53:45 - mmengine - INFO - Epoch(train) [38][1460/1879] lr: 2.0000e-02 eta: 12:02:03 time: 0.3398 data_time: 0.0156 memory: 6717 grad_norm: 2.7217 loss: 1.5565 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.5565 2023/04/14 03:53:51 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 03:53:53 - mmengine - INFO - Epoch(train) [38][1480/1879] lr: 2.0000e-02 eta: 12:01:56 time: 0.3825 data_time: 0.0132 memory: 6717 grad_norm: 2.6536 loss: 1.4890 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4890 2023/04/14 03:54:00 - mmengine - INFO - Epoch(train) [38][1500/1879] lr: 2.0000e-02 eta: 12:01:47 time: 0.3365 data_time: 0.0144 memory: 6717 grad_norm: 2.6716 loss: 1.4288 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4288 2023/04/14 03:54:07 - mmengine - INFO - Epoch(train) [38][1520/1879] lr: 2.0000e-02 eta: 12:01:40 time: 0.3808 data_time: 0.0138 memory: 6717 grad_norm: 2.6643 loss: 1.4297 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4297 2023/04/14 03:54:15 - mmengine - INFO - Epoch(train) [38][1540/1879] lr: 2.0000e-02 eta: 12:01:32 time: 0.3696 data_time: 0.0139 memory: 6717 grad_norm: 2.7042 loss: 1.7884 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.7884 2023/04/14 03:54:23 - mmengine - INFO - Epoch(train) [38][1560/1879] lr: 2.0000e-02 eta: 12:01:26 time: 0.4020 data_time: 0.0142 memory: 6717 grad_norm: 2.6851 loss: 1.8025 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.8025 2023/04/14 03:54:29 - mmengine - INFO - Epoch(train) [38][1580/1879] lr: 2.0000e-02 eta: 12:01:18 time: 0.3439 data_time: 0.0136 memory: 6717 grad_norm: 2.6070 loss: 1.7170 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7170 2023/04/14 03:54:37 - mmengine - INFO - Epoch(train) [38][1600/1879] lr: 2.0000e-02 eta: 12:01:11 time: 0.3804 data_time: 0.0350 memory: 6717 grad_norm: 2.7121 loss: 1.5215 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5215 2023/04/14 03:54:44 - mmengine - INFO - Epoch(train) [38][1620/1879] lr: 2.0000e-02 eta: 12:01:02 time: 0.3340 data_time: 0.0325 memory: 6717 grad_norm: 2.6714 loss: 1.6205 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6205 2023/04/14 03:54:52 - mmengine - INFO - Epoch(train) [38][1640/1879] lr: 2.0000e-02 eta: 12:00:56 time: 0.3995 data_time: 0.0282 memory: 6717 grad_norm: 2.7283 loss: 1.5363 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5363 2023/04/14 03:54:59 - mmengine - INFO - Epoch(train) [38][1660/1879] lr: 2.0000e-02 eta: 12:00:48 time: 0.3655 data_time: 0.0574 memory: 6717 grad_norm: 2.6841 loss: 1.6003 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6003 2023/04/14 03:55:06 - mmengine - INFO - Epoch(train) [38][1680/1879] lr: 2.0000e-02 eta: 12:00:40 time: 0.3632 data_time: 0.0598 memory: 6717 grad_norm: 2.7306 loss: 1.6511 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6511 2023/04/14 03:55:14 - mmengine - INFO - Epoch(train) [38][1700/1879] lr: 2.0000e-02 eta: 12:00:34 time: 0.4079 data_time: 0.0119 memory: 6717 grad_norm: 2.6922 loss: 1.4746 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4746 2023/04/14 03:55:22 - mmengine - INFO - Epoch(train) [38][1720/1879] lr: 2.0000e-02 eta: 12:00:26 time: 0.3572 data_time: 0.0186 memory: 6717 grad_norm: 2.6840 loss: 1.9968 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9968 2023/04/14 03:55:30 - mmengine - INFO - Epoch(train) [38][1740/1879] lr: 2.0000e-02 eta: 12:00:20 time: 0.4004 data_time: 0.0137 memory: 6717 grad_norm: 2.7060 loss: 1.5852 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.5852 2023/04/14 03:55:36 - mmengine - INFO - Epoch(train) [38][1760/1879] lr: 2.0000e-02 eta: 12:00:11 time: 0.3177 data_time: 0.0289 memory: 6717 grad_norm: 2.6760 loss: 1.6125 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6125 2023/04/14 03:55:44 - mmengine - INFO - Epoch(train) [38][1780/1879] lr: 2.0000e-02 eta: 12:00:04 time: 0.4031 data_time: 0.0487 memory: 6717 grad_norm: 2.6288 loss: 1.4860 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4860 2023/04/14 03:55:51 - mmengine - INFO - Epoch(train) [38][1800/1879] lr: 2.0000e-02 eta: 11:59:57 time: 0.3686 data_time: 0.1473 memory: 6717 grad_norm: 2.6797 loss: 1.6524 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6524 2023/04/14 03:55:59 - mmengine - INFO - Epoch(train) [38][1820/1879] lr: 2.0000e-02 eta: 11:59:50 time: 0.3889 data_time: 0.0331 memory: 6717 grad_norm: 2.7342 loss: 1.5949 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5949 2023/04/14 03:56:06 - mmengine - INFO - Epoch(train) [38][1840/1879] lr: 2.0000e-02 eta: 11:59:41 time: 0.3256 data_time: 0.0241 memory: 6717 grad_norm: 2.7349 loss: 1.7970 top1_acc: 0.3750 top5_acc: 0.9375 loss_cls: 1.7970 2023/04/14 03:56:14 - mmengine - INFO - Epoch(train) [38][1860/1879] lr: 2.0000e-02 eta: 11:59:35 time: 0.4181 data_time: 0.0122 memory: 6717 grad_norm: 2.7104 loss: 1.5444 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5444 2023/04/14 03:56:20 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 03:56:20 - mmengine - INFO - Epoch(train) [38][1879/1879] lr: 2.0000e-02 eta: 11:59:27 time: 0.3137 data_time: 0.0131 memory: 6717 grad_norm: 2.6942 loss: 1.6617 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.6617 2023/04/14 03:56:29 - mmengine - INFO - Epoch(val) [38][ 20/155] eta: 0:01:00 time: 0.4448 data_time: 0.4118 memory: 1391 2023/04/14 03:56:36 - mmengine - INFO - Epoch(val) [38][ 40/155] eta: 0:00:43 time: 0.3193 data_time: 0.2858 memory: 1391 2023/04/14 03:56:44 - mmengine - INFO - Epoch(val) [38][ 60/155] eta: 0:00:38 time: 0.4387 data_time: 0.4060 memory: 1391 2023/04/14 03:56:51 - mmengine - INFO - Epoch(val) [38][ 80/155] eta: 0:00:28 time: 0.3161 data_time: 0.2834 memory: 1391 2023/04/14 03:57:00 - mmengine - INFO - Epoch(val) [38][100/155] eta: 0:00:21 time: 0.4531 data_time: 0.4202 memory: 1391 2023/04/14 03:57:06 - mmengine - INFO - Epoch(val) [38][120/155] eta: 0:00:13 time: 0.3036 data_time: 0.2711 memory: 1391 2023/04/14 03:57:16 - mmengine - INFO - Epoch(val) [38][140/155] eta: 0:00:05 time: 0.4899 data_time: 0.4569 memory: 1391 2023/04/14 03:57:23 - mmengine - INFO - Epoch(val) [38][155/155] acc/top1: 0.6087 acc/top5: 0.8390 acc/mean1: 0.6086 data_time: 0.4225 time: 0.4547 2023/04/14 03:57:33 - mmengine - INFO - Epoch(train) [39][ 20/1879] lr: 2.0000e-02 eta: 11:59:24 time: 0.5010 data_time: 0.2098 memory: 6717 grad_norm: 2.7227 loss: 1.9119 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.9119 2023/04/14 03:57:39 - mmengine - INFO - Epoch(train) [39][ 40/1879] lr: 2.0000e-02 eta: 11:59:15 time: 0.3275 data_time: 0.0128 memory: 6717 grad_norm: 2.6807 loss: 1.5947 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5947 2023/04/14 03:57:48 - mmengine - INFO - Epoch(train) [39][ 60/1879] lr: 2.0000e-02 eta: 11:59:09 time: 0.4126 data_time: 0.0159 memory: 6717 grad_norm: 2.7492 loss: 1.4990 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.4990 2023/04/14 03:57:54 - mmengine - INFO - Epoch(train) [39][ 80/1879] lr: 2.0000e-02 eta: 11:59:00 time: 0.3334 data_time: 0.0141 memory: 6717 grad_norm: 2.7514 loss: 1.3977 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3977 2023/04/14 03:58:02 - mmengine - INFO - Epoch(train) [39][ 100/1879] lr: 2.0000e-02 eta: 11:58:54 time: 0.4020 data_time: 0.0183 memory: 6717 grad_norm: 2.7153 loss: 1.5849 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5849 2023/04/14 03:58:09 - mmengine - INFO - Epoch(train) [39][ 120/1879] lr: 2.0000e-02 eta: 11:58:45 time: 0.3137 data_time: 0.0132 memory: 6717 grad_norm: 2.8204 loss: 1.6761 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.6761 2023/04/14 03:58:17 - mmengine - INFO - Epoch(train) [39][ 140/1879] lr: 2.0000e-02 eta: 11:58:39 time: 0.4231 data_time: 0.0160 memory: 6717 grad_norm: 2.7913 loss: 1.6474 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6474 2023/04/14 03:58:24 - mmengine - INFO - Epoch(train) [39][ 160/1879] lr: 2.0000e-02 eta: 11:58:31 time: 0.3509 data_time: 0.0133 memory: 6717 grad_norm: 2.7059 loss: 1.7071 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7071 2023/04/14 03:58:32 - mmengine - INFO - Epoch(train) [39][ 180/1879] lr: 2.0000e-02 eta: 11:58:24 time: 0.3852 data_time: 0.0160 memory: 6717 grad_norm: 2.7690 loss: 1.5713 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5713 2023/04/14 03:58:37 - mmengine - INFO - Epoch(train) [39][ 200/1879] lr: 2.0000e-02 eta: 11:58:14 time: 0.2861 data_time: 0.0274 memory: 6717 grad_norm: 2.7012 loss: 1.6760 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6760 2023/04/14 03:58:45 - mmengine - INFO - Epoch(train) [39][ 220/1879] lr: 2.0000e-02 eta: 11:58:07 time: 0.3984 data_time: 0.0159 memory: 6717 grad_norm: 2.7665 loss: 1.5196 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5196 2023/04/14 03:58:52 - mmengine - INFO - Epoch(train) [39][ 240/1879] lr: 2.0000e-02 eta: 11:57:58 time: 0.3215 data_time: 0.0129 memory: 6717 grad_norm: 2.6818 loss: 1.5476 top1_acc: 0.3125 top5_acc: 0.8750 loss_cls: 1.5476 2023/04/14 03:59:00 - mmengine - INFO - Epoch(train) [39][ 260/1879] lr: 2.0000e-02 eta: 11:57:52 time: 0.4150 data_time: 0.0265 memory: 6717 grad_norm: 2.7753 loss: 1.6509 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6509 2023/04/14 03:59:07 - mmengine - INFO - Epoch(train) [39][ 280/1879] lr: 2.0000e-02 eta: 11:57:44 time: 0.3343 data_time: 0.0137 memory: 6717 grad_norm: 2.7997 loss: 1.5428 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5428 2023/04/14 03:59:15 - mmengine - INFO - Epoch(train) [39][ 300/1879] lr: 2.0000e-02 eta: 11:57:38 time: 0.4144 data_time: 0.0196 memory: 6717 grad_norm: 2.7110 loss: 1.7348 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7348 2023/04/14 03:59:22 - mmengine - INFO - Epoch(train) [39][ 320/1879] lr: 2.0000e-02 eta: 11:57:30 time: 0.3500 data_time: 0.0898 memory: 6717 grad_norm: 2.6826 loss: 1.5558 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5558 2023/04/14 03:59:29 - mmengine - INFO - Epoch(train) [39][ 340/1879] lr: 2.0000e-02 eta: 11:57:22 time: 0.3552 data_time: 0.1291 memory: 6717 grad_norm: 2.6924 loss: 1.5217 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.5217 2023/04/14 03:59:38 - mmengine - INFO - Epoch(train) [39][ 360/1879] lr: 2.0000e-02 eta: 11:57:16 time: 0.4119 data_time: 0.2420 memory: 6717 grad_norm: 2.7737 loss: 1.6120 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6120 2023/04/14 03:59:45 - mmengine - INFO - Epoch(train) [39][ 380/1879] lr: 2.0000e-02 eta: 11:57:08 time: 0.3532 data_time: 0.1053 memory: 6717 grad_norm: 2.7482 loss: 1.5598 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.5598 2023/04/14 03:59:52 - mmengine - INFO - Epoch(train) [39][ 400/1879] lr: 2.0000e-02 eta: 11:57:00 time: 0.3672 data_time: 0.1799 memory: 6717 grad_norm: 2.7537 loss: 1.5871 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.5871 2023/04/14 03:59:59 - mmengine - INFO - Epoch(train) [39][ 420/1879] lr: 2.0000e-02 eta: 11:56:52 time: 0.3461 data_time: 0.1557 memory: 6717 grad_norm: 2.7138 loss: 1.7234 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.7234 2023/04/14 04:00:07 - mmengine - INFO - Epoch(train) [39][ 440/1879] lr: 2.0000e-02 eta: 11:56:45 time: 0.3931 data_time: 0.2243 memory: 6717 grad_norm: 2.7372 loss: 1.6496 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6496 2023/04/14 04:00:14 - mmengine - INFO - Epoch(train) [39][ 460/1879] lr: 2.0000e-02 eta: 11:56:37 time: 0.3586 data_time: 0.0987 memory: 6717 grad_norm: 2.8281 loss: 1.6558 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6558 2023/04/14 04:00:22 - mmengine - INFO - Epoch(train) [39][ 480/1879] lr: 2.0000e-02 eta: 11:56:31 time: 0.3930 data_time: 0.1742 memory: 6717 grad_norm: 2.6513 loss: 1.6479 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.6479 2023/04/14 04:00:28 - mmengine - INFO - Epoch(train) [39][ 500/1879] lr: 2.0000e-02 eta: 11:56:22 time: 0.3268 data_time: 0.1322 memory: 6717 grad_norm: 2.6613 loss: 1.7343 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.7343 2023/04/14 04:00:36 - mmengine - INFO - Epoch(train) [39][ 520/1879] lr: 2.0000e-02 eta: 11:56:15 time: 0.3966 data_time: 0.2141 memory: 6717 grad_norm: 2.7594 loss: 1.6630 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6630 2023/04/14 04:00:43 - mmengine - INFO - Epoch(train) [39][ 540/1879] lr: 2.0000e-02 eta: 11:56:07 time: 0.3438 data_time: 0.1754 memory: 6717 grad_norm: 2.6563 loss: 1.7940 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7940 2023/04/14 04:00:51 - mmengine - INFO - Epoch(train) [39][ 560/1879] lr: 2.0000e-02 eta: 11:56:00 time: 0.3827 data_time: 0.0992 memory: 6717 grad_norm: 2.7311 loss: 1.5693 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.5693 2023/04/14 04:00:58 - mmengine - INFO - Epoch(train) [39][ 580/1879] lr: 2.0000e-02 eta: 11:55:52 time: 0.3384 data_time: 0.0883 memory: 6717 grad_norm: 2.7312 loss: 1.6589 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6589 2023/04/14 04:01:05 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 04:01:06 - mmengine - INFO - Epoch(train) [39][ 600/1879] lr: 2.0000e-02 eta: 11:55:46 time: 0.4214 data_time: 0.1151 memory: 6717 grad_norm: 2.7448 loss: 1.7166 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7166 2023/04/14 04:01:13 - mmengine - INFO - Epoch(train) [39][ 620/1879] lr: 2.0000e-02 eta: 11:55:37 time: 0.3384 data_time: 0.0531 memory: 6717 grad_norm: 2.7094 loss: 1.5748 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5748 2023/04/14 04:01:20 - mmengine - INFO - Epoch(train) [39][ 640/1879] lr: 2.0000e-02 eta: 11:55:30 time: 0.3547 data_time: 0.0228 memory: 6717 grad_norm: 2.7425 loss: 1.7130 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7130 2023/04/14 04:01:27 - mmengine - INFO - Epoch(train) [39][ 660/1879] lr: 2.0000e-02 eta: 11:55:22 time: 0.3662 data_time: 0.0324 memory: 6717 grad_norm: 2.7503 loss: 1.6169 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.6169 2023/04/14 04:01:36 - mmengine - INFO - Epoch(train) [39][ 680/1879] lr: 2.0000e-02 eta: 11:55:16 time: 0.4237 data_time: 0.0599 memory: 6717 grad_norm: 2.7528 loss: 1.6225 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.6225 2023/04/14 04:01:42 - mmengine - INFO - Epoch(train) [39][ 700/1879] lr: 2.0000e-02 eta: 11:55:08 time: 0.3417 data_time: 0.0186 memory: 6717 grad_norm: 2.7624 loss: 1.6971 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6971 2023/04/14 04:01:51 - mmengine - INFO - Epoch(train) [39][ 720/1879] lr: 2.0000e-02 eta: 11:55:02 time: 0.4142 data_time: 0.0388 memory: 6717 grad_norm: 2.6856 loss: 1.6039 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6039 2023/04/14 04:01:57 - mmengine - INFO - Epoch(train) [39][ 740/1879] lr: 2.0000e-02 eta: 11:54:53 time: 0.3350 data_time: 0.0135 memory: 6717 grad_norm: 2.6453 loss: 1.8272 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.8272 2023/04/14 04:02:06 - mmengine - INFO - Epoch(train) [39][ 760/1879] lr: 2.0000e-02 eta: 11:54:48 time: 0.4289 data_time: 0.0135 memory: 6717 grad_norm: 2.6542 loss: 1.7082 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7082 2023/04/14 04:02:13 - mmengine - INFO - Epoch(train) [39][ 780/1879] lr: 2.0000e-02 eta: 11:54:39 time: 0.3377 data_time: 0.0137 memory: 6717 grad_norm: 2.6580 loss: 1.6038 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6038 2023/04/14 04:02:21 - mmengine - INFO - Epoch(train) [39][ 800/1879] lr: 2.0000e-02 eta: 11:54:33 time: 0.4023 data_time: 0.0147 memory: 6717 grad_norm: 2.7579 loss: 1.4594 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4594 2023/04/14 04:02:27 - mmengine - INFO - Epoch(train) [39][ 820/1879] lr: 2.0000e-02 eta: 11:54:24 time: 0.3262 data_time: 0.0143 memory: 6717 grad_norm: 2.8027 loss: 1.6759 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6759 2023/04/14 04:02:36 - mmengine - INFO - Epoch(train) [39][ 840/1879] lr: 2.0000e-02 eta: 11:54:18 time: 0.4236 data_time: 0.0139 memory: 6717 grad_norm: 2.7197 loss: 1.7364 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7364 2023/04/14 04:02:42 - mmengine - INFO - Epoch(train) [39][ 860/1879] lr: 2.0000e-02 eta: 11:54:10 time: 0.3238 data_time: 0.0148 memory: 6717 grad_norm: 2.7031 loss: 1.7373 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7373 2023/04/14 04:02:50 - mmengine - INFO - Epoch(train) [39][ 880/1879] lr: 2.0000e-02 eta: 11:54:03 time: 0.3955 data_time: 0.0156 memory: 6717 grad_norm: 2.6944 loss: 1.5955 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5955 2023/04/14 04:02:57 - mmengine - INFO - Epoch(train) [39][ 900/1879] lr: 2.0000e-02 eta: 11:53:55 time: 0.3514 data_time: 0.0147 memory: 6717 grad_norm: 2.7737 loss: 1.7107 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.7107 2023/04/14 04:03:05 - mmengine - INFO - Epoch(train) [39][ 920/1879] lr: 2.0000e-02 eta: 11:53:49 time: 0.4062 data_time: 0.0137 memory: 6717 grad_norm: 2.6783 loss: 1.5799 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5799 2023/04/14 04:03:11 - mmengine - INFO - Epoch(train) [39][ 940/1879] lr: 2.0000e-02 eta: 11:53:39 time: 0.3040 data_time: 0.0149 memory: 6717 grad_norm: 2.7127 loss: 1.8839 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.8839 2023/04/14 04:03:19 - mmengine - INFO - Epoch(train) [39][ 960/1879] lr: 2.0000e-02 eta: 11:53:32 time: 0.3809 data_time: 0.0153 memory: 6717 grad_norm: 2.6653 loss: 1.6414 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6414 2023/04/14 04:03:26 - mmengine - INFO - Epoch(train) [39][ 980/1879] lr: 2.0000e-02 eta: 11:53:24 time: 0.3491 data_time: 0.0139 memory: 6717 grad_norm: 2.6453 loss: 1.6852 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.6852 2023/04/14 04:03:34 - mmengine - INFO - Epoch(train) [39][1000/1879] lr: 2.0000e-02 eta: 11:53:18 time: 0.4049 data_time: 0.0151 memory: 6717 grad_norm: 2.6834 loss: 1.6017 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6017 2023/04/14 04:03:41 - mmengine - INFO - Epoch(train) [39][1020/1879] lr: 2.0000e-02 eta: 11:53:09 time: 0.3221 data_time: 0.0124 memory: 6717 grad_norm: 2.6988 loss: 1.5002 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5002 2023/04/14 04:03:49 - mmengine - INFO - Epoch(train) [39][1040/1879] lr: 2.0000e-02 eta: 11:53:02 time: 0.3957 data_time: 0.0162 memory: 6717 grad_norm: 2.7248 loss: 1.6385 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6385 2023/04/14 04:03:55 - mmengine - INFO - Epoch(train) [39][1060/1879] lr: 2.0000e-02 eta: 11:52:53 time: 0.3124 data_time: 0.0128 memory: 6717 grad_norm: 2.6237 loss: 1.4591 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4591 2023/04/14 04:04:04 - mmengine - INFO - Epoch(train) [39][1080/1879] lr: 2.0000e-02 eta: 11:52:48 time: 0.4485 data_time: 0.1316 memory: 6717 grad_norm: 2.7889 loss: 1.5709 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5709 2023/04/14 04:04:10 - mmengine - INFO - Epoch(train) [39][1100/1879] lr: 2.0000e-02 eta: 11:52:39 time: 0.3191 data_time: 0.0547 memory: 6717 grad_norm: 2.7339 loss: 1.5981 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5981 2023/04/14 04:04:19 - mmengine - INFO - Epoch(train) [39][1120/1879] lr: 2.0000e-02 eta: 11:52:33 time: 0.4185 data_time: 0.1799 memory: 6717 grad_norm: 2.6718 loss: 1.6843 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6843 2023/04/14 04:04:25 - mmengine - INFO - Epoch(train) [39][1140/1879] lr: 2.0000e-02 eta: 11:52:25 time: 0.3402 data_time: 0.1601 memory: 6717 grad_norm: 2.6702 loss: 1.5534 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5534 2023/04/14 04:04:33 - mmengine - INFO - Epoch(train) [39][1160/1879] lr: 2.0000e-02 eta: 11:52:18 time: 0.3966 data_time: 0.1945 memory: 6717 grad_norm: 2.6062 loss: 1.5749 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5749 2023/04/14 04:04:40 - mmengine - INFO - Epoch(train) [39][1180/1879] lr: 2.0000e-02 eta: 11:52:09 time: 0.3188 data_time: 0.1344 memory: 6717 grad_norm: 2.6402 loss: 1.4884 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4884 2023/04/14 04:04:48 - mmengine - INFO - Epoch(train) [39][1200/1879] lr: 2.0000e-02 eta: 11:52:02 time: 0.3998 data_time: 0.1937 memory: 6717 grad_norm: 2.6632 loss: 1.5758 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5758 2023/04/14 04:04:54 - mmengine - INFO - Epoch(train) [39][1220/1879] lr: 2.0000e-02 eta: 11:51:53 time: 0.3122 data_time: 0.1401 memory: 6717 grad_norm: 2.7442 loss: 1.6770 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.6770 2023/04/14 04:05:02 - mmengine - INFO - Epoch(train) [39][1240/1879] lr: 2.0000e-02 eta: 11:51:47 time: 0.3990 data_time: 0.1540 memory: 6717 grad_norm: 2.6580 loss: 1.5650 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5650 2023/04/14 04:05:08 - mmengine - INFO - Epoch(train) [39][1260/1879] lr: 2.0000e-02 eta: 11:51:38 time: 0.3223 data_time: 0.0916 memory: 6717 grad_norm: 2.6789 loss: 1.5385 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5385 2023/04/14 04:05:17 - mmengine - INFO - Epoch(train) [39][1280/1879] lr: 2.0000e-02 eta: 11:51:32 time: 0.4117 data_time: 0.1504 memory: 6717 grad_norm: 2.6413 loss: 1.6367 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6367 2023/04/14 04:05:24 - mmengine - INFO - Epoch(train) [39][1300/1879] lr: 2.0000e-02 eta: 11:51:24 time: 0.3558 data_time: 0.1267 memory: 6717 grad_norm: 2.7613 loss: 1.6358 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6358 2023/04/14 04:05:32 - mmengine - INFO - Epoch(train) [39][1320/1879] lr: 2.0000e-02 eta: 11:51:18 time: 0.4194 data_time: 0.1969 memory: 6717 grad_norm: 2.6040 loss: 1.7422 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7422 2023/04/14 04:05:39 - mmengine - INFO - Epoch(train) [39][1340/1879] lr: 2.0000e-02 eta: 11:51:09 time: 0.3250 data_time: 0.0784 memory: 6717 grad_norm: 2.7498 loss: 1.6245 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.6245 2023/04/14 04:05:47 - mmengine - INFO - Epoch(train) [39][1360/1879] lr: 2.0000e-02 eta: 11:51:03 time: 0.4131 data_time: 0.0678 memory: 6717 grad_norm: 2.5888 loss: 1.6098 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6098 2023/04/14 04:05:53 - mmengine - INFO - Epoch(train) [39][1380/1879] lr: 2.0000e-02 eta: 11:50:54 time: 0.3162 data_time: 0.0493 memory: 6717 grad_norm: 2.6735 loss: 1.6729 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6729 2023/04/14 04:06:01 - mmengine - INFO - Epoch(train) [39][1400/1879] lr: 2.0000e-02 eta: 11:50:48 time: 0.4065 data_time: 0.0256 memory: 6717 grad_norm: 2.6860 loss: 1.5883 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5883 2023/04/14 04:06:08 - mmengine - INFO - Epoch(train) [39][1420/1879] lr: 2.0000e-02 eta: 11:50:39 time: 0.3375 data_time: 0.0829 memory: 6717 grad_norm: 2.6862 loss: 1.5628 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.5628 2023/04/14 04:06:15 - mmengine - INFO - Epoch(train) [39][1440/1879] lr: 2.0000e-02 eta: 11:50:32 time: 0.3682 data_time: 0.1089 memory: 6717 grad_norm: 2.6087 loss: 1.4766 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4766 2023/04/14 04:06:23 - mmengine - INFO - Epoch(train) [39][1460/1879] lr: 2.0000e-02 eta: 11:50:24 time: 0.3652 data_time: 0.0573 memory: 6717 grad_norm: 2.6837 loss: 1.6599 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.6599 2023/04/14 04:06:31 - mmengine - INFO - Epoch(train) [39][1480/1879] lr: 2.0000e-02 eta: 11:50:18 time: 0.4004 data_time: 0.0269 memory: 6717 grad_norm: 2.6908 loss: 1.4102 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4102 2023/04/14 04:06:37 - mmengine - INFO - Epoch(train) [39][1500/1879] lr: 2.0000e-02 eta: 11:50:08 time: 0.3042 data_time: 0.0187 memory: 6717 grad_norm: 2.6417 loss: 1.7237 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.7237 2023/04/14 04:06:45 - mmengine - INFO - Epoch(train) [39][1520/1879] lr: 2.0000e-02 eta: 11:50:02 time: 0.4255 data_time: 0.0727 memory: 6717 grad_norm: 2.7738 loss: 1.7097 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7097 2023/04/14 04:06:52 - mmengine - INFO - Epoch(train) [39][1540/1879] lr: 2.0000e-02 eta: 11:49:54 time: 0.3416 data_time: 0.1280 memory: 6717 grad_norm: 2.6680 loss: 1.5531 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5531 2023/04/14 04:07:00 - mmengine - INFO - Epoch(train) [39][1560/1879] lr: 2.0000e-02 eta: 11:49:47 time: 0.3843 data_time: 0.1343 memory: 6717 grad_norm: 2.6675 loss: 1.9381 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.9381 2023/04/14 04:07:07 - mmengine - INFO - Epoch(train) [39][1580/1879] lr: 2.0000e-02 eta: 11:49:39 time: 0.3605 data_time: 0.0743 memory: 6717 grad_norm: 2.7304 loss: 1.5994 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5994 2023/04/14 04:07:14 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 04:07:15 - mmengine - INFO - Epoch(train) [39][1600/1879] lr: 2.0000e-02 eta: 11:49:33 time: 0.3931 data_time: 0.0790 memory: 6717 grad_norm: 2.7112 loss: 1.5652 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.5652 2023/04/14 04:07:22 - mmengine - INFO - Epoch(train) [39][1620/1879] lr: 2.0000e-02 eta: 11:49:24 time: 0.3357 data_time: 0.0863 memory: 6717 grad_norm: 2.6695 loss: 1.5648 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5648 2023/04/14 04:07:29 - mmengine - INFO - Epoch(train) [39][1640/1879] lr: 2.0000e-02 eta: 11:49:17 time: 0.3645 data_time: 0.1829 memory: 6717 grad_norm: 2.6229 loss: 1.6095 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6095 2023/04/14 04:07:36 - mmengine - INFO - Epoch(train) [39][1660/1879] lr: 2.0000e-02 eta: 11:49:08 time: 0.3417 data_time: 0.0921 memory: 6717 grad_norm: 2.6713 loss: 1.6282 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6282 2023/04/14 04:07:44 - mmengine - INFO - Epoch(train) [39][1680/1879] lr: 2.0000e-02 eta: 11:49:02 time: 0.3962 data_time: 0.1010 memory: 6717 grad_norm: 2.6760 loss: 1.7108 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7108 2023/04/14 04:07:51 - mmengine - INFO - Epoch(train) [39][1700/1879] lr: 2.0000e-02 eta: 11:48:54 time: 0.3643 data_time: 0.0391 memory: 6717 grad_norm: 2.7590 loss: 1.5804 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 1.5804 2023/04/14 04:07:58 - mmengine - INFO - Epoch(train) [39][1720/1879] lr: 2.0000e-02 eta: 11:48:46 time: 0.3658 data_time: 0.0153 memory: 6717 grad_norm: 2.7581 loss: 1.6607 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6607 2023/04/14 04:08:07 - mmengine - INFO - Epoch(train) [39][1740/1879] lr: 2.0000e-02 eta: 11:48:41 time: 0.4366 data_time: 0.0133 memory: 6717 grad_norm: 2.7397 loss: 1.6888 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6888 2023/04/14 04:08:13 - mmengine - INFO - Epoch(train) [39][1760/1879] lr: 2.0000e-02 eta: 11:48:32 time: 0.3106 data_time: 0.0136 memory: 6717 grad_norm: 2.6317 loss: 1.5944 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5944 2023/04/14 04:08:21 - mmengine - INFO - Epoch(train) [39][1780/1879] lr: 2.0000e-02 eta: 11:48:25 time: 0.3889 data_time: 0.0132 memory: 6717 grad_norm: 2.6318 loss: 1.7226 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7226 2023/04/14 04:08:28 - mmengine - INFO - Epoch(train) [39][1800/1879] lr: 2.0000e-02 eta: 11:48:16 time: 0.3228 data_time: 0.0624 memory: 6717 grad_norm: 2.7435 loss: 1.6322 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.6322 2023/04/14 04:08:34 - mmengine - INFO - Epoch(train) [39][1820/1879] lr: 2.0000e-02 eta: 11:48:08 time: 0.3427 data_time: 0.0719 memory: 6717 grad_norm: 2.6431 loss: 1.6112 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6112 2023/04/14 04:08:43 - mmengine - INFO - Epoch(train) [39][1840/1879] lr: 2.0000e-02 eta: 11:48:02 time: 0.4225 data_time: 0.0286 memory: 6717 grad_norm: 2.6600 loss: 1.7174 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.7174 2023/04/14 04:08:49 - mmengine - INFO - Epoch(train) [39][1860/1879] lr: 2.0000e-02 eta: 11:47:53 time: 0.3206 data_time: 0.0135 memory: 6717 grad_norm: 2.6988 loss: 1.5884 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5884 2023/04/14 04:08:58 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 04:08:58 - mmengine - INFO - Epoch(train) [39][1879/1879] lr: 2.0000e-02 eta: 11:47:48 time: 0.4265 data_time: 0.0125 memory: 6717 grad_norm: 2.6774 loss: 1.8569 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.8569 2023/04/14 04:08:58 - mmengine - INFO - Saving checkpoint at 39 epochs 2023/04/14 04:09:07 - mmengine - INFO - Epoch(val) [39][ 20/155] eta: 0:01:02 time: 0.4605 data_time: 0.4269 memory: 1391 2023/04/14 04:09:14 - mmengine - INFO - Epoch(val) [39][ 40/155] eta: 0:00:44 time: 0.3078 data_time: 0.2751 memory: 1391 2023/04/14 04:09:22 - mmengine - INFO - Epoch(val) [39][ 60/155] eta: 0:00:38 time: 0.4332 data_time: 0.3997 memory: 1391 2023/04/14 04:09:28 - mmengine - INFO - Epoch(val) [39][ 80/155] eta: 0:00:28 time: 0.3136 data_time: 0.2807 memory: 1391 2023/04/14 04:09:37 - mmengine - INFO - Epoch(val) [39][100/155] eta: 0:00:21 time: 0.4302 data_time: 0.3971 memory: 1391 2023/04/14 04:09:44 - mmengine - INFO - Epoch(val) [39][120/155] eta: 0:00:13 time: 0.3379 data_time: 0.3052 memory: 1391 2023/04/14 04:09:54 - mmengine - INFO - Epoch(val) [39][140/155] eta: 0:00:05 time: 0.4862 data_time: 0.4534 memory: 1391 2023/04/14 04:10:01 - mmengine - INFO - Epoch(val) [39][155/155] acc/top1: 0.6154 acc/top5: 0.8436 acc/mean1: 0.6153 data_time: 0.4175 time: 0.4502 2023/04/14 04:10:01 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_37.pth is removed 2023/04/14 04:10:01 - mmengine - INFO - The best checkpoint with 0.6154 acc/top1 at 39 epoch is saved to best_acc_top1_epoch_39.pth. 2023/04/14 04:10:11 - mmengine - INFO - Epoch(train) [40][ 20/1879] lr: 2.0000e-02 eta: 11:47:44 time: 0.4770 data_time: 0.3402 memory: 6717 grad_norm: 2.7376 loss: 1.7374 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.7374 2023/04/14 04:10:17 - mmengine - INFO - Epoch(train) [40][ 40/1879] lr: 2.0000e-02 eta: 11:47:35 time: 0.3175 data_time: 0.1866 memory: 6717 grad_norm: 2.7282 loss: 1.6803 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6803 2023/04/14 04:10:25 - mmengine - INFO - Epoch(train) [40][ 60/1879] lr: 2.0000e-02 eta: 11:47:29 time: 0.4213 data_time: 0.2043 memory: 6717 grad_norm: 3.2820 loss: 1.7686 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.7686 2023/04/14 04:10:32 - mmengine - INFO - Epoch(train) [40][ 80/1879] lr: 2.0000e-02 eta: 11:47:20 time: 0.3239 data_time: 0.0926 memory: 6717 grad_norm: 2.7021 loss: 1.5397 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.5397 2023/04/14 04:10:40 - mmengine - INFO - Epoch(train) [40][ 100/1879] lr: 2.0000e-02 eta: 11:47:14 time: 0.4181 data_time: 0.1089 memory: 6717 grad_norm: 2.6174 loss: 1.5148 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5148 2023/04/14 04:10:47 - mmengine - INFO - Epoch(train) [40][ 120/1879] lr: 2.0000e-02 eta: 11:47:05 time: 0.3224 data_time: 0.1209 memory: 6717 grad_norm: 2.7390 loss: 1.5991 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.5991 2023/04/14 04:10:55 - mmengine - INFO - Epoch(train) [40][ 140/1879] lr: 2.0000e-02 eta: 11:47:00 time: 0.4319 data_time: 0.2270 memory: 6717 grad_norm: 2.6817 loss: 1.6223 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.6223 2023/04/14 04:11:02 - mmengine - INFO - Epoch(train) [40][ 160/1879] lr: 2.0000e-02 eta: 11:46:50 time: 0.3031 data_time: 0.0806 memory: 6717 grad_norm: 2.6571 loss: 1.4386 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.4386 2023/04/14 04:11:10 - mmengine - INFO - Epoch(train) [40][ 180/1879] lr: 2.0000e-02 eta: 11:46:44 time: 0.4056 data_time: 0.1847 memory: 6717 grad_norm: 2.6956 loss: 1.6659 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6659 2023/04/14 04:11:16 - mmengine - INFO - Epoch(train) [40][ 200/1879] lr: 2.0000e-02 eta: 11:46:35 time: 0.3295 data_time: 0.1106 memory: 6717 grad_norm: 2.7508 loss: 1.4597 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.4597 2023/04/14 04:11:25 - mmengine - INFO - Epoch(train) [40][ 220/1879] lr: 2.0000e-02 eta: 11:46:30 time: 0.4305 data_time: 0.2333 memory: 6717 grad_norm: 2.6829 loss: 1.6723 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.6723 2023/04/14 04:11:31 - mmengine - INFO - Epoch(train) [40][ 240/1879] lr: 2.0000e-02 eta: 11:46:21 time: 0.3165 data_time: 0.1774 memory: 6717 grad_norm: 2.7297 loss: 1.7644 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.7644 2023/04/14 04:11:39 - mmengine - INFO - Epoch(train) [40][ 260/1879] lr: 2.0000e-02 eta: 11:46:14 time: 0.4020 data_time: 0.2318 memory: 6717 grad_norm: 2.6906 loss: 1.6252 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.6252 2023/04/14 04:11:46 - mmengine - INFO - Epoch(train) [40][ 280/1879] lr: 2.0000e-02 eta: 11:46:06 time: 0.3372 data_time: 0.1890 memory: 6717 grad_norm: 2.6585 loss: 1.6908 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6908 2023/04/14 04:11:54 - mmengine - INFO - Epoch(train) [40][ 300/1879] lr: 2.0000e-02 eta: 11:45:59 time: 0.3931 data_time: 0.2547 memory: 6717 grad_norm: 2.7513 loss: 1.6800 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6800 2023/04/14 04:12:00 - mmengine - INFO - Epoch(train) [40][ 320/1879] lr: 2.0000e-02 eta: 11:45:50 time: 0.3264 data_time: 0.1702 memory: 6717 grad_norm: 2.6764 loss: 1.5296 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.5296 2023/04/14 04:12:08 - mmengine - INFO - Epoch(train) [40][ 340/1879] lr: 2.0000e-02 eta: 11:45:43 time: 0.3774 data_time: 0.1629 memory: 6717 grad_norm: 2.7107 loss: 1.6087 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.6087 2023/04/14 04:12:16 - mmengine - INFO - Epoch(train) [40][ 360/1879] lr: 2.0000e-02 eta: 11:45:36 time: 0.3820 data_time: 0.0197 memory: 6717 grad_norm: 2.7520 loss: 1.5743 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5743 2023/04/14 04:12:23 - mmengine - INFO - Epoch(train) [40][ 380/1879] lr: 2.0000e-02 eta: 11:45:28 time: 0.3547 data_time: 0.1018 memory: 6717 grad_norm: 2.7234 loss: 1.4374 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4374 2023/04/14 04:12:30 - mmengine - INFO - Epoch(train) [40][ 400/1879] lr: 2.0000e-02 eta: 11:45:21 time: 0.3764 data_time: 0.0986 memory: 6717 grad_norm: 2.6710 loss: 1.5219 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5219 2023/04/14 04:12:38 - mmengine - INFO - Epoch(train) [40][ 420/1879] lr: 2.0000e-02 eta: 11:45:14 time: 0.3851 data_time: 0.2368 memory: 6717 grad_norm: 2.6813 loss: 1.4846 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4846 2023/04/14 04:12:44 - mmengine - INFO - Epoch(train) [40][ 440/1879] lr: 2.0000e-02 eta: 11:45:05 time: 0.3295 data_time: 0.1891 memory: 6717 grad_norm: 2.6961 loss: 1.5287 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5287 2023/04/14 04:12:53 - mmengine - INFO - Epoch(train) [40][ 460/1879] lr: 2.0000e-02 eta: 11:44:59 time: 0.4116 data_time: 0.2562 memory: 6717 grad_norm: 2.7781 loss: 1.4811 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4811 2023/04/14 04:13:00 - mmengine - INFO - Epoch(train) [40][ 480/1879] lr: 2.0000e-02 eta: 11:44:51 time: 0.3513 data_time: 0.2135 memory: 6717 grad_norm: 2.6527 loss: 1.3888 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3888 2023/04/14 04:13:08 - mmengine - INFO - Epoch(train) [40][ 500/1879] lr: 2.0000e-02 eta: 11:44:45 time: 0.3978 data_time: 0.2581 memory: 6717 grad_norm: 2.7204 loss: 1.4726 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4726 2023/04/14 04:13:14 - mmengine - INFO - Epoch(train) [40][ 520/1879] lr: 2.0000e-02 eta: 11:44:35 time: 0.3063 data_time: 0.1659 memory: 6717 grad_norm: 2.7323 loss: 1.6939 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.6939 2023/04/14 04:13:22 - mmengine - INFO - Epoch(train) [40][ 540/1879] lr: 2.0000e-02 eta: 11:44:29 time: 0.3974 data_time: 0.1942 memory: 6717 grad_norm: 2.6510 loss: 1.4890 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4890 2023/04/14 04:13:29 - mmengine - INFO - Epoch(train) [40][ 560/1879] lr: 2.0000e-02 eta: 11:44:21 time: 0.3620 data_time: 0.1197 memory: 6717 grad_norm: 2.6938 loss: 1.6944 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6944 2023/04/14 04:13:37 - mmengine - INFO - Epoch(train) [40][ 580/1879] lr: 2.0000e-02 eta: 11:44:15 time: 0.4059 data_time: 0.2416 memory: 6717 grad_norm: 2.7075 loss: 1.4012 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.4012 2023/04/14 04:13:44 - mmengine - INFO - Epoch(train) [40][ 600/1879] lr: 2.0000e-02 eta: 11:44:07 time: 0.3516 data_time: 0.2124 memory: 6717 grad_norm: 2.7617 loss: 1.5838 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5838 2023/04/14 04:13:52 - mmengine - INFO - Epoch(train) [40][ 620/1879] lr: 2.0000e-02 eta: 11:44:00 time: 0.3918 data_time: 0.2559 memory: 6717 grad_norm: 2.6473 loss: 1.5286 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5286 2023/04/14 04:13:59 - mmengine - INFO - Epoch(train) [40][ 640/1879] lr: 2.0000e-02 eta: 11:43:51 time: 0.3288 data_time: 0.1583 memory: 6717 grad_norm: 2.7771 loss: 1.6346 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6346 2023/04/14 04:14:06 - mmengine - INFO - Epoch(train) [40][ 660/1879] lr: 2.0000e-02 eta: 11:43:45 time: 0.3952 data_time: 0.2424 memory: 6717 grad_norm: 2.5984 loss: 1.5988 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.5988 2023/04/14 04:14:13 - mmengine - INFO - Epoch(train) [40][ 680/1879] lr: 2.0000e-02 eta: 11:43:36 time: 0.3342 data_time: 0.1988 memory: 6717 grad_norm: 2.7808 loss: 1.4928 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4928 2023/04/14 04:14:21 - mmengine - INFO - Epoch(train) [40][ 700/1879] lr: 2.0000e-02 eta: 11:43:29 time: 0.3947 data_time: 0.2580 memory: 6717 grad_norm: 2.7189 loss: 1.5963 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.5963 2023/04/14 04:14:27 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 04:14:28 - mmengine - INFO - Epoch(train) [40][ 720/1879] lr: 2.0000e-02 eta: 11:43:21 time: 0.3269 data_time: 0.1904 memory: 6717 grad_norm: 2.6434 loss: 1.6399 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6399 2023/04/14 04:14:35 - mmengine - INFO - Epoch(train) [40][ 740/1879] lr: 2.0000e-02 eta: 11:43:13 time: 0.3741 data_time: 0.2201 memory: 6717 grad_norm: 2.7671 loss: 1.4633 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.4633 2023/04/14 04:14:43 - mmengine - INFO - Epoch(train) [40][ 760/1879] lr: 2.0000e-02 eta: 11:43:06 time: 0.3826 data_time: 0.0956 memory: 6717 grad_norm: 2.6893 loss: 1.4328 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4328 2023/04/14 04:14:49 - mmengine - INFO - Epoch(train) [40][ 780/1879] lr: 2.0000e-02 eta: 11:42:57 time: 0.3231 data_time: 0.0221 memory: 6717 grad_norm: 2.7190 loss: 1.7225 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.7225 2023/04/14 04:14:57 - mmengine - INFO - Epoch(train) [40][ 800/1879] lr: 2.0000e-02 eta: 11:42:51 time: 0.4122 data_time: 0.0150 memory: 6717 grad_norm: 2.7311 loss: 1.4780 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4780 2023/04/14 04:15:04 - mmengine - INFO - Epoch(train) [40][ 820/1879] lr: 2.0000e-02 eta: 11:42:43 time: 0.3407 data_time: 0.0140 memory: 6717 grad_norm: 2.6259 loss: 1.3792 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.3792 2023/04/14 04:15:13 - mmengine - INFO - Epoch(train) [40][ 840/1879] lr: 2.0000e-02 eta: 11:42:38 time: 0.4489 data_time: 0.0154 memory: 6717 grad_norm: 2.7595 loss: 1.4923 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4923 2023/04/14 04:15:20 - mmengine - INFO - Epoch(train) [40][ 860/1879] lr: 2.0000e-02 eta: 11:42:29 time: 0.3248 data_time: 0.0123 memory: 6717 grad_norm: 2.6987 loss: 1.6621 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.6621 2023/04/14 04:15:27 - mmengine - INFO - Epoch(train) [40][ 880/1879] lr: 2.0000e-02 eta: 11:42:22 time: 0.3867 data_time: 0.0154 memory: 6717 grad_norm: 2.6517 loss: 1.6703 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6703 2023/04/14 04:15:34 - mmengine - INFO - Epoch(train) [40][ 900/1879] lr: 2.0000e-02 eta: 11:42:14 time: 0.3505 data_time: 0.0508 memory: 6717 grad_norm: 2.7180 loss: 1.4368 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.4368 2023/04/14 04:15:42 - mmengine - INFO - Epoch(train) [40][ 920/1879] lr: 2.0000e-02 eta: 11:42:07 time: 0.3615 data_time: 0.0924 memory: 6717 grad_norm: 2.7627 loss: 1.4850 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4850 2023/04/14 04:15:49 - mmengine - INFO - Epoch(train) [40][ 940/1879] lr: 2.0000e-02 eta: 11:41:58 time: 0.3451 data_time: 0.1363 memory: 6717 grad_norm: 2.6989 loss: 1.6501 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.6501 2023/04/14 04:15:56 - mmengine - INFO - Epoch(train) [40][ 960/1879] lr: 2.0000e-02 eta: 11:41:50 time: 0.3547 data_time: 0.1053 memory: 6717 grad_norm: 2.6492 loss: 1.4853 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4853 2023/04/14 04:16:04 - mmengine - INFO - Epoch(train) [40][ 980/1879] lr: 2.0000e-02 eta: 11:41:44 time: 0.4083 data_time: 0.2157 memory: 6717 grad_norm: 2.6665 loss: 1.3999 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3999 2023/04/14 04:16:11 - mmengine - INFO - Epoch(train) [40][1000/1879] lr: 2.0000e-02 eta: 11:41:36 time: 0.3588 data_time: 0.1188 memory: 6717 grad_norm: 2.7238 loss: 1.6741 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.6741 2023/04/14 04:16:18 - mmengine - INFO - Epoch(train) [40][1020/1879] lr: 2.0000e-02 eta: 11:41:28 time: 0.3366 data_time: 0.0579 memory: 6717 grad_norm: 2.6964 loss: 1.7570 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7570 2023/04/14 04:16:25 - mmengine - INFO - Epoch(train) [40][1040/1879] lr: 2.0000e-02 eta: 11:41:21 time: 0.3792 data_time: 0.0462 memory: 6717 grad_norm: 2.7397 loss: 1.4199 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4199 2023/04/14 04:16:33 - mmengine - INFO - Epoch(train) [40][1060/1879] lr: 2.0000e-02 eta: 11:41:13 time: 0.3632 data_time: 0.1483 memory: 6717 grad_norm: 2.6899 loss: 1.6268 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.6268 2023/04/14 04:16:40 - mmengine - INFO - Epoch(train) [40][1080/1879] lr: 2.0000e-02 eta: 11:41:05 time: 0.3536 data_time: 0.1248 memory: 6717 grad_norm: 2.7001 loss: 1.3880 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3880 2023/04/14 04:16:48 - mmengine - INFO - Epoch(train) [40][1100/1879] lr: 2.0000e-02 eta: 11:40:59 time: 0.4201 data_time: 0.1990 memory: 6717 grad_norm: 2.7368 loss: 1.5444 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5444 2023/04/14 04:16:54 - mmengine - INFO - Epoch(train) [40][1120/1879] lr: 2.0000e-02 eta: 11:40:50 time: 0.3121 data_time: 0.1047 memory: 6717 grad_norm: 2.7084 loss: 1.5060 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5060 2023/04/14 04:17:02 - mmengine - INFO - Epoch(train) [40][1140/1879] lr: 2.0000e-02 eta: 11:40:44 time: 0.3964 data_time: 0.1942 memory: 6717 grad_norm: 2.5663 loss: 1.5897 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.5897 2023/04/14 04:17:09 - mmengine - INFO - Epoch(train) [40][1160/1879] lr: 2.0000e-02 eta: 11:40:35 time: 0.3447 data_time: 0.1125 memory: 6717 grad_norm: 2.6795 loss: 1.7444 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.7444 2023/04/14 04:17:18 - mmengine - INFO - Epoch(train) [40][1180/1879] lr: 2.0000e-02 eta: 11:40:30 time: 0.4365 data_time: 0.1208 memory: 6717 grad_norm: 2.7317 loss: 1.7275 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7275 2023/04/14 04:17:24 - mmengine - INFO - Epoch(train) [40][1200/1879] lr: 2.0000e-02 eta: 11:40:21 time: 0.3072 data_time: 0.0774 memory: 6717 grad_norm: 2.6472 loss: 1.5381 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5381 2023/04/14 04:17:33 - mmengine - INFO - Epoch(train) [40][1220/1879] lr: 2.0000e-02 eta: 11:40:15 time: 0.4208 data_time: 0.1800 memory: 6717 grad_norm: 2.7640 loss: 1.7576 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.7576 2023/04/14 04:17:39 - mmengine - INFO - Epoch(train) [40][1240/1879] lr: 2.0000e-02 eta: 11:40:06 time: 0.3231 data_time: 0.1037 memory: 6717 grad_norm: 2.7617 loss: 1.7028 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7028 2023/04/14 04:17:48 - mmengine - INFO - Epoch(train) [40][1260/1879] lr: 2.0000e-02 eta: 11:40:01 time: 0.4405 data_time: 0.1255 memory: 6717 grad_norm: 2.7073 loss: 1.4096 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4096 2023/04/14 04:17:55 - mmengine - INFO - Epoch(train) [40][1280/1879] lr: 2.0000e-02 eta: 11:39:52 time: 0.3399 data_time: 0.0256 memory: 6717 grad_norm: 2.6105 loss: 1.6561 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6561 2023/04/14 04:18:02 - mmengine - INFO - Epoch(train) [40][1300/1879] lr: 2.0000e-02 eta: 11:39:45 time: 0.3766 data_time: 0.0142 memory: 6717 grad_norm: 2.7496 loss: 1.5841 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5841 2023/04/14 04:18:08 - mmengine - INFO - Epoch(train) [40][1320/1879] lr: 2.0000e-02 eta: 11:39:36 time: 0.3175 data_time: 0.0150 memory: 6717 grad_norm: 2.6233 loss: 1.6095 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6095 2023/04/14 04:18:17 - mmengine - INFO - Epoch(train) [40][1340/1879] lr: 2.0000e-02 eta: 11:39:30 time: 0.4040 data_time: 0.0138 memory: 6717 grad_norm: 2.7207 loss: 1.7225 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.7225 2023/04/14 04:18:23 - mmengine - INFO - Epoch(train) [40][1360/1879] lr: 2.0000e-02 eta: 11:39:21 time: 0.3353 data_time: 0.0149 memory: 6717 grad_norm: 2.6762 loss: 1.6260 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.6260 2023/04/14 04:18:31 - mmengine - INFO - Epoch(train) [40][1380/1879] lr: 2.0000e-02 eta: 11:39:14 time: 0.3872 data_time: 0.0140 memory: 6717 grad_norm: 2.6594 loss: 1.6070 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6070 2023/04/14 04:18:38 - mmengine - INFO - Epoch(train) [40][1400/1879] lr: 2.0000e-02 eta: 11:39:06 time: 0.3301 data_time: 0.0153 memory: 6717 grad_norm: 2.6833 loss: 1.6012 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6012 2023/04/14 04:18:46 - mmengine - INFO - Epoch(train) [40][1420/1879] lr: 2.0000e-02 eta: 11:39:00 time: 0.4126 data_time: 0.0142 memory: 6717 grad_norm: 2.7244 loss: 1.5468 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5468 2023/04/14 04:18:52 - mmengine - INFO - Epoch(train) [40][1440/1879] lr: 2.0000e-02 eta: 11:38:51 time: 0.3269 data_time: 0.0148 memory: 6717 grad_norm: 2.7499 loss: 1.7966 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.7966 2023/04/14 04:19:01 - mmengine - INFO - Epoch(train) [40][1460/1879] lr: 2.0000e-02 eta: 11:38:45 time: 0.4367 data_time: 0.0142 memory: 6717 grad_norm: 2.7092 loss: 1.7253 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.7253 2023/04/14 04:19:08 - mmengine - INFO - Epoch(train) [40][1480/1879] lr: 2.0000e-02 eta: 11:38:36 time: 0.3174 data_time: 0.0146 memory: 6717 grad_norm: 2.7016 loss: 1.6357 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.6357 2023/04/14 04:19:16 - mmengine - INFO - Epoch(train) [40][1500/1879] lr: 2.0000e-02 eta: 11:38:30 time: 0.4126 data_time: 0.0132 memory: 6717 grad_norm: 2.6503 loss: 1.6470 top1_acc: 0.3125 top5_acc: 0.6875 loss_cls: 1.6470 2023/04/14 04:19:23 - mmengine - INFO - Epoch(train) [40][1520/1879] lr: 2.0000e-02 eta: 11:38:22 time: 0.3536 data_time: 0.0141 memory: 6717 grad_norm: 2.6292 loss: 1.6203 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.6203 2023/04/14 04:19:31 - mmengine - INFO - Epoch(train) [40][1540/1879] lr: 2.0000e-02 eta: 11:38:16 time: 0.4199 data_time: 0.0129 memory: 6717 grad_norm: 2.6732 loss: 1.4086 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4086 2023/04/14 04:19:38 - mmengine - INFO - Epoch(train) [40][1560/1879] lr: 2.0000e-02 eta: 11:38:08 time: 0.3345 data_time: 0.0159 memory: 6717 grad_norm: 2.6566 loss: 1.6433 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6433 2023/04/14 04:19:46 - mmengine - INFO - Epoch(train) [40][1580/1879] lr: 2.0000e-02 eta: 11:38:02 time: 0.4142 data_time: 0.0156 memory: 6717 grad_norm: 2.6791 loss: 1.4366 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4366 2023/04/14 04:19:53 - mmengine - INFO - Epoch(train) [40][1600/1879] lr: 2.0000e-02 eta: 11:37:54 time: 0.3415 data_time: 0.0144 memory: 6717 grad_norm: 2.6714 loss: 1.6749 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6749 2023/04/14 04:20:01 - mmengine - INFO - Epoch(train) [40][1620/1879] lr: 2.0000e-02 eta: 11:37:47 time: 0.3995 data_time: 0.0133 memory: 6717 grad_norm: 2.7441 loss: 1.6877 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6877 2023/04/14 04:20:08 - mmengine - INFO - Epoch(train) [40][1640/1879] lr: 2.0000e-02 eta: 11:37:38 time: 0.3323 data_time: 0.0158 memory: 6717 grad_norm: 2.6955 loss: 1.6530 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.6530 2023/04/14 04:20:16 - mmengine - INFO - Epoch(train) [40][1660/1879] lr: 2.0000e-02 eta: 11:37:32 time: 0.4073 data_time: 0.0138 memory: 6717 grad_norm: 2.6598 loss: 1.7583 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.7583 2023/04/14 04:20:22 - mmengine - INFO - Epoch(train) [40][1680/1879] lr: 2.0000e-02 eta: 11:37:23 time: 0.3103 data_time: 0.0154 memory: 6717 grad_norm: 2.7128 loss: 1.5650 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.5650 2023/04/14 04:20:30 - mmengine - INFO - Epoch(train) [40][1700/1879] lr: 2.0000e-02 eta: 11:37:16 time: 0.3822 data_time: 0.0136 memory: 6717 grad_norm: 2.7000 loss: 1.5447 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.5447 2023/04/14 04:20:36 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 04:20:36 - mmengine - INFO - Epoch(train) [40][1720/1879] lr: 2.0000e-02 eta: 11:37:07 time: 0.3187 data_time: 0.0163 memory: 6717 grad_norm: 2.6159 loss: 1.4902 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.4902 2023/04/14 04:20:44 - mmengine - INFO - Epoch(train) [40][1740/1879] lr: 2.0000e-02 eta: 11:37:00 time: 0.4035 data_time: 0.0130 memory: 6717 grad_norm: 2.6928 loss: 1.5912 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5912 2023/04/14 04:20:51 - mmengine - INFO - Epoch(train) [40][1760/1879] lr: 2.0000e-02 eta: 11:36:52 time: 0.3198 data_time: 0.0158 memory: 6717 grad_norm: 2.6015 loss: 1.5443 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5443 2023/04/14 04:20:59 - mmengine - INFO - Epoch(train) [40][1780/1879] lr: 2.0000e-02 eta: 11:36:45 time: 0.4091 data_time: 0.0136 memory: 6717 grad_norm: 2.7259 loss: 1.7103 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.7103 2023/04/14 04:21:06 - mmengine - INFO - Epoch(train) [40][1800/1879] lr: 2.0000e-02 eta: 11:36:38 time: 0.3646 data_time: 0.0153 memory: 6717 grad_norm: 2.7103 loss: 1.6554 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.6554 2023/04/14 04:21:13 - mmengine - INFO - Epoch(train) [40][1820/1879] lr: 2.0000e-02 eta: 11:36:30 time: 0.3572 data_time: 0.0137 memory: 6717 grad_norm: 2.6892 loss: 1.6308 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.6308 2023/04/14 04:21:21 - mmengine - INFO - Epoch(train) [40][1840/1879] lr: 2.0000e-02 eta: 11:36:23 time: 0.3980 data_time: 0.0170 memory: 6717 grad_norm: 2.7710 loss: 1.5362 top1_acc: 0.3125 top5_acc: 0.6250 loss_cls: 1.5362 2023/04/14 04:21:27 - mmengine - INFO - Epoch(train) [40][1860/1879] lr: 2.0000e-02 eta: 11:36:14 time: 0.3153 data_time: 0.0131 memory: 6717 grad_norm: 2.6468 loss: 1.6824 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.6824 2023/04/14 04:21:34 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 04:21:34 - mmengine - INFO - Epoch(train) [40][1879/1879] lr: 2.0000e-02 eta: 11:36:07 time: 0.3553 data_time: 0.0134 memory: 6717 grad_norm: 2.8123 loss: 1.5885 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.5885 2023/04/14 04:21:43 - mmengine - INFO - Epoch(val) [40][ 20/155] eta: 0:01:00 time: 0.4468 data_time: 0.4133 memory: 1391 2023/04/14 04:21:50 - mmengine - INFO - Epoch(val) [40][ 40/155] eta: 0:00:44 time: 0.3275 data_time: 0.2946 memory: 1391 2023/04/14 04:21:59 - mmengine - INFO - Epoch(val) [40][ 60/155] eta: 0:00:38 time: 0.4307 data_time: 0.3970 memory: 1391 2023/04/14 04:22:05 - mmengine - INFO - Epoch(val) [40][ 80/155] eta: 0:00:28 time: 0.3173 data_time: 0.2839 memory: 1391 2023/04/14 04:22:14 - mmengine - INFO - Epoch(val) [40][100/155] eta: 0:00:21 time: 0.4565 data_time: 0.4233 memory: 1391 2023/04/14 04:22:20 - mmengine - INFO - Epoch(val) [40][120/155] eta: 0:00:13 time: 0.3035 data_time: 0.2699 memory: 1391 2023/04/14 04:22:30 - mmengine - INFO - Epoch(val) [40][140/155] eta: 0:00:05 time: 0.4873 data_time: 0.4541 memory: 1391 2023/04/14 04:22:37 - mmengine - INFO - Epoch(val) [40][155/155] acc/top1: 0.6118 acc/top5: 0.8392 acc/mean1: 0.6117 data_time: 0.4219 time: 0.4543 2023/04/14 04:22:47 - mmengine - INFO - Epoch(train) [41][ 20/1879] lr: 2.0000e-03 eta: 11:36:04 time: 0.5131 data_time: 0.2389 memory: 6717 grad_norm: 2.8577 loss: 1.6149 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6149 2023/04/14 04:22:54 - mmengine - INFO - Epoch(train) [41][ 40/1879] lr: 2.0000e-03 eta: 11:35:56 time: 0.3497 data_time: 0.0429 memory: 6717 grad_norm: 2.5292 loss: 1.4206 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.4206 2023/04/14 04:23:02 - mmengine - INFO - Epoch(train) [41][ 60/1879] lr: 2.0000e-03 eta: 11:35:49 time: 0.3886 data_time: 0.0594 memory: 6717 grad_norm: 2.6123 loss: 1.7221 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7221 2023/04/14 04:23:08 - mmengine - INFO - Epoch(train) [41][ 80/1879] lr: 2.0000e-03 eta: 11:35:40 time: 0.3075 data_time: 0.0479 memory: 6717 grad_norm: 2.6443 loss: 1.6238 top1_acc: 0.3750 top5_acc: 0.5625 loss_cls: 1.6238 2023/04/14 04:23:17 - mmengine - INFO - Epoch(train) [41][ 100/1879] lr: 2.0000e-03 eta: 11:35:34 time: 0.4278 data_time: 0.0623 memory: 6717 grad_norm: 2.5259 loss: 1.2468 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2468 2023/04/14 04:23:23 - mmengine - INFO - Epoch(train) [41][ 120/1879] lr: 2.0000e-03 eta: 11:35:25 time: 0.3173 data_time: 0.0140 memory: 6717 grad_norm: 2.5804 loss: 1.5111 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5111 2023/04/14 04:23:31 - mmengine - INFO - Epoch(train) [41][ 140/1879] lr: 2.0000e-03 eta: 11:35:19 time: 0.4058 data_time: 0.0145 memory: 6717 grad_norm: 2.6220 loss: 1.5331 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5331 2023/04/14 04:23:38 - mmengine - INFO - Epoch(train) [41][ 160/1879] lr: 2.0000e-03 eta: 11:35:10 time: 0.3129 data_time: 0.0313 memory: 6717 grad_norm: 2.6327 loss: 1.6623 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6623 2023/04/14 04:23:46 - mmengine - INFO - Epoch(train) [41][ 180/1879] lr: 2.0000e-03 eta: 11:35:03 time: 0.4139 data_time: 0.0153 memory: 6717 grad_norm: 2.6270 loss: 1.4262 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.4262 2023/04/14 04:23:52 - mmengine - INFO - Epoch(train) [41][ 200/1879] lr: 2.0000e-03 eta: 11:34:54 time: 0.3140 data_time: 0.0155 memory: 6717 grad_norm: 2.6725 loss: 1.5900 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.5900 2023/04/14 04:24:00 - mmengine - INFO - Epoch(train) [41][ 220/1879] lr: 2.0000e-03 eta: 11:34:48 time: 0.4129 data_time: 0.0158 memory: 6717 grad_norm: 2.6099 loss: 1.5805 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5805 2023/04/14 04:24:07 - mmengine - INFO - Epoch(train) [41][ 240/1879] lr: 2.0000e-03 eta: 11:34:40 time: 0.3338 data_time: 0.0130 memory: 6717 grad_norm: 2.6444 loss: 1.3747 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3747 2023/04/14 04:24:15 - mmengine - INFO - Epoch(train) [41][ 260/1879] lr: 2.0000e-03 eta: 11:34:33 time: 0.3940 data_time: 0.1323 memory: 6717 grad_norm: 2.5963 loss: 1.2236 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2236 2023/04/14 04:24:21 - mmengine - INFO - Epoch(train) [41][ 280/1879] lr: 2.0000e-03 eta: 11:34:24 time: 0.3196 data_time: 0.1120 memory: 6717 grad_norm: 2.5880 loss: 1.3611 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3611 2023/04/14 04:24:29 - mmengine - INFO - Epoch(train) [41][ 300/1879] lr: 2.0000e-03 eta: 11:34:18 time: 0.4025 data_time: 0.1050 memory: 6717 grad_norm: 2.6487 loss: 1.4724 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4724 2023/04/14 04:24:36 - mmengine - INFO - Epoch(train) [41][ 320/1879] lr: 2.0000e-03 eta: 11:34:09 time: 0.3280 data_time: 0.0231 memory: 6717 grad_norm: 2.5812 loss: 1.4034 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4034 2023/04/14 04:24:44 - mmengine - INFO - Epoch(train) [41][ 340/1879] lr: 2.0000e-03 eta: 11:34:03 time: 0.4103 data_time: 0.0453 memory: 6717 grad_norm: 2.6522 loss: 1.4603 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4603 2023/04/14 04:24:51 - mmengine - INFO - Epoch(train) [41][ 360/1879] lr: 2.0000e-03 eta: 11:33:54 time: 0.3255 data_time: 0.0154 memory: 6717 grad_norm: 2.6703 loss: 1.4206 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4206 2023/04/14 04:24:59 - mmengine - INFO - Epoch(train) [41][ 380/1879] lr: 2.0000e-03 eta: 11:33:47 time: 0.3997 data_time: 0.0478 memory: 6717 grad_norm: 2.6331 loss: 1.3740 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3740 2023/04/14 04:25:06 - mmengine - INFO - Epoch(train) [41][ 400/1879] lr: 2.0000e-03 eta: 11:33:39 time: 0.3521 data_time: 0.0531 memory: 6717 grad_norm: 2.6264 loss: 1.3516 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3516 2023/04/14 04:25:13 - mmengine - INFO - Epoch(train) [41][ 420/1879] lr: 2.0000e-03 eta: 11:33:32 time: 0.3815 data_time: 0.0299 memory: 6717 grad_norm: 2.5766 loss: 1.2314 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2314 2023/04/14 04:25:21 - mmengine - INFO - Epoch(train) [41][ 440/1879] lr: 2.0000e-03 eta: 11:33:25 time: 0.3824 data_time: 0.0914 memory: 6717 grad_norm: 2.6610 loss: 1.4744 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.4744 2023/04/14 04:25:28 - mmengine - INFO - Epoch(train) [41][ 460/1879] lr: 2.0000e-03 eta: 11:33:17 time: 0.3448 data_time: 0.0955 memory: 6717 grad_norm: 2.6972 loss: 1.2712 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2712 2023/04/14 04:25:35 - mmengine - INFO - Epoch(train) [41][ 480/1879] lr: 2.0000e-03 eta: 11:33:10 time: 0.3792 data_time: 0.0199 memory: 6717 grad_norm: 2.6377 loss: 1.3042 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3042 2023/04/14 04:25:42 - mmengine - INFO - Epoch(train) [41][ 500/1879] lr: 2.0000e-03 eta: 11:33:02 time: 0.3391 data_time: 0.0152 memory: 6717 grad_norm: 2.6863 loss: 1.4390 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.4390 2023/04/14 04:25:50 - mmengine - INFO - Epoch(train) [41][ 520/1879] lr: 2.0000e-03 eta: 11:32:55 time: 0.4039 data_time: 0.0130 memory: 6717 grad_norm: 2.7302 loss: 1.4915 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4915 2023/04/14 04:25:57 - mmengine - INFO - Epoch(train) [41][ 540/1879] lr: 2.0000e-03 eta: 11:32:47 time: 0.3442 data_time: 0.0156 memory: 6717 grad_norm: 2.6716 loss: 1.3075 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3075 2023/04/14 04:26:05 - mmengine - INFO - Epoch(train) [41][ 560/1879] lr: 2.0000e-03 eta: 11:32:39 time: 0.3642 data_time: 0.0132 memory: 6717 grad_norm: 2.6340 loss: 1.3630 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3630 2023/04/14 04:26:12 - mmengine - INFO - Epoch(train) [41][ 580/1879] lr: 2.0000e-03 eta: 11:32:32 time: 0.3845 data_time: 0.0156 memory: 6717 grad_norm: 2.6685 loss: 1.4062 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4062 2023/04/14 04:26:19 - mmengine - INFO - Epoch(train) [41][ 600/1879] lr: 2.0000e-03 eta: 11:32:24 time: 0.3382 data_time: 0.0476 memory: 6717 grad_norm: 2.6604 loss: 1.2822 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2822 2023/04/14 04:26:26 - mmengine - INFO - Epoch(train) [41][ 620/1879] lr: 2.0000e-03 eta: 11:32:16 time: 0.3642 data_time: 0.0258 memory: 6717 grad_norm: 2.7051 loss: 1.4711 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4711 2023/04/14 04:26:34 - mmengine - INFO - Epoch(train) [41][ 640/1879] lr: 2.0000e-03 eta: 11:32:10 time: 0.3873 data_time: 0.0151 memory: 6717 grad_norm: 2.6123 loss: 1.3789 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3789 2023/04/14 04:26:41 - mmengine - INFO - Epoch(train) [41][ 660/1879] lr: 2.0000e-03 eta: 11:32:02 time: 0.3495 data_time: 0.0137 memory: 6717 grad_norm: 2.6559 loss: 1.4255 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4255 2023/04/14 04:26:49 - mmengine - INFO - Epoch(train) [41][ 680/1879] lr: 2.0000e-03 eta: 11:31:55 time: 0.4027 data_time: 0.0152 memory: 6717 grad_norm: 2.6692 loss: 1.5126 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.5126 2023/04/14 04:26:56 - mmengine - INFO - Epoch(train) [41][ 700/1879] lr: 2.0000e-03 eta: 11:31:46 time: 0.3262 data_time: 0.0135 memory: 6717 grad_norm: 2.6645 loss: 1.4267 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4267 2023/04/14 04:27:03 - mmengine - INFO - Epoch(train) [41][ 720/1879] lr: 2.0000e-03 eta: 11:31:40 time: 0.3963 data_time: 0.0157 memory: 6717 grad_norm: 2.6958 loss: 1.5245 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.5245 2023/04/14 04:27:10 - mmengine - INFO - Epoch(train) [41][ 740/1879] lr: 2.0000e-03 eta: 11:31:31 time: 0.3405 data_time: 0.0313 memory: 6717 grad_norm: 2.6526 loss: 1.2564 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2564 2023/04/14 04:27:19 - mmengine - INFO - Epoch(train) [41][ 760/1879] lr: 2.0000e-03 eta: 11:31:25 time: 0.4201 data_time: 0.0586 memory: 6717 grad_norm: 2.6831 loss: 1.1511 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1511 2023/04/14 04:27:25 - mmengine - INFO - Epoch(train) [41][ 780/1879] lr: 2.0000e-03 eta: 11:31:17 time: 0.3304 data_time: 0.0124 memory: 6717 grad_norm: 2.7084 loss: 1.5513 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5513 2023/04/14 04:27:33 - mmengine - INFO - Epoch(train) [41][ 800/1879] lr: 2.0000e-03 eta: 11:31:10 time: 0.3980 data_time: 0.0154 memory: 6717 grad_norm: 2.6611 loss: 1.4198 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4198 2023/04/14 04:27:39 - mmengine - INFO - Epoch(train) [41][ 820/1879] lr: 2.0000e-03 eta: 11:31:01 time: 0.3101 data_time: 0.0191 memory: 6717 grad_norm: 2.6774 loss: 1.2430 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2430 2023/04/14 04:27:48 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 04:27:48 - mmengine - INFO - Epoch(train) [41][ 840/1879] lr: 2.0000e-03 eta: 11:30:55 time: 0.4174 data_time: 0.0615 memory: 6717 grad_norm: 2.6411 loss: 1.5139 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5139 2023/04/14 04:27:55 - mmengine - INFO - Epoch(train) [41][ 860/1879] lr: 2.0000e-03 eta: 11:30:47 time: 0.3521 data_time: 0.1233 memory: 6717 grad_norm: 2.7226 loss: 1.4147 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.4147 2023/04/14 04:28:03 - mmengine - INFO - Epoch(train) [41][ 880/1879] lr: 2.0000e-03 eta: 11:30:41 time: 0.4045 data_time: 0.1370 memory: 6717 grad_norm: 2.6491 loss: 1.3081 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3081 2023/04/14 04:28:10 - mmengine - INFO - Epoch(train) [41][ 900/1879] lr: 2.0000e-03 eta: 11:30:32 time: 0.3419 data_time: 0.0460 memory: 6717 grad_norm: 2.6899 loss: 1.4164 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4164 2023/04/14 04:28:19 - mmengine - INFO - Epoch(train) [41][ 920/1879] lr: 2.0000e-03 eta: 11:30:27 time: 0.4419 data_time: 0.0452 memory: 6717 grad_norm: 2.7472 loss: 1.4455 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4455 2023/04/14 04:28:25 - mmengine - INFO - Epoch(train) [41][ 940/1879] lr: 2.0000e-03 eta: 11:30:18 time: 0.2986 data_time: 0.0132 memory: 6717 grad_norm: 2.7499 loss: 1.3321 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3321 2023/04/14 04:28:33 - mmengine - INFO - Epoch(train) [41][ 960/1879] lr: 2.0000e-03 eta: 11:30:12 time: 0.4250 data_time: 0.0556 memory: 6717 grad_norm: 2.6365 loss: 1.2449 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2449 2023/04/14 04:28:39 - mmengine - INFO - Epoch(train) [41][ 980/1879] lr: 2.0000e-03 eta: 11:30:03 time: 0.3168 data_time: 0.0343 memory: 6717 grad_norm: 2.7858 loss: 1.4381 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4381 2023/04/14 04:28:48 - mmengine - INFO - Epoch(train) [41][1000/1879] lr: 2.0000e-03 eta: 11:29:58 time: 0.4470 data_time: 0.0325 memory: 6717 grad_norm: 2.7064 loss: 1.3777 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3777 2023/04/14 04:28:55 - mmengine - INFO - Epoch(train) [41][1020/1879] lr: 2.0000e-03 eta: 11:29:49 time: 0.3194 data_time: 0.0118 memory: 6717 grad_norm: 2.6401 loss: 1.5044 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5044 2023/04/14 04:29:03 - mmengine - INFO - Epoch(train) [41][1040/1879] lr: 2.0000e-03 eta: 11:29:43 time: 0.4272 data_time: 0.0166 memory: 6717 grad_norm: 2.7126 loss: 1.4636 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4636 2023/04/14 04:29:10 - mmengine - INFO - Epoch(train) [41][1060/1879] lr: 2.0000e-03 eta: 11:29:34 time: 0.3248 data_time: 0.0130 memory: 6717 grad_norm: 2.6430 loss: 1.1135 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1135 2023/04/14 04:29:18 - mmengine - INFO - Epoch(train) [41][1080/1879] lr: 2.0000e-03 eta: 11:29:28 time: 0.4056 data_time: 0.0142 memory: 6717 grad_norm: 2.6879 loss: 1.3101 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.3101 2023/04/14 04:29:24 - mmengine - INFO - Epoch(train) [41][1100/1879] lr: 2.0000e-03 eta: 11:29:19 time: 0.3043 data_time: 0.0159 memory: 6717 grad_norm: 2.7295 loss: 1.1843 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1843 2023/04/14 04:29:32 - mmengine - INFO - Epoch(train) [41][1120/1879] lr: 2.0000e-03 eta: 11:29:13 time: 0.4171 data_time: 0.0145 memory: 6717 grad_norm: 2.7065 loss: 1.3890 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3890 2023/04/14 04:29:39 - mmengine - INFO - Epoch(train) [41][1140/1879] lr: 2.0000e-03 eta: 11:29:04 time: 0.3219 data_time: 0.0149 memory: 6717 grad_norm: 2.6203 loss: 1.4072 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4072 2023/04/14 04:29:47 - mmengine - INFO - Epoch(train) [41][1160/1879] lr: 2.0000e-03 eta: 11:28:57 time: 0.4099 data_time: 0.0148 memory: 6717 grad_norm: 2.6753 loss: 1.3352 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3352 2023/04/14 04:29:53 - mmengine - INFO - Epoch(train) [41][1180/1879] lr: 2.0000e-03 eta: 11:28:48 time: 0.3020 data_time: 0.0131 memory: 6717 grad_norm: 2.6634 loss: 1.6484 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.6484 2023/04/14 04:30:01 - mmengine - INFO - Epoch(train) [41][1200/1879] lr: 2.0000e-03 eta: 11:28:41 time: 0.3877 data_time: 0.0152 memory: 6717 grad_norm: 2.6972 loss: 1.3373 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.3373 2023/04/14 04:30:08 - mmengine - INFO - Epoch(train) [41][1220/1879] lr: 2.0000e-03 eta: 11:28:33 time: 0.3372 data_time: 0.0148 memory: 6717 grad_norm: 2.7324 loss: 1.3730 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3730 2023/04/14 04:30:16 - mmengine - INFO - Epoch(train) [41][1240/1879] lr: 2.0000e-03 eta: 11:28:27 time: 0.4182 data_time: 0.0141 memory: 6717 grad_norm: 2.7158 loss: 1.4993 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4993 2023/04/14 04:30:22 - mmengine - INFO - Epoch(train) [41][1260/1879] lr: 2.0000e-03 eta: 11:28:17 time: 0.2954 data_time: 0.0151 memory: 6717 grad_norm: 2.7138 loss: 1.3599 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3599 2023/04/14 04:30:30 - mmengine - INFO - Epoch(train) [41][1280/1879] lr: 2.0000e-03 eta: 11:28:10 time: 0.3972 data_time: 0.0130 memory: 6717 grad_norm: 2.6811 loss: 1.3464 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3464 2023/04/14 04:30:37 - mmengine - INFO - Epoch(train) [41][1300/1879] lr: 2.0000e-03 eta: 11:28:03 time: 0.3591 data_time: 0.0407 memory: 6717 grad_norm: 2.6918 loss: 1.1628 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.1628 2023/04/14 04:30:44 - mmengine - INFO - Epoch(train) [41][1320/1879] lr: 2.0000e-03 eta: 11:27:55 time: 0.3646 data_time: 0.0267 memory: 6717 grad_norm: 2.6939 loss: 1.5143 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.5143 2023/04/14 04:30:52 - mmengine - INFO - Epoch(train) [41][1340/1879] lr: 2.0000e-03 eta: 11:27:47 time: 0.3597 data_time: 0.0559 memory: 6717 grad_norm: 2.6539 loss: 1.4071 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4071 2023/04/14 04:31:00 - mmengine - INFO - Epoch(train) [41][1360/1879] lr: 2.0000e-03 eta: 11:27:41 time: 0.4068 data_time: 0.1106 memory: 6717 grad_norm: 2.6851 loss: 1.3272 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3272 2023/04/14 04:31:07 - mmengine - INFO - Epoch(train) [41][1380/1879] lr: 2.0000e-03 eta: 11:27:33 time: 0.3624 data_time: 0.0724 memory: 6717 grad_norm: 2.7034 loss: 1.4235 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4235 2023/04/14 04:31:15 - mmengine - INFO - Epoch(train) [41][1400/1879] lr: 2.0000e-03 eta: 11:27:27 time: 0.3912 data_time: 0.0360 memory: 6717 grad_norm: 2.6930 loss: 1.4145 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4145 2023/04/14 04:31:22 - mmengine - INFO - Epoch(train) [41][1420/1879] lr: 2.0000e-03 eta: 11:27:19 time: 0.3492 data_time: 0.0166 memory: 6717 grad_norm: 2.7311 loss: 1.2329 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.2329 2023/04/14 04:31:30 - mmengine - INFO - Epoch(train) [41][1440/1879] lr: 2.0000e-03 eta: 11:27:12 time: 0.4047 data_time: 0.0133 memory: 6717 grad_norm: 2.6356 loss: 1.2500 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2500 2023/04/14 04:31:37 - mmengine - INFO - Epoch(train) [41][1460/1879] lr: 2.0000e-03 eta: 11:27:04 time: 0.3441 data_time: 0.0160 memory: 6717 grad_norm: 2.6982 loss: 1.2876 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2876 2023/04/14 04:31:44 - mmengine - INFO - Epoch(train) [41][1480/1879] lr: 2.0000e-03 eta: 11:26:57 time: 0.3850 data_time: 0.0138 memory: 6717 grad_norm: 2.6948 loss: 1.5892 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5892 2023/04/14 04:31:52 - mmengine - INFO - Epoch(train) [41][1500/1879] lr: 2.0000e-03 eta: 11:26:50 time: 0.3711 data_time: 0.0150 memory: 6717 grad_norm: 2.7196 loss: 1.2738 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2738 2023/04/14 04:32:00 - mmengine - INFO - Epoch(train) [41][1520/1879] lr: 2.0000e-03 eta: 11:26:43 time: 0.3959 data_time: 0.0135 memory: 6717 grad_norm: 2.6884 loss: 1.5636 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5636 2023/04/14 04:32:07 - mmengine - INFO - Epoch(train) [41][1540/1879] lr: 2.0000e-03 eta: 11:26:35 time: 0.3558 data_time: 0.0158 memory: 6717 grad_norm: 2.7473 loss: 1.4229 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.4229 2023/04/14 04:32:14 - mmengine - INFO - Epoch(train) [41][1560/1879] lr: 2.0000e-03 eta: 11:26:28 time: 0.3670 data_time: 0.0132 memory: 6717 grad_norm: 2.7083 loss: 1.4039 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4039 2023/04/14 04:32:22 - mmengine - INFO - Epoch(train) [41][1580/1879] lr: 2.0000e-03 eta: 11:26:21 time: 0.3836 data_time: 0.0151 memory: 6717 grad_norm: 2.6737 loss: 1.3163 top1_acc: 0.3125 top5_acc: 0.4375 loss_cls: 1.3163 2023/04/14 04:32:29 - mmengine - INFO - Epoch(train) [41][1600/1879] lr: 2.0000e-03 eta: 11:26:12 time: 0.3400 data_time: 0.0136 memory: 6717 grad_norm: 2.7705 loss: 1.2625 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2625 2023/04/14 04:32:37 - mmengine - INFO - Epoch(train) [41][1620/1879] lr: 2.0000e-03 eta: 11:26:06 time: 0.4120 data_time: 0.0163 memory: 6717 grad_norm: 2.7907 loss: 1.4114 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.4114 2023/04/14 04:32:44 - mmengine - INFO - Epoch(train) [41][1640/1879] lr: 2.0000e-03 eta: 11:25:58 time: 0.3404 data_time: 0.0128 memory: 6717 grad_norm: 2.7492 loss: 1.2766 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2766 2023/04/14 04:32:52 - mmengine - INFO - Epoch(train) [41][1660/1879] lr: 2.0000e-03 eta: 11:25:51 time: 0.4069 data_time: 0.0150 memory: 6717 grad_norm: 2.7510 loss: 1.4054 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4054 2023/04/14 04:32:58 - mmengine - INFO - Epoch(train) [41][1680/1879] lr: 2.0000e-03 eta: 11:25:43 time: 0.3208 data_time: 0.0143 memory: 6717 grad_norm: 2.7133 loss: 1.2173 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2173 2023/04/14 04:33:07 - mmengine - INFO - Epoch(train) [41][1700/1879] lr: 2.0000e-03 eta: 11:25:37 time: 0.4372 data_time: 0.0141 memory: 6717 grad_norm: 2.7725 loss: 1.5300 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5300 2023/04/14 04:33:14 - mmengine - INFO - Epoch(train) [41][1720/1879] lr: 2.0000e-03 eta: 11:25:29 time: 0.3306 data_time: 0.0141 memory: 6717 grad_norm: 2.7099 loss: 1.2340 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2340 2023/04/14 04:33:21 - mmengine - INFO - Epoch(train) [41][1740/1879] lr: 2.0000e-03 eta: 11:25:21 time: 0.3783 data_time: 0.0158 memory: 6717 grad_norm: 2.8060 loss: 1.4885 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.4885 2023/04/14 04:33:28 - mmengine - INFO - Epoch(train) [41][1760/1879] lr: 2.0000e-03 eta: 11:25:13 time: 0.3287 data_time: 0.0132 memory: 6717 grad_norm: 2.6981 loss: 1.3619 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3619 2023/04/14 04:33:36 - mmengine - INFO - Epoch(train) [41][1780/1879] lr: 2.0000e-03 eta: 11:25:06 time: 0.3975 data_time: 0.0160 memory: 6717 grad_norm: 2.7045 loss: 1.3789 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3789 2023/04/14 04:33:42 - mmengine - INFO - Epoch(train) [41][1800/1879] lr: 2.0000e-03 eta: 11:24:57 time: 0.3011 data_time: 0.0126 memory: 6717 grad_norm: 2.7484 loss: 1.5091 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5091 2023/04/14 04:33:50 - mmengine - INFO - Epoch(train) [41][1820/1879] lr: 2.0000e-03 eta: 11:24:50 time: 0.3989 data_time: 0.0575 memory: 6717 grad_norm: 2.7075 loss: 1.2759 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2759 2023/04/14 04:33:56 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 04:33:56 - mmengine - INFO - Epoch(train) [41][1840/1879] lr: 2.0000e-03 eta: 11:24:41 time: 0.3144 data_time: 0.0319 memory: 6717 grad_norm: 2.7858 loss: 1.2358 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2358 2023/04/14 04:34:04 - mmengine - INFO - Epoch(train) [41][1860/1879] lr: 2.0000e-03 eta: 11:24:35 time: 0.4067 data_time: 0.0406 memory: 6717 grad_norm: 2.7611 loss: 1.3960 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3960 2023/04/14 04:34:10 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 04:34:10 - mmengine - INFO - Epoch(train) [41][1879/1879] lr: 2.0000e-03 eta: 11:24:26 time: 0.2989 data_time: 0.0131 memory: 6717 grad_norm: 2.7848 loss: 1.3870 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.3870 2023/04/14 04:34:19 - mmengine - INFO - Epoch(val) [41][ 20/155] eta: 0:01:01 time: 0.4588 data_time: 0.4264 memory: 1391 2023/04/14 04:34:25 - mmengine - INFO - Epoch(val) [41][ 40/155] eta: 0:00:43 time: 0.2994 data_time: 0.2657 memory: 1391 2023/04/14 04:34:34 - mmengine - INFO - Epoch(val) [41][ 60/155] eta: 0:00:38 time: 0.4538 data_time: 0.4208 memory: 1391 2023/04/14 04:34:41 - mmengine - INFO - Epoch(val) [41][ 80/155] eta: 0:00:28 time: 0.3195 data_time: 0.2860 memory: 1391 2023/04/14 04:34:50 - mmengine - INFO - Epoch(val) [41][100/155] eta: 0:00:21 time: 0.4538 data_time: 0.4205 memory: 1391 2023/04/14 04:34:56 - mmengine - INFO - Epoch(val) [41][120/155] eta: 0:00:13 time: 0.2958 data_time: 0.2623 memory: 1391 2023/04/14 04:35:05 - mmengine - INFO - Epoch(val) [41][140/155] eta: 0:00:05 time: 0.4831 data_time: 0.4504 memory: 1391 2023/04/14 04:35:12 - mmengine - INFO - Epoch(val) [41][155/155] acc/top1: 0.6506 acc/top5: 0.8640 acc/mean1: 0.6505 data_time: 0.4206 time: 0.4524 2023/04/14 04:35:13 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_39.pth is removed 2023/04/14 04:35:13 - mmengine - INFO - The best checkpoint with 0.6506 acc/top1 at 41 epoch is saved to best_acc_top1_epoch_41.pth. 2023/04/14 04:35:22 - mmengine - INFO - Epoch(train) [42][ 20/1879] lr: 2.0000e-03 eta: 11:24:21 time: 0.4686 data_time: 0.2572 memory: 6717 grad_norm: 2.7290 loss: 1.2703 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.2703 2023/04/14 04:35:29 - mmengine - INFO - Epoch(train) [42][ 40/1879] lr: 2.0000e-03 eta: 11:24:12 time: 0.3162 data_time: 0.1470 memory: 6717 grad_norm: 2.7370 loss: 1.2833 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2833 2023/04/14 04:35:37 - mmengine - INFO - Epoch(train) [42][ 60/1879] lr: 2.0000e-03 eta: 11:24:07 time: 0.4282 data_time: 0.1546 memory: 6717 grad_norm: 2.7896 loss: 1.2093 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2093 2023/04/14 04:35:44 - mmengine - INFO - Epoch(train) [42][ 80/1879] lr: 2.0000e-03 eta: 11:23:58 time: 0.3300 data_time: 0.0224 memory: 6717 grad_norm: 2.7607 loss: 1.5097 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.5097 2023/04/14 04:35:53 - mmengine - INFO - Epoch(train) [42][ 100/1879] lr: 2.0000e-03 eta: 11:23:52 time: 0.4294 data_time: 0.0161 memory: 6717 grad_norm: 2.7310 loss: 1.4158 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4158 2023/04/14 04:35:59 - mmengine - INFO - Epoch(train) [42][ 120/1879] lr: 2.0000e-03 eta: 11:23:43 time: 0.3139 data_time: 0.0266 memory: 6717 grad_norm: 2.6739 loss: 1.1895 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1895 2023/04/14 04:36:07 - mmengine - INFO - Epoch(train) [42][ 140/1879] lr: 2.0000e-03 eta: 11:23:38 time: 0.4329 data_time: 0.0531 memory: 6717 grad_norm: 2.7093 loss: 1.3393 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3393 2023/04/14 04:36:14 - mmengine - INFO - Epoch(train) [42][ 160/1879] lr: 2.0000e-03 eta: 11:23:29 time: 0.3396 data_time: 0.0129 memory: 6717 grad_norm: 2.7502 loss: 1.2703 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2703 2023/04/14 04:36:23 - mmengine - INFO - Epoch(train) [42][ 180/1879] lr: 2.0000e-03 eta: 11:23:24 time: 0.4305 data_time: 0.0161 memory: 6717 grad_norm: 2.7192 loss: 1.4507 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4507 2023/04/14 04:36:30 - mmengine - INFO - Epoch(train) [42][ 200/1879] lr: 2.0000e-03 eta: 11:23:15 time: 0.3309 data_time: 0.0129 memory: 6717 grad_norm: 2.7183 loss: 1.2462 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2462 2023/04/14 04:36:38 - mmengine - INFO - Epoch(train) [42][ 220/1879] lr: 2.0000e-03 eta: 11:23:10 time: 0.4397 data_time: 0.0137 memory: 6717 grad_norm: 2.7813 loss: 1.2607 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.2607 2023/04/14 04:36:45 - mmengine - INFO - Epoch(train) [42][ 240/1879] lr: 2.0000e-03 eta: 11:23:02 time: 0.3464 data_time: 0.0136 memory: 6717 grad_norm: 2.7612 loss: 1.2670 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2670 2023/04/14 04:36:53 - mmengine - INFO - Epoch(train) [42][ 260/1879] lr: 2.0000e-03 eta: 11:22:55 time: 0.4059 data_time: 0.0140 memory: 6717 grad_norm: 2.7421 loss: 1.3931 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3931 2023/04/14 04:36:59 - mmengine - INFO - Epoch(train) [42][ 280/1879] lr: 2.0000e-03 eta: 11:22:46 time: 0.3047 data_time: 0.0154 memory: 6717 grad_norm: 2.6995 loss: 1.2453 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.2453 2023/04/14 04:37:08 - mmengine - INFO - Epoch(train) [42][ 300/1879] lr: 2.0000e-03 eta: 11:22:40 time: 0.4118 data_time: 0.0143 memory: 6717 grad_norm: 2.6793 loss: 1.3056 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.3056 2023/04/14 04:37:14 - mmengine - INFO - Epoch(train) [42][ 320/1879] lr: 2.0000e-03 eta: 11:22:30 time: 0.2949 data_time: 0.0156 memory: 6717 grad_norm: 2.7104 loss: 1.3301 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3301 2023/04/14 04:37:22 - mmengine - INFO - Epoch(train) [42][ 340/1879] lr: 2.0000e-03 eta: 11:22:24 time: 0.4117 data_time: 0.0141 memory: 6717 grad_norm: 2.6550 loss: 1.3758 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3758 2023/04/14 04:37:28 - mmengine - INFO - Epoch(train) [42][ 360/1879] lr: 2.0000e-03 eta: 11:22:14 time: 0.2869 data_time: 0.0144 memory: 6717 grad_norm: 2.7161 loss: 1.3032 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3032 2023/04/14 04:37:36 - mmengine - INFO - Epoch(train) [42][ 380/1879] lr: 2.0000e-03 eta: 11:22:08 time: 0.4277 data_time: 0.0144 memory: 6717 grad_norm: 2.7469 loss: 1.5026 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5026 2023/04/14 04:37:43 - mmengine - INFO - Epoch(train) [42][ 400/1879] lr: 2.0000e-03 eta: 11:22:00 time: 0.3278 data_time: 0.0133 memory: 6717 grad_norm: 2.6969 loss: 1.2867 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2867 2023/04/14 04:37:51 - mmengine - INFO - Epoch(train) [42][ 420/1879] lr: 2.0000e-03 eta: 11:21:54 time: 0.4305 data_time: 0.0132 memory: 6717 grad_norm: 2.7848 loss: 1.3458 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3458 2023/04/14 04:37:58 - mmengine - INFO - Epoch(train) [42][ 440/1879] lr: 2.0000e-03 eta: 11:21:46 time: 0.3434 data_time: 0.0145 memory: 6717 grad_norm: 2.6827 loss: 1.3998 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3998 2023/04/14 04:38:06 - mmengine - INFO - Epoch(train) [42][ 460/1879] lr: 2.0000e-03 eta: 11:21:40 time: 0.4096 data_time: 0.0149 memory: 6717 grad_norm: 2.6535 loss: 1.2702 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2702 2023/04/14 04:38:13 - mmengine - INFO - Epoch(train) [42][ 480/1879] lr: 2.0000e-03 eta: 11:21:31 time: 0.3189 data_time: 0.0132 memory: 6717 grad_norm: 2.6633 loss: 1.4294 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4294 2023/04/14 04:38:21 - mmengine - INFO - Epoch(train) [42][ 500/1879] lr: 2.0000e-03 eta: 11:21:25 time: 0.4333 data_time: 0.0139 memory: 6717 grad_norm: 2.6597 loss: 1.2262 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2262 2023/04/14 04:38:28 - mmengine - INFO - Epoch(train) [42][ 520/1879] lr: 2.0000e-03 eta: 11:21:16 time: 0.3124 data_time: 0.0152 memory: 6717 grad_norm: 2.7100 loss: 1.4463 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4463 2023/04/14 04:38:37 - mmengine - INFO - Epoch(train) [42][ 540/1879] lr: 2.0000e-03 eta: 11:21:11 time: 0.4473 data_time: 0.0125 memory: 6717 grad_norm: 2.7517 loss: 1.3471 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3471 2023/04/14 04:38:43 - mmengine - INFO - Epoch(train) [42][ 560/1879] lr: 2.0000e-03 eta: 11:21:02 time: 0.3377 data_time: 0.0148 memory: 6717 grad_norm: 2.7400 loss: 1.2160 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2160 2023/04/14 04:38:52 - mmengine - INFO - Epoch(train) [42][ 580/1879] lr: 2.0000e-03 eta: 11:20:57 time: 0.4389 data_time: 0.0121 memory: 6717 grad_norm: 2.8017 loss: 1.3553 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3553 2023/04/14 04:38:59 - mmengine - INFO - Epoch(train) [42][ 600/1879] lr: 2.0000e-03 eta: 11:20:49 time: 0.3384 data_time: 0.0147 memory: 6717 grad_norm: 2.7299 loss: 1.3657 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3657 2023/04/14 04:39:08 - mmengine - INFO - Epoch(train) [42][ 620/1879] lr: 2.0000e-03 eta: 11:20:43 time: 0.4322 data_time: 0.0142 memory: 6717 grad_norm: 2.9997 loss: 1.4117 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4117 2023/04/14 04:39:14 - mmengine - INFO - Epoch(train) [42][ 640/1879] lr: 2.0000e-03 eta: 11:20:34 time: 0.3148 data_time: 0.0143 memory: 6717 grad_norm: 2.7678 loss: 1.2327 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.2327 2023/04/14 04:39:21 - mmengine - INFO - Epoch(train) [42][ 660/1879] lr: 2.0000e-03 eta: 11:20:27 time: 0.3715 data_time: 0.0141 memory: 6717 grad_norm: 2.6581 loss: 1.2250 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2250 2023/04/14 04:39:28 - mmengine - INFO - Epoch(train) [42][ 680/1879] lr: 2.0000e-03 eta: 11:20:18 time: 0.3163 data_time: 0.0152 memory: 6717 grad_norm: 2.8034 loss: 1.3942 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3942 2023/04/14 04:39:35 - mmengine - INFO - Epoch(train) [42][ 700/1879] lr: 2.0000e-03 eta: 11:20:11 time: 0.3901 data_time: 0.0134 memory: 6717 grad_norm: 2.6861 loss: 1.2756 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2756 2023/04/14 04:39:42 - mmengine - INFO - Epoch(train) [42][ 720/1879] lr: 2.0000e-03 eta: 11:20:03 time: 0.3451 data_time: 0.0160 memory: 6717 grad_norm: 2.7359 loss: 1.5061 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5061 2023/04/14 04:39:51 - mmengine - INFO - Epoch(train) [42][ 740/1879] lr: 2.0000e-03 eta: 11:19:56 time: 0.4105 data_time: 0.0163 memory: 6717 grad_norm: 2.6859 loss: 1.4359 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.4359 2023/04/14 04:39:57 - mmengine - INFO - Epoch(train) [42][ 760/1879] lr: 2.0000e-03 eta: 11:19:48 time: 0.3345 data_time: 0.0152 memory: 6717 grad_norm: 2.7459 loss: 1.5519 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.5519 2023/04/14 04:40:05 - mmengine - INFO - Epoch(train) [42][ 780/1879] lr: 2.0000e-03 eta: 11:19:41 time: 0.3795 data_time: 0.0135 memory: 6717 grad_norm: 2.6850 loss: 1.1870 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1870 2023/04/14 04:40:12 - mmengine - INFO - Epoch(train) [42][ 800/1879] lr: 2.0000e-03 eta: 11:19:33 time: 0.3513 data_time: 0.0153 memory: 6717 grad_norm: 2.7569 loss: 1.3769 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3769 2023/04/14 04:40:19 - mmengine - INFO - Epoch(train) [42][ 820/1879] lr: 2.0000e-03 eta: 11:19:25 time: 0.3474 data_time: 0.0153 memory: 6717 grad_norm: 2.7757 loss: 1.4599 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4599 2023/04/14 04:40:26 - mmengine - INFO - Epoch(train) [42][ 840/1879] lr: 2.0000e-03 eta: 11:19:17 time: 0.3513 data_time: 0.0145 memory: 6717 grad_norm: 2.7748 loss: 1.4246 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4246 2023/04/14 04:40:34 - mmengine - INFO - Epoch(train) [42][ 860/1879] lr: 2.0000e-03 eta: 11:19:10 time: 0.3885 data_time: 0.0147 memory: 6717 grad_norm: 2.7467 loss: 1.2562 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2562 2023/04/14 04:40:41 - mmengine - INFO - Epoch(train) [42][ 880/1879] lr: 2.0000e-03 eta: 11:19:03 time: 0.3794 data_time: 0.0161 memory: 6717 grad_norm: 2.6697 loss: 1.3242 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3242 2023/04/14 04:40:48 - mmengine - INFO - Epoch(train) [42][ 900/1879] lr: 2.0000e-03 eta: 11:18:54 time: 0.3320 data_time: 0.0139 memory: 6717 grad_norm: 2.7302 loss: 1.3426 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3426 2023/04/14 04:40:56 - mmengine - INFO - Epoch(train) [42][ 920/1879] lr: 2.0000e-03 eta: 11:18:48 time: 0.3999 data_time: 0.0383 memory: 6717 grad_norm: 2.7465 loss: 1.3445 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.3445 2023/04/14 04:41:02 - mmengine - INFO - Epoch(train) [42][ 940/1879] lr: 2.0000e-03 eta: 11:18:39 time: 0.3267 data_time: 0.0408 memory: 6717 grad_norm: 2.7316 loss: 1.3049 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3049 2023/04/14 04:41:10 - mmengine - INFO - Epoch(train) [42][ 960/1879] lr: 2.0000e-03 eta: 11:18:32 time: 0.3924 data_time: 0.0882 memory: 6717 grad_norm: 2.6920 loss: 1.4179 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.4179 2023/04/14 04:41:10 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 04:41:17 - mmengine - INFO - Epoch(train) [42][ 980/1879] lr: 2.0000e-03 eta: 11:18:24 time: 0.3280 data_time: 0.1112 memory: 6717 grad_norm: 2.7954 loss: 1.3747 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3747 2023/04/14 04:41:25 - mmengine - INFO - Epoch(train) [42][1000/1879] lr: 2.0000e-03 eta: 11:18:17 time: 0.3896 data_time: 0.1248 memory: 6717 grad_norm: 2.7330 loss: 1.5089 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.5089 2023/04/14 04:41:32 - mmengine - INFO - Epoch(train) [42][1020/1879] lr: 2.0000e-03 eta: 11:18:09 time: 0.3598 data_time: 0.0515 memory: 6717 grad_norm: 2.7306 loss: 1.4988 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.4988 2023/04/14 04:41:40 - mmengine - INFO - Epoch(train) [42][1040/1879] lr: 2.0000e-03 eta: 11:18:02 time: 0.3925 data_time: 0.1156 memory: 6717 grad_norm: 2.8192 loss: 1.3174 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3174 2023/04/14 04:41:47 - mmengine - INFO - Epoch(train) [42][1060/1879] lr: 2.0000e-03 eta: 11:17:54 time: 0.3527 data_time: 0.0781 memory: 6717 grad_norm: 2.7474 loss: 1.3111 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3111 2023/04/14 04:41:54 - mmengine - INFO - Epoch(train) [42][1080/1879] lr: 2.0000e-03 eta: 11:17:47 time: 0.3703 data_time: 0.0796 memory: 6717 grad_norm: 2.7541 loss: 1.4090 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.4090 2023/04/14 04:42:02 - mmengine - INFO - Epoch(train) [42][1100/1879] lr: 2.0000e-03 eta: 11:17:40 time: 0.3884 data_time: 0.1691 memory: 6717 grad_norm: 2.7778 loss: 1.3367 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3367 2023/04/14 04:42:09 - mmengine - INFO - Epoch(train) [42][1120/1879] lr: 2.0000e-03 eta: 11:17:33 time: 0.3619 data_time: 0.1129 memory: 6717 grad_norm: 2.7817 loss: 1.3798 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3798 2023/04/14 04:42:16 - mmengine - INFO - Epoch(train) [42][1140/1879] lr: 2.0000e-03 eta: 11:17:25 time: 0.3624 data_time: 0.0629 memory: 6717 grad_norm: 2.8283 loss: 1.3722 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3722 2023/04/14 04:42:23 - mmengine - INFO - Epoch(train) [42][1160/1879] lr: 2.0000e-03 eta: 11:17:17 time: 0.3544 data_time: 0.0814 memory: 6717 grad_norm: 2.7512 loss: 1.2693 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2693 2023/04/14 04:42:31 - mmengine - INFO - Epoch(train) [42][1180/1879] lr: 2.0000e-03 eta: 11:17:09 time: 0.3588 data_time: 0.1002 memory: 6717 grad_norm: 2.7504 loss: 1.3323 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3323 2023/04/14 04:42:38 - mmengine - INFO - Epoch(train) [42][1200/1879] lr: 2.0000e-03 eta: 11:17:02 time: 0.3836 data_time: 0.0342 memory: 6717 grad_norm: 2.8149 loss: 1.2989 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2989 2023/04/14 04:42:46 - mmengine - INFO - Epoch(train) [42][1220/1879] lr: 2.0000e-03 eta: 11:16:55 time: 0.3609 data_time: 0.0873 memory: 6717 grad_norm: 2.7461 loss: 1.2595 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2595 2023/04/14 04:42:53 - mmengine - INFO - Epoch(train) [42][1240/1879] lr: 2.0000e-03 eta: 11:16:47 time: 0.3636 data_time: 0.1082 memory: 6717 grad_norm: 2.7944 loss: 1.4552 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4552 2023/04/14 04:43:01 - mmengine - INFO - Epoch(train) [42][1260/1879] lr: 2.0000e-03 eta: 11:16:40 time: 0.3955 data_time: 0.2348 memory: 6717 grad_norm: 2.7402 loss: 1.2611 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.2611 2023/04/14 04:43:07 - mmengine - INFO - Epoch(train) [42][1280/1879] lr: 2.0000e-03 eta: 11:16:32 time: 0.3307 data_time: 0.1897 memory: 6717 grad_norm: 2.8071 loss: 1.3733 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3733 2023/04/14 04:43:16 - mmengine - INFO - Epoch(train) [42][1300/1879] lr: 2.0000e-03 eta: 11:16:26 time: 0.4310 data_time: 0.2904 memory: 6717 grad_norm: 2.7089 loss: 1.3763 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3763 2023/04/14 04:43:22 - mmengine - INFO - Epoch(train) [42][1320/1879] lr: 2.0000e-03 eta: 11:16:17 time: 0.3150 data_time: 0.1748 memory: 6717 grad_norm: 2.7335 loss: 1.2775 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2775 2023/04/14 04:43:31 - mmengine - INFO - Epoch(train) [42][1340/1879] lr: 2.0000e-03 eta: 11:16:11 time: 0.4187 data_time: 0.2794 memory: 6717 grad_norm: 2.7835 loss: 1.3468 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3468 2023/04/14 04:43:37 - mmengine - INFO - Epoch(train) [42][1360/1879] lr: 2.0000e-03 eta: 11:16:03 time: 0.3395 data_time: 0.2040 memory: 6717 grad_norm: 2.8313 loss: 1.4312 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4312 2023/04/14 04:43:46 - mmengine - INFO - Epoch(train) [42][1380/1879] lr: 2.0000e-03 eta: 11:15:56 time: 0.4084 data_time: 0.2696 memory: 6717 grad_norm: 2.7963 loss: 1.5095 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.5095 2023/04/14 04:43:52 - mmengine - INFO - Epoch(train) [42][1400/1879] lr: 2.0000e-03 eta: 11:15:47 time: 0.3069 data_time: 0.1691 memory: 6717 grad_norm: 2.7429 loss: 1.4753 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4753 2023/04/14 04:44:00 - mmengine - INFO - Epoch(train) [42][1420/1879] lr: 2.0000e-03 eta: 11:15:40 time: 0.3875 data_time: 0.2453 memory: 6717 grad_norm: 2.8313 loss: 1.4990 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4990 2023/04/14 04:44:07 - mmengine - INFO - Epoch(train) [42][1440/1879] lr: 2.0000e-03 eta: 11:15:33 time: 0.3615 data_time: 0.1573 memory: 6717 grad_norm: 2.7629 loss: 1.4740 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.4740 2023/04/14 04:44:14 - mmengine - INFO - Epoch(train) [42][1460/1879] lr: 2.0000e-03 eta: 11:15:25 time: 0.3653 data_time: 0.1756 memory: 6717 grad_norm: 2.7361 loss: 1.4506 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4506 2023/04/14 04:44:21 - mmengine - INFO - Epoch(train) [42][1480/1879] lr: 2.0000e-03 eta: 11:15:17 time: 0.3579 data_time: 0.0773 memory: 6717 grad_norm: 2.7917 loss: 1.3288 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3288 2023/04/14 04:44:28 - mmengine - INFO - Epoch(train) [42][1500/1879] lr: 2.0000e-03 eta: 11:15:10 time: 0.3601 data_time: 0.0425 memory: 6717 grad_norm: 2.7460 loss: 1.4160 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.4160 2023/04/14 04:44:36 - mmengine - INFO - Epoch(train) [42][1520/1879] lr: 2.0000e-03 eta: 11:15:02 time: 0.3790 data_time: 0.0134 memory: 6717 grad_norm: 2.7573 loss: 1.3414 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3414 2023/04/14 04:44:44 - mmengine - INFO - Epoch(train) [42][1540/1879] lr: 2.0000e-03 eta: 11:14:56 time: 0.3967 data_time: 0.0131 memory: 6717 grad_norm: 2.7602 loss: 1.3358 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3358 2023/04/14 04:44:51 - mmengine - INFO - Epoch(train) [42][1560/1879] lr: 2.0000e-03 eta: 11:14:48 time: 0.3508 data_time: 0.0228 memory: 6717 grad_norm: 2.7574 loss: 1.4751 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.4751 2023/04/14 04:44:59 - mmengine - INFO - Epoch(train) [42][1580/1879] lr: 2.0000e-03 eta: 11:14:41 time: 0.4093 data_time: 0.0140 memory: 6717 grad_norm: 2.7402 loss: 1.2454 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2454 2023/04/14 04:45:06 - mmengine - INFO - Epoch(train) [42][1600/1879] lr: 2.0000e-03 eta: 11:14:33 time: 0.3221 data_time: 0.0139 memory: 6717 grad_norm: 2.6592 loss: 1.3084 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3084 2023/04/14 04:45:14 - mmengine - INFO - Epoch(train) [42][1620/1879] lr: 2.0000e-03 eta: 11:14:27 time: 0.4352 data_time: 0.0146 memory: 6717 grad_norm: 2.7291 loss: 1.3177 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.3177 2023/04/14 04:45:21 - mmengine - INFO - Epoch(train) [42][1640/1879] lr: 2.0000e-03 eta: 11:14:18 time: 0.3234 data_time: 0.0147 memory: 6717 grad_norm: 2.7760 loss: 1.3964 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.3964 2023/04/14 04:45:28 - mmengine - INFO - Epoch(train) [42][1660/1879] lr: 2.0000e-03 eta: 11:14:11 time: 0.3846 data_time: 0.0134 memory: 6717 grad_norm: 2.7824 loss: 1.2563 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2563 2023/04/14 04:45:35 - mmengine - INFO - Epoch(train) [42][1680/1879] lr: 2.0000e-03 eta: 11:14:02 time: 0.3132 data_time: 0.0145 memory: 6717 grad_norm: 2.7116 loss: 1.2866 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2866 2023/04/14 04:45:43 - mmengine - INFO - Epoch(train) [42][1700/1879] lr: 2.0000e-03 eta: 11:13:56 time: 0.4099 data_time: 0.1003 memory: 6717 grad_norm: 2.7776 loss: 1.3699 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3699 2023/04/14 04:45:49 - mmengine - INFO - Epoch(train) [42][1720/1879] lr: 2.0000e-03 eta: 11:13:47 time: 0.3200 data_time: 0.0795 memory: 6717 grad_norm: 2.7763 loss: 1.2875 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2875 2023/04/14 04:45:57 - mmengine - INFO - Epoch(train) [42][1740/1879] lr: 2.0000e-03 eta: 11:13:41 time: 0.4067 data_time: 0.0822 memory: 6717 grad_norm: 2.7592 loss: 1.4223 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4223 2023/04/14 04:46:05 - mmengine - INFO - Epoch(train) [42][1760/1879] lr: 2.0000e-03 eta: 11:13:34 time: 0.3789 data_time: 0.0262 memory: 6717 grad_norm: 2.8344 loss: 1.4233 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4233 2023/04/14 04:46:13 - mmengine - INFO - Epoch(train) [42][1780/1879] lr: 2.0000e-03 eta: 11:13:26 time: 0.3769 data_time: 0.0592 memory: 6717 grad_norm: 2.7084 loss: 1.4189 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4189 2023/04/14 04:46:20 - mmengine - INFO - Epoch(train) [42][1800/1879] lr: 2.0000e-03 eta: 11:13:19 time: 0.3608 data_time: 0.0220 memory: 6717 grad_norm: 2.8760 loss: 1.4942 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4942 2023/04/14 04:46:27 - mmengine - INFO - Epoch(train) [42][1820/1879] lr: 2.0000e-03 eta: 11:13:10 time: 0.3369 data_time: 0.0331 memory: 6717 grad_norm: 2.7954 loss: 1.3731 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3731 2023/04/14 04:46:34 - mmengine - INFO - Epoch(train) [42][1840/1879] lr: 2.0000e-03 eta: 11:13:03 time: 0.3672 data_time: 0.0868 memory: 6717 grad_norm: 2.7343 loss: 1.1625 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1625 2023/04/14 04:46:41 - mmengine - INFO - Epoch(train) [42][1860/1879] lr: 2.0000e-03 eta: 11:12:55 time: 0.3615 data_time: 0.2069 memory: 6717 grad_norm: 2.7759 loss: 1.3565 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3565 2023/04/14 04:46:47 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 04:46:47 - mmengine - INFO - Epoch(train) [42][1879/1879] lr: 2.0000e-03 eta: 11:12:46 time: 0.2886 data_time: 0.1419 memory: 6717 grad_norm: 2.8973 loss: 1.3726 top1_acc: 0.4286 top5_acc: 1.0000 loss_cls: 1.3726 2023/04/14 04:46:47 - mmengine - INFO - Saving checkpoint at 42 epochs 2023/04/14 04:46:56 - mmengine - INFO - Epoch(val) [42][ 20/155] eta: 0:01:01 time: 0.4589 data_time: 0.4253 memory: 1391 2023/04/14 04:47:03 - mmengine - INFO - Epoch(val) [42][ 40/155] eta: 0:00:44 time: 0.3167 data_time: 0.2833 memory: 1391 2023/04/14 04:47:11 - mmengine - INFO - Epoch(val) [42][ 60/155] eta: 0:00:38 time: 0.4322 data_time: 0.3989 memory: 1391 2023/04/14 04:47:18 - mmengine - INFO - Epoch(val) [42][ 80/155] eta: 0:00:28 time: 0.3164 data_time: 0.2833 memory: 1391 2023/04/14 04:47:27 - mmengine - INFO - Epoch(val) [42][100/155] eta: 0:00:21 time: 0.4558 data_time: 0.4228 memory: 1391 2023/04/14 04:47:33 - mmengine - INFO - Epoch(val) [42][120/155] eta: 0:00:13 time: 0.2972 data_time: 0.2637 memory: 1391 2023/04/14 04:47:42 - mmengine - INFO - Epoch(val) [42][140/155] eta: 0:00:05 time: 0.4441 data_time: 0.4105 memory: 1391 2023/04/14 04:47:48 - mmengine - INFO - Epoch(val) [42][155/155] acc/top1: 0.6537 acc/top5: 0.8654 acc/mean1: 0.6536 data_time: 0.3674 time: 0.3999 2023/04/14 04:47:48 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_41.pth is removed 2023/04/14 04:47:49 - mmengine - INFO - The best checkpoint with 0.6537 acc/top1 at 42 epoch is saved to best_acc_top1_epoch_42.pth. 2023/04/14 04:47:59 - mmengine - INFO - Epoch(train) [43][ 20/1879] lr: 2.0000e-03 eta: 11:12:42 time: 0.4849 data_time: 0.3498 memory: 6717 grad_norm: 2.7941 loss: 1.4180 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.4180 2023/04/14 04:48:05 - mmengine - INFO - Epoch(train) [43][ 40/1879] lr: 2.0000e-03 eta: 11:12:34 time: 0.3374 data_time: 0.2043 memory: 6717 grad_norm: 2.7812 loss: 1.4270 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4270 2023/04/14 04:48:14 - mmengine - INFO - Epoch(train) [43][ 60/1879] lr: 2.0000e-03 eta: 11:12:28 time: 0.4320 data_time: 0.2932 memory: 6717 grad_norm: 2.7846 loss: 1.3464 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.3464 2023/04/14 04:48:21 - mmengine - INFO - Epoch(train) [43][ 80/1879] lr: 2.0000e-03 eta: 11:12:20 time: 0.3321 data_time: 0.1987 memory: 6717 grad_norm: 2.7649 loss: 1.4236 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.4236 2023/04/14 04:48:21 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 04:48:29 - mmengine - INFO - Epoch(train) [43][ 100/1879] lr: 2.0000e-03 eta: 11:12:14 time: 0.4213 data_time: 0.2828 memory: 6717 grad_norm: 2.8029 loss: 1.2921 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2921 2023/04/14 04:48:35 - mmengine - INFO - Epoch(train) [43][ 120/1879] lr: 2.0000e-03 eta: 11:12:05 time: 0.3186 data_time: 0.1774 memory: 6717 grad_norm: 2.8013 loss: 1.3874 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3874 2023/04/14 04:48:44 - mmengine - INFO - Epoch(train) [43][ 140/1879] lr: 2.0000e-03 eta: 11:11:59 time: 0.4398 data_time: 0.2746 memory: 6717 grad_norm: 2.8451 loss: 1.1305 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1305 2023/04/14 04:48:50 - mmengine - INFO - Epoch(train) [43][ 160/1879] lr: 2.0000e-03 eta: 11:11:50 time: 0.3098 data_time: 0.1354 memory: 6717 grad_norm: 2.7373 loss: 1.2866 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2866 2023/04/14 04:48:58 - mmengine - INFO - Epoch(train) [43][ 180/1879] lr: 2.0000e-03 eta: 11:11:43 time: 0.3907 data_time: 0.2483 memory: 6717 grad_norm: 2.7269 loss: 1.2079 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2079 2023/04/14 04:49:05 - mmengine - INFO - Epoch(train) [43][ 200/1879] lr: 2.0000e-03 eta: 11:11:35 time: 0.3241 data_time: 0.1886 memory: 6717 grad_norm: 2.7846 loss: 1.4357 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4357 2023/04/14 04:49:13 - mmengine - INFO - Epoch(train) [43][ 220/1879] lr: 2.0000e-03 eta: 11:11:28 time: 0.3930 data_time: 0.2151 memory: 6717 grad_norm: 2.6598 loss: 1.2500 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2500 2023/04/14 04:49:19 - mmengine - INFO - Epoch(train) [43][ 240/1879] lr: 2.0000e-03 eta: 11:11:19 time: 0.3319 data_time: 0.1308 memory: 6717 grad_norm: 2.7872 loss: 1.3751 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3751 2023/04/14 04:49:28 - mmengine - INFO - Epoch(train) [43][ 260/1879] lr: 2.0000e-03 eta: 11:11:13 time: 0.4261 data_time: 0.1625 memory: 6717 grad_norm: 2.7914 loss: 1.2858 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2858 2023/04/14 04:49:34 - mmengine - INFO - Epoch(train) [43][ 280/1879] lr: 2.0000e-03 eta: 11:11:04 time: 0.3035 data_time: 0.0507 memory: 6717 grad_norm: 2.8151 loss: 1.4304 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4304 2023/04/14 04:49:42 - mmengine - INFO - Epoch(train) [43][ 300/1879] lr: 2.0000e-03 eta: 11:10:58 time: 0.4137 data_time: 0.1506 memory: 6717 grad_norm: 2.8051 loss: 1.3256 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3256 2023/04/14 04:49:49 - mmengine - INFO - Epoch(train) [43][ 320/1879] lr: 2.0000e-03 eta: 11:10:50 time: 0.3436 data_time: 0.1501 memory: 6717 grad_norm: 2.7203 loss: 1.3397 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 1.3397 2023/04/14 04:49:58 - mmengine - INFO - Epoch(train) [43][ 340/1879] lr: 2.0000e-03 eta: 11:10:44 time: 0.4270 data_time: 0.1598 memory: 6717 grad_norm: 2.7405 loss: 1.2699 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2699 2023/04/14 04:50:04 - mmengine - INFO - Epoch(train) [43][ 360/1879] lr: 2.0000e-03 eta: 11:10:35 time: 0.3221 data_time: 0.1162 memory: 6717 grad_norm: 2.7879 loss: 1.3263 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3263 2023/04/14 04:50:13 - mmengine - INFO - Epoch(train) [43][ 380/1879] lr: 2.0000e-03 eta: 11:10:30 time: 0.4434 data_time: 0.2659 memory: 6717 grad_norm: 2.7915 loss: 1.2886 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2886 2023/04/14 04:50:19 - mmengine - INFO - Epoch(train) [43][ 400/1879] lr: 2.0000e-03 eta: 11:10:21 time: 0.3189 data_time: 0.1773 memory: 6717 grad_norm: 2.7828 loss: 1.2446 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2446 2023/04/14 04:50:27 - mmengine - INFO - Epoch(train) [43][ 420/1879] lr: 2.0000e-03 eta: 11:10:14 time: 0.3764 data_time: 0.2339 memory: 6717 grad_norm: 2.8223 loss: 1.3818 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3818 2023/04/14 04:50:34 - mmengine - INFO - Epoch(train) [43][ 440/1879] lr: 2.0000e-03 eta: 11:10:06 time: 0.3494 data_time: 0.2060 memory: 6717 grad_norm: 2.8361 loss: 1.3510 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3510 2023/04/14 04:50:41 - mmengine - INFO - Epoch(train) [43][ 460/1879] lr: 2.0000e-03 eta: 11:09:58 time: 0.3608 data_time: 0.1933 memory: 6717 grad_norm: 2.7584 loss: 1.3890 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3890 2023/04/14 04:50:49 - mmengine - INFO - Epoch(train) [43][ 480/1879] lr: 2.0000e-03 eta: 11:09:52 time: 0.4124 data_time: 0.1514 memory: 6717 grad_norm: 2.8190 loss: 1.3196 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3196 2023/04/14 04:50:56 - mmengine - INFO - Epoch(train) [43][ 500/1879] lr: 2.0000e-03 eta: 11:09:43 time: 0.3317 data_time: 0.1442 memory: 6717 grad_norm: 2.7045 loss: 1.4436 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.4436 2023/04/14 04:51:04 - mmengine - INFO - Epoch(train) [43][ 520/1879] lr: 2.0000e-03 eta: 11:09:37 time: 0.3929 data_time: 0.2502 memory: 6717 grad_norm: 2.7380 loss: 1.1870 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1870 2023/04/14 04:51:11 - mmengine - INFO - Epoch(train) [43][ 540/1879] lr: 2.0000e-03 eta: 11:09:29 time: 0.3647 data_time: 0.2234 memory: 6717 grad_norm: 2.8304 loss: 1.5346 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.5346 2023/04/14 04:51:20 - mmengine - INFO - Epoch(train) [43][ 560/1879] lr: 2.0000e-03 eta: 11:09:23 time: 0.4299 data_time: 0.2917 memory: 6717 grad_norm: 2.8127 loss: 1.2110 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2110 2023/04/14 04:51:26 - mmengine - INFO - Epoch(train) [43][ 580/1879] lr: 2.0000e-03 eta: 11:09:15 time: 0.3374 data_time: 0.1980 memory: 6717 grad_norm: 2.8215 loss: 1.3467 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.3467 2023/04/14 04:51:35 - mmengine - INFO - Epoch(train) [43][ 600/1879] lr: 2.0000e-03 eta: 11:09:09 time: 0.4414 data_time: 0.3000 memory: 6717 grad_norm: 2.7104 loss: 1.2359 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2359 2023/04/14 04:51:41 - mmengine - INFO - Epoch(train) [43][ 620/1879] lr: 2.0000e-03 eta: 11:09:00 time: 0.3003 data_time: 0.1584 memory: 6717 grad_norm: 2.8861 loss: 1.4851 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4851 2023/04/14 04:51:49 - mmengine - INFO - Epoch(train) [43][ 640/1879] lr: 2.0000e-03 eta: 11:08:54 time: 0.4105 data_time: 0.2720 memory: 6717 grad_norm: 2.8017 loss: 1.3228 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3228 2023/04/14 04:51:56 - mmengine - INFO - Epoch(train) [43][ 660/1879] lr: 2.0000e-03 eta: 11:08:46 time: 0.3434 data_time: 0.2038 memory: 6717 grad_norm: 2.8047 loss: 1.2860 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2860 2023/04/14 04:52:04 - mmengine - INFO - Epoch(train) [43][ 680/1879] lr: 2.0000e-03 eta: 11:08:39 time: 0.3793 data_time: 0.2391 memory: 6717 grad_norm: 2.7695 loss: 1.3074 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3074 2023/04/14 04:52:10 - mmengine - INFO - Epoch(train) [43][ 700/1879] lr: 2.0000e-03 eta: 11:08:30 time: 0.3213 data_time: 0.1824 memory: 6717 grad_norm: 2.7744 loss: 1.4531 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4531 2023/04/14 04:52:19 - mmengine - INFO - Epoch(train) [43][ 720/1879] lr: 2.0000e-03 eta: 11:08:24 time: 0.4320 data_time: 0.2903 memory: 6717 grad_norm: 2.7978 loss: 1.3844 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3844 2023/04/14 04:52:26 - mmengine - INFO - Epoch(train) [43][ 740/1879] lr: 2.0000e-03 eta: 11:08:16 time: 0.3578 data_time: 0.2179 memory: 6717 grad_norm: 2.8104 loss: 1.2599 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.2599 2023/04/14 04:52:34 - mmengine - INFO - Epoch(train) [43][ 760/1879] lr: 2.0000e-03 eta: 11:08:09 time: 0.3828 data_time: 0.2397 memory: 6717 grad_norm: 2.8509 loss: 1.3072 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3072 2023/04/14 04:52:41 - mmengine - INFO - Epoch(train) [43][ 780/1879] lr: 2.0000e-03 eta: 11:08:01 time: 0.3383 data_time: 0.1956 memory: 6717 grad_norm: 2.7365 loss: 1.2970 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.2970 2023/04/14 04:52:49 - mmengine - INFO - Epoch(train) [43][ 800/1879] lr: 2.0000e-03 eta: 11:07:55 time: 0.4156 data_time: 0.2670 memory: 6717 grad_norm: 2.6954 loss: 1.4312 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.4312 2023/04/14 04:52:55 - mmengine - INFO - Epoch(train) [43][ 820/1879] lr: 2.0000e-03 eta: 11:07:46 time: 0.3169 data_time: 0.1718 memory: 6717 grad_norm: 2.7990 loss: 1.2791 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2791 2023/04/14 04:53:04 - mmengine - INFO - Epoch(train) [43][ 840/1879] lr: 2.0000e-03 eta: 11:07:40 time: 0.4424 data_time: 0.2981 memory: 6717 grad_norm: 2.8478 loss: 1.2844 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2844 2023/04/14 04:53:11 - mmengine - INFO - Epoch(train) [43][ 860/1879] lr: 2.0000e-03 eta: 11:07:32 time: 0.3303 data_time: 0.1873 memory: 6717 grad_norm: 2.7216 loss: 1.3474 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3474 2023/04/14 04:53:19 - mmengine - INFO - Epoch(train) [43][ 880/1879] lr: 2.0000e-03 eta: 11:07:25 time: 0.3996 data_time: 0.2552 memory: 6717 grad_norm: 2.6880 loss: 1.3134 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3134 2023/04/14 04:53:25 - mmengine - INFO - Epoch(train) [43][ 900/1879] lr: 2.0000e-03 eta: 11:07:17 time: 0.3336 data_time: 0.1874 memory: 6717 grad_norm: 2.7494 loss: 1.2193 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2193 2023/04/14 04:53:34 - mmengine - INFO - Epoch(train) [43][ 920/1879] lr: 2.0000e-03 eta: 11:07:11 time: 0.4185 data_time: 0.2750 memory: 6717 grad_norm: 2.7546 loss: 1.5241 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.5241 2023/04/14 04:53:40 - mmengine - INFO - Epoch(train) [43][ 940/1879] lr: 2.0000e-03 eta: 11:07:02 time: 0.3247 data_time: 0.1784 memory: 6717 grad_norm: 2.7676 loss: 1.3364 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3364 2023/04/14 04:53:48 - mmengine - INFO - Epoch(train) [43][ 960/1879] lr: 2.0000e-03 eta: 11:06:55 time: 0.3703 data_time: 0.2267 memory: 6717 grad_norm: 2.7927 loss: 1.2719 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2719 2023/04/14 04:53:54 - mmengine - INFO - Epoch(train) [43][ 980/1879] lr: 2.0000e-03 eta: 11:06:46 time: 0.3117 data_time: 0.1687 memory: 6717 grad_norm: 2.7155 loss: 1.3547 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3547 2023/04/14 04:54:02 - mmengine - INFO - Epoch(train) [43][1000/1879] lr: 2.0000e-03 eta: 11:06:40 time: 0.4245 data_time: 0.2718 memory: 6717 grad_norm: 2.8446 loss: 1.2911 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2911 2023/04/14 04:54:09 - mmengine - INFO - Epoch(train) [43][1020/1879] lr: 2.0000e-03 eta: 11:06:32 time: 0.3373 data_time: 0.1076 memory: 6717 grad_norm: 2.7740 loss: 1.3136 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3136 2023/04/14 04:54:18 - mmengine - INFO - Epoch(train) [43][1040/1879] lr: 2.0000e-03 eta: 11:06:26 time: 0.4315 data_time: 0.1168 memory: 6717 grad_norm: 2.7905 loss: 1.2493 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2493 2023/04/14 04:54:24 - mmengine - INFO - Epoch(train) [43][1060/1879] lr: 2.0000e-03 eta: 11:06:17 time: 0.3228 data_time: 0.0164 memory: 6717 grad_norm: 2.8597 loss: 1.4635 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4635 2023/04/14 04:54:32 - mmengine - INFO - Epoch(train) [43][1080/1879] lr: 2.0000e-03 eta: 11:06:11 time: 0.4038 data_time: 0.0435 memory: 6717 grad_norm: 2.8824 loss: 1.5405 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.5405 2023/04/14 04:54:33 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 04:54:39 - mmengine - INFO - Epoch(train) [43][1100/1879] lr: 2.0000e-03 eta: 11:06:02 time: 0.3187 data_time: 0.0351 memory: 6717 grad_norm: 2.8287 loss: 1.2066 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2066 2023/04/14 04:54:46 - mmengine - INFO - Epoch(train) [43][1120/1879] lr: 2.0000e-03 eta: 11:05:55 time: 0.3913 data_time: 0.0234 memory: 6717 grad_norm: 2.7860 loss: 1.1446 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1446 2023/04/14 04:54:53 - mmengine - INFO - Epoch(train) [43][1140/1879] lr: 2.0000e-03 eta: 11:05:47 time: 0.3434 data_time: 0.0628 memory: 6717 grad_norm: 2.8746 loss: 1.3690 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3690 2023/04/14 04:55:01 - mmengine - INFO - Epoch(train) [43][1160/1879] lr: 2.0000e-03 eta: 11:05:40 time: 0.3903 data_time: 0.0304 memory: 6717 grad_norm: 2.7975 loss: 1.2232 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2232 2023/04/14 04:55:08 - mmengine - INFO - Epoch(train) [43][1180/1879] lr: 2.0000e-03 eta: 11:05:32 time: 0.3582 data_time: 0.0135 memory: 6717 grad_norm: 2.8112 loss: 1.2989 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2989 2023/04/14 04:55:16 - mmengine - INFO - Epoch(train) [43][1200/1879] lr: 2.0000e-03 eta: 11:05:25 time: 0.3624 data_time: 0.0132 memory: 6717 grad_norm: 2.7937 loss: 1.2959 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.2959 2023/04/14 04:55:23 - mmengine - INFO - Epoch(train) [43][1220/1879] lr: 2.0000e-03 eta: 11:05:17 time: 0.3696 data_time: 0.0152 memory: 6717 grad_norm: 2.7597 loss: 1.3539 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3539 2023/04/14 04:55:31 - mmengine - INFO - Epoch(train) [43][1240/1879] lr: 2.0000e-03 eta: 11:05:10 time: 0.3813 data_time: 0.0133 memory: 6717 grad_norm: 2.7740 loss: 1.2377 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2377 2023/04/14 04:55:38 - mmengine - INFO - Epoch(train) [43][1260/1879] lr: 2.0000e-03 eta: 11:05:02 time: 0.3622 data_time: 0.0153 memory: 6717 grad_norm: 2.8423 loss: 1.4509 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4509 2023/04/14 04:55:45 - mmengine - INFO - Epoch(train) [43][1280/1879] lr: 2.0000e-03 eta: 11:04:55 time: 0.3802 data_time: 0.0136 memory: 6717 grad_norm: 2.7753 loss: 1.2539 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2539 2023/04/14 04:55:52 - mmengine - INFO - Epoch(train) [43][1300/1879] lr: 2.0000e-03 eta: 11:04:47 time: 0.3372 data_time: 0.0150 memory: 6717 grad_norm: 2.7779 loss: 1.3169 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3169 2023/04/14 04:56:00 - mmengine - INFO - Epoch(train) [43][1320/1879] lr: 2.0000e-03 eta: 11:04:39 time: 0.3679 data_time: 0.0132 memory: 6717 grad_norm: 2.8004 loss: 1.2486 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2486 2023/04/14 04:56:07 - mmengine - INFO - Epoch(train) [43][1340/1879] lr: 2.0000e-03 eta: 11:04:32 time: 0.3618 data_time: 0.0152 memory: 6717 grad_norm: 2.7535 loss: 1.2843 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2843 2023/04/14 04:56:15 - mmengine - INFO - Epoch(train) [43][1360/1879] lr: 2.0000e-03 eta: 11:04:25 time: 0.4034 data_time: 0.0136 memory: 6717 grad_norm: 2.7845 loss: 1.1890 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1890 2023/04/14 04:56:22 - mmengine - INFO - Epoch(train) [43][1380/1879] lr: 2.0000e-03 eta: 11:04:17 time: 0.3414 data_time: 0.0157 memory: 6717 grad_norm: 2.8008 loss: 1.3969 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3969 2023/04/14 04:56:29 - mmengine - INFO - Epoch(train) [43][1400/1879] lr: 2.0000e-03 eta: 11:04:10 time: 0.3770 data_time: 0.0137 memory: 6717 grad_norm: 2.8386 loss: 1.2846 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2846 2023/04/14 04:56:36 - mmengine - INFO - Epoch(train) [43][1420/1879] lr: 2.0000e-03 eta: 11:04:01 time: 0.3296 data_time: 0.0158 memory: 6717 grad_norm: 2.7728 loss: 1.2517 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2517 2023/04/14 04:56:44 - mmengine - INFO - Epoch(train) [43][1440/1879] lr: 2.0000e-03 eta: 11:03:54 time: 0.3857 data_time: 0.0204 memory: 6717 grad_norm: 2.7381 loss: 1.3553 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3553 2023/04/14 04:56:51 - mmengine - INFO - Epoch(train) [43][1460/1879] lr: 2.0000e-03 eta: 11:03:47 time: 0.3812 data_time: 0.0151 memory: 6717 grad_norm: 2.7593 loss: 1.2603 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2603 2023/04/14 04:56:58 - mmengine - INFO - Epoch(train) [43][1480/1879] lr: 2.0000e-03 eta: 11:03:40 time: 0.3622 data_time: 0.0142 memory: 6717 grad_norm: 2.7807 loss: 1.3485 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3485 2023/04/14 04:57:06 - mmengine - INFO - Epoch(train) [43][1500/1879] lr: 2.0000e-03 eta: 11:03:32 time: 0.3699 data_time: 0.0138 memory: 6717 grad_norm: 2.7910 loss: 1.6239 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.6239 2023/04/14 04:57:13 - mmengine - INFO - Epoch(train) [43][1520/1879] lr: 2.0000e-03 eta: 11:03:24 time: 0.3607 data_time: 0.0146 memory: 6717 grad_norm: 2.8494 loss: 1.4172 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.4172 2023/04/14 04:57:21 - mmengine - INFO - Epoch(train) [43][1540/1879] lr: 2.0000e-03 eta: 11:03:17 time: 0.3847 data_time: 0.0146 memory: 6717 grad_norm: 2.7914 loss: 1.2577 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2577 2023/04/14 04:57:27 - mmengine - INFO - Epoch(train) [43][1560/1879] lr: 2.0000e-03 eta: 11:03:09 time: 0.3361 data_time: 0.0135 memory: 6717 grad_norm: 2.7853 loss: 1.2791 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2791 2023/04/14 04:57:36 - mmengine - INFO - Epoch(train) [43][1580/1879] lr: 2.0000e-03 eta: 11:03:03 time: 0.4177 data_time: 0.0147 memory: 6717 grad_norm: 2.8276 loss: 1.3008 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3008 2023/04/14 04:57:42 - mmengine - INFO - Epoch(train) [43][1600/1879] lr: 2.0000e-03 eta: 11:02:54 time: 0.3218 data_time: 0.0143 memory: 6717 grad_norm: 2.7114 loss: 1.2713 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2713 2023/04/14 04:57:51 - mmengine - INFO - Epoch(train) [43][1620/1879] lr: 2.0000e-03 eta: 11:02:48 time: 0.4171 data_time: 0.0135 memory: 6717 grad_norm: 2.8095 loss: 1.3832 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3832 2023/04/14 04:57:57 - mmengine - INFO - Epoch(train) [43][1640/1879] lr: 2.0000e-03 eta: 11:02:39 time: 0.3165 data_time: 0.0135 memory: 6717 grad_norm: 2.8573 loss: 1.4452 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4452 2023/04/14 04:58:05 - mmengine - INFO - Epoch(train) [43][1660/1879] lr: 2.0000e-03 eta: 11:02:32 time: 0.3922 data_time: 0.0152 memory: 6717 grad_norm: 2.7013 loss: 1.4607 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4607 2023/04/14 04:58:12 - mmengine - INFO - Epoch(train) [43][1680/1879] lr: 2.0000e-03 eta: 11:02:25 time: 0.3663 data_time: 0.0141 memory: 6717 grad_norm: 2.7735 loss: 1.4580 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.4580 2023/04/14 04:58:20 - mmengine - INFO - Epoch(train) [43][1700/1879] lr: 2.0000e-03 eta: 11:02:18 time: 0.3729 data_time: 0.0143 memory: 6717 grad_norm: 2.7701 loss: 1.2720 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2720 2023/04/14 04:58:27 - mmengine - INFO - Epoch(train) [43][1720/1879] lr: 2.0000e-03 eta: 11:02:10 time: 0.3697 data_time: 0.0150 memory: 6717 grad_norm: 2.7595 loss: 1.2120 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2120 2023/04/14 04:58:35 - mmengine - INFO - Epoch(train) [43][1740/1879] lr: 2.0000e-03 eta: 11:02:03 time: 0.3849 data_time: 0.0163 memory: 6717 grad_norm: 2.8202 loss: 1.4244 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4244 2023/04/14 04:58:42 - mmengine - INFO - Epoch(train) [43][1760/1879] lr: 2.0000e-03 eta: 11:01:56 time: 0.3765 data_time: 0.0132 memory: 6717 grad_norm: 2.8109 loss: 1.4055 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.4055 2023/04/14 04:58:50 - mmengine - INFO - Epoch(train) [43][1780/1879] lr: 2.0000e-03 eta: 11:01:49 time: 0.3816 data_time: 0.0147 memory: 6717 grad_norm: 2.7685 loss: 1.1941 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1941 2023/04/14 04:58:57 - mmengine - INFO - Epoch(train) [43][1800/1879] lr: 2.0000e-03 eta: 11:01:41 time: 0.3378 data_time: 0.0147 memory: 6717 grad_norm: 2.7747 loss: 1.3833 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3833 2023/04/14 04:59:04 - mmengine - INFO - Epoch(train) [43][1820/1879] lr: 2.0000e-03 eta: 11:01:34 time: 0.3871 data_time: 0.0147 memory: 6717 grad_norm: 2.7435 loss: 1.2353 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.2353 2023/04/14 04:59:12 - mmengine - INFO - Epoch(train) [43][1840/1879] lr: 2.0000e-03 eta: 11:01:27 time: 0.3899 data_time: 0.0142 memory: 6717 grad_norm: 2.7487 loss: 1.2944 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2944 2023/04/14 04:59:19 - mmengine - INFO - Epoch(train) [43][1860/1879] lr: 2.0000e-03 eta: 11:01:18 time: 0.3286 data_time: 0.0153 memory: 6717 grad_norm: 2.7610 loss: 1.3325 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3325 2023/04/14 04:59:24 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 04:59:24 - mmengine - INFO - Epoch(train) [43][1879/1879] lr: 2.0000e-03 eta: 11:01:09 time: 0.2924 data_time: 0.0126 memory: 6717 grad_norm: 2.8207 loss: 1.1976 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.1976 2023/04/14 04:59:34 - mmengine - INFO - Epoch(val) [43][ 20/155] eta: 0:01:02 time: 0.4644 data_time: 0.4317 memory: 1391 2023/04/14 04:59:40 - mmengine - INFO - Epoch(val) [43][ 40/155] eta: 0:00:44 time: 0.3171 data_time: 0.2834 memory: 1391 2023/04/14 04:59:49 - mmengine - INFO - Epoch(val) [43][ 60/155] eta: 0:00:38 time: 0.4345 data_time: 0.4014 memory: 1391 2023/04/14 04:59:55 - mmengine - INFO - Epoch(val) [43][ 80/155] eta: 0:00:28 time: 0.3168 data_time: 0.2827 memory: 1391 2023/04/14 05:00:04 - mmengine - INFO - Epoch(val) [43][100/155] eta: 0:00:21 time: 0.4547 data_time: 0.4212 memory: 1391 2023/04/14 05:00:10 - mmengine - INFO - Epoch(val) [43][120/155] eta: 0:00:13 time: 0.2970 data_time: 0.2631 memory: 1391 2023/04/14 05:00:19 - mmengine - INFO - Epoch(val) [43][140/155] eta: 0:00:05 time: 0.4442 data_time: 0.4105 memory: 1391 2023/04/14 05:00:26 - mmengine - INFO - Epoch(val) [43][155/155] acc/top1: 0.6532 acc/top5: 0.8662 acc/mean1: 0.6531 data_time: 0.3731 time: 0.4065 2023/04/14 05:00:36 - mmengine - INFO - Epoch(train) [44][ 20/1879] lr: 2.0000e-03 eta: 11:01:05 time: 0.4793 data_time: 0.2454 memory: 6717 grad_norm: 2.7217 loss: 1.4182 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4182 2023/04/14 05:00:42 - mmengine - INFO - Epoch(train) [44][ 40/1879] lr: 2.0000e-03 eta: 11:00:56 time: 0.3245 data_time: 0.1136 memory: 6717 grad_norm: 2.8012 loss: 1.3527 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.3527 2023/04/14 05:00:52 - mmengine - INFO - Epoch(train) [44][ 60/1879] lr: 2.0000e-03 eta: 11:00:51 time: 0.4670 data_time: 0.0794 memory: 6717 grad_norm: 2.7733 loss: 1.2015 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.2015 2023/04/14 05:00:58 - mmengine - INFO - Epoch(train) [44][ 80/1879] lr: 2.0000e-03 eta: 11:00:43 time: 0.3188 data_time: 0.0137 memory: 6717 grad_norm: 2.7976 loss: 1.2029 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2029 2023/04/14 05:01:06 - mmengine - INFO - Epoch(train) [44][ 100/1879] lr: 2.0000e-03 eta: 11:00:36 time: 0.3966 data_time: 0.0160 memory: 6717 grad_norm: 2.7963 loss: 1.2472 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2472 2023/04/14 05:01:12 - mmengine - INFO - Epoch(train) [44][ 120/1879] lr: 2.0000e-03 eta: 11:00:27 time: 0.3257 data_time: 0.0137 memory: 6717 grad_norm: 2.8125 loss: 1.2715 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2715 2023/04/14 05:01:21 - mmengine - INFO - Epoch(train) [44][ 140/1879] lr: 2.0000e-03 eta: 11:00:21 time: 0.4022 data_time: 0.0159 memory: 6717 grad_norm: 2.7943 loss: 1.3289 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3289 2023/04/14 05:01:27 - mmengine - INFO - Epoch(train) [44][ 160/1879] lr: 2.0000e-03 eta: 11:00:12 time: 0.3397 data_time: 0.0163 memory: 6717 grad_norm: 2.8105 loss: 1.1843 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1843 2023/04/14 05:01:36 - mmengine - INFO - Epoch(train) [44][ 180/1879] lr: 2.0000e-03 eta: 11:00:06 time: 0.4239 data_time: 0.0409 memory: 6717 grad_norm: 2.8001 loss: 1.2676 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2676 2023/04/14 05:01:43 - mmengine - INFO - Epoch(train) [44][ 200/1879] lr: 2.0000e-03 eta: 10:59:58 time: 0.3410 data_time: 0.0905 memory: 6717 grad_norm: 2.7493 loss: 1.2522 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2522 2023/04/14 05:01:45 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 05:01:51 - mmengine - INFO - Epoch(train) [44][ 220/1879] lr: 2.0000e-03 eta: 10:59:52 time: 0.4250 data_time: 0.1505 memory: 6717 grad_norm: 2.8346 loss: 1.2929 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2929 2023/04/14 05:01:57 - mmengine - INFO - Epoch(train) [44][ 240/1879] lr: 2.0000e-03 eta: 10:59:43 time: 0.3104 data_time: 0.1506 memory: 6717 grad_norm: 2.7922 loss: 1.2627 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2627 2023/04/14 05:02:05 - mmengine - INFO - Epoch(train) [44][ 260/1879] lr: 2.0000e-03 eta: 10:59:36 time: 0.3763 data_time: 0.2104 memory: 6717 grad_norm: 2.8259 loss: 1.3757 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3757 2023/04/14 05:02:12 - mmengine - INFO - Epoch(train) [44][ 280/1879] lr: 2.0000e-03 eta: 10:59:28 time: 0.3463 data_time: 0.1502 memory: 6717 grad_norm: 2.7887 loss: 1.3396 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3396 2023/04/14 05:02:21 - mmengine - INFO - Epoch(train) [44][ 300/1879] lr: 2.0000e-03 eta: 10:59:23 time: 0.4506 data_time: 0.2806 memory: 6717 grad_norm: 2.7851 loss: 1.2840 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2840 2023/04/14 05:02:27 - mmengine - INFO - Epoch(train) [44][ 320/1879] lr: 2.0000e-03 eta: 10:59:14 time: 0.3279 data_time: 0.1873 memory: 6717 grad_norm: 2.7705 loss: 1.3176 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.3176 2023/04/14 05:02:37 - mmengine - INFO - Epoch(train) [44][ 340/1879] lr: 2.0000e-03 eta: 10:59:09 time: 0.4653 data_time: 0.3261 memory: 6717 grad_norm: 2.7936 loss: 1.1932 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1932 2023/04/14 05:02:43 - mmengine - INFO - Epoch(train) [44][ 360/1879] lr: 2.0000e-03 eta: 10:59:01 time: 0.3281 data_time: 0.1909 memory: 6717 grad_norm: 2.8278 loss: 1.2657 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2657 2023/04/14 05:02:52 - mmengine - INFO - Epoch(train) [44][ 380/1879] lr: 2.0000e-03 eta: 10:58:55 time: 0.4167 data_time: 0.2780 memory: 6717 grad_norm: 2.7912 loss: 1.3163 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3163 2023/04/14 05:02:58 - mmengine - INFO - Epoch(train) [44][ 400/1879] lr: 2.0000e-03 eta: 10:58:46 time: 0.3310 data_time: 0.1907 memory: 6717 grad_norm: 2.7587 loss: 1.2492 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2492 2023/04/14 05:03:06 - mmengine - INFO - Epoch(train) [44][ 420/1879] lr: 2.0000e-03 eta: 10:58:39 time: 0.3850 data_time: 0.2460 memory: 6717 grad_norm: 2.7310 loss: 1.3111 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3111 2023/04/14 05:03:12 - mmengine - INFO - Epoch(train) [44][ 440/1879] lr: 2.0000e-03 eta: 10:58:29 time: 0.2797 data_time: 0.1388 memory: 6717 grad_norm: 2.8252 loss: 1.2928 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2928 2023/04/14 05:03:19 - mmengine - INFO - Epoch(train) [44][ 460/1879] lr: 2.0000e-03 eta: 10:58:22 time: 0.3842 data_time: 0.2448 memory: 6717 grad_norm: 2.8295 loss: 1.3399 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3399 2023/04/14 05:03:26 - mmengine - INFO - Epoch(train) [44][ 480/1879] lr: 2.0000e-03 eta: 10:58:14 time: 0.3343 data_time: 0.1967 memory: 6717 grad_norm: 2.8633 loss: 1.3386 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3386 2023/04/14 05:03:35 - mmengine - INFO - Epoch(train) [44][ 500/1879] lr: 2.0000e-03 eta: 10:58:08 time: 0.4426 data_time: 0.2906 memory: 6717 grad_norm: 2.7946 loss: 1.2606 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2606 2023/04/14 05:03:42 - mmengine - INFO - Epoch(train) [44][ 520/1879] lr: 2.0000e-03 eta: 10:58:01 time: 0.3524 data_time: 0.2106 memory: 6717 grad_norm: 2.8678 loss: 1.3663 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3663 2023/04/14 05:03:50 - mmengine - INFO - Epoch(train) [44][ 540/1879] lr: 2.0000e-03 eta: 10:57:54 time: 0.4132 data_time: 0.2736 memory: 6717 grad_norm: 2.7714 loss: 1.2191 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2191 2023/04/14 05:03:57 - mmengine - INFO - Epoch(train) [44][ 560/1879] lr: 2.0000e-03 eta: 10:57:46 time: 0.3250 data_time: 0.1833 memory: 6717 grad_norm: 2.7305 loss: 1.1241 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1241 2023/04/14 05:04:05 - mmengine - INFO - Epoch(train) [44][ 580/1879] lr: 2.0000e-03 eta: 10:57:40 time: 0.4352 data_time: 0.2962 memory: 6717 grad_norm: 2.7993 loss: 1.2768 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2768 2023/04/14 05:04:12 - mmengine - INFO - Epoch(train) [44][ 600/1879] lr: 2.0000e-03 eta: 10:57:31 time: 0.3194 data_time: 0.1783 memory: 6717 grad_norm: 2.7355 loss: 1.2554 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2554 2023/04/14 05:04:20 - mmengine - INFO - Epoch(train) [44][ 620/1879] lr: 2.0000e-03 eta: 10:57:25 time: 0.4065 data_time: 0.2661 memory: 6717 grad_norm: 2.8165 loss: 1.3648 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3648 2023/04/14 05:04:26 - mmengine - INFO - Epoch(train) [44][ 640/1879] lr: 2.0000e-03 eta: 10:57:16 time: 0.3252 data_time: 0.1847 memory: 6717 grad_norm: 2.7860 loss: 1.5220 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.5220 2023/04/14 05:04:34 - mmengine - INFO - Epoch(train) [44][ 660/1879] lr: 2.0000e-03 eta: 10:57:10 time: 0.4058 data_time: 0.2688 memory: 6717 grad_norm: 2.8735 loss: 1.2915 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2915 2023/04/14 05:04:41 - mmengine - INFO - Epoch(train) [44][ 680/1879] lr: 2.0000e-03 eta: 10:57:01 time: 0.3093 data_time: 0.1678 memory: 6717 grad_norm: 2.7855 loss: 1.1790 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1790 2023/04/14 05:04:49 - mmengine - INFO - Epoch(train) [44][ 700/1879] lr: 2.0000e-03 eta: 10:56:55 time: 0.4429 data_time: 0.3048 memory: 6717 grad_norm: 2.8124 loss: 1.2405 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2405 2023/04/14 05:04:55 - mmengine - INFO - Epoch(train) [44][ 720/1879] lr: 2.0000e-03 eta: 10:56:46 time: 0.2920 data_time: 0.1510 memory: 6717 grad_norm: 2.8003 loss: 1.2109 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.2109 2023/04/14 05:05:04 - mmengine - INFO - Epoch(train) [44][ 740/1879] lr: 2.0000e-03 eta: 10:56:40 time: 0.4367 data_time: 0.2953 memory: 6717 grad_norm: 2.7978 loss: 1.2987 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2987 2023/04/14 05:05:11 - mmengine - INFO - Epoch(train) [44][ 760/1879] lr: 2.0000e-03 eta: 10:56:32 time: 0.3345 data_time: 0.1910 memory: 6717 grad_norm: 2.8479 loss: 1.3729 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3729 2023/04/14 05:05:19 - mmengine - INFO - Epoch(train) [44][ 780/1879] lr: 2.0000e-03 eta: 10:56:26 time: 0.4322 data_time: 0.2913 memory: 6717 grad_norm: 2.7877 loss: 1.4532 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.4532 2023/04/14 05:05:26 - mmengine - INFO - Epoch(train) [44][ 800/1879] lr: 2.0000e-03 eta: 10:56:17 time: 0.3324 data_time: 0.1927 memory: 6717 grad_norm: 2.8275 loss: 1.2485 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2485 2023/04/14 05:05:34 - mmengine - INFO - Epoch(train) [44][ 820/1879] lr: 2.0000e-03 eta: 10:56:11 time: 0.4044 data_time: 0.2645 memory: 6717 grad_norm: 2.8006 loss: 1.3453 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.3453 2023/04/14 05:05:40 - mmengine - INFO - Epoch(train) [44][ 840/1879] lr: 2.0000e-03 eta: 10:56:02 time: 0.3079 data_time: 0.1688 memory: 6717 grad_norm: 2.8573 loss: 1.3813 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3813 2023/04/14 05:05:48 - mmengine - INFO - Epoch(train) [44][ 860/1879] lr: 2.0000e-03 eta: 10:55:55 time: 0.4076 data_time: 0.2668 memory: 6717 grad_norm: 2.8273 loss: 1.3399 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3399 2023/04/14 05:05:55 - mmengine - INFO - Epoch(train) [44][ 880/1879] lr: 2.0000e-03 eta: 10:55:47 time: 0.3145 data_time: 0.1679 memory: 6717 grad_norm: 2.8197 loss: 1.2472 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.2472 2023/04/14 05:06:03 - mmengine - INFO - Epoch(train) [44][ 900/1879] lr: 2.0000e-03 eta: 10:55:40 time: 0.4039 data_time: 0.2618 memory: 6717 grad_norm: 2.7640 loss: 1.2962 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2962 2023/04/14 05:06:09 - mmengine - INFO - Epoch(train) [44][ 920/1879] lr: 2.0000e-03 eta: 10:55:31 time: 0.3278 data_time: 0.1825 memory: 6717 grad_norm: 2.8783 loss: 1.1767 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1767 2023/04/14 05:06:17 - mmengine - INFO - Epoch(train) [44][ 940/1879] lr: 2.0000e-03 eta: 10:55:25 time: 0.3937 data_time: 0.2460 memory: 6717 grad_norm: 2.8388 loss: 1.3015 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3015 2023/04/14 05:06:24 - mmengine - INFO - Epoch(train) [44][ 960/1879] lr: 2.0000e-03 eta: 10:55:16 time: 0.3349 data_time: 0.1838 memory: 6717 grad_norm: 2.8431 loss: 1.3027 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3027 2023/04/14 05:06:32 - mmengine - INFO - Epoch(train) [44][ 980/1879] lr: 2.0000e-03 eta: 10:55:09 time: 0.3903 data_time: 0.2337 memory: 6717 grad_norm: 2.8690 loss: 1.1503 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1503 2023/04/14 05:06:39 - mmengine - INFO - Epoch(train) [44][1000/1879] lr: 2.0000e-03 eta: 10:55:01 time: 0.3356 data_time: 0.1510 memory: 6717 grad_norm: 2.8702 loss: 1.3490 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3490 2023/04/14 05:06:46 - mmengine - INFO - Epoch(train) [44][1020/1879] lr: 2.0000e-03 eta: 10:54:54 time: 0.3651 data_time: 0.2337 memory: 6717 grad_norm: 2.7689 loss: 1.3066 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3066 2023/04/14 05:06:53 - mmengine - INFO - Epoch(train) [44][1040/1879] lr: 2.0000e-03 eta: 10:54:46 time: 0.3699 data_time: 0.2331 memory: 6717 grad_norm: 2.8308 loss: 1.2979 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2979 2023/04/14 05:07:01 - mmengine - INFO - Epoch(train) [44][1060/1879] lr: 2.0000e-03 eta: 10:54:39 time: 0.3682 data_time: 0.2007 memory: 6717 grad_norm: 2.8374 loss: 1.2715 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2715 2023/04/14 05:07:08 - mmengine - INFO - Epoch(train) [44][1080/1879] lr: 2.0000e-03 eta: 10:54:32 time: 0.3955 data_time: 0.0790 memory: 6717 grad_norm: 2.8696 loss: 1.4528 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4528 2023/04/14 05:07:16 - mmengine - INFO - Epoch(train) [44][1100/1879] lr: 2.0000e-03 eta: 10:54:24 time: 0.3581 data_time: 0.0912 memory: 6717 grad_norm: 2.8106 loss: 1.1886 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1886 2023/04/14 05:07:23 - mmengine - INFO - Epoch(train) [44][1120/1879] lr: 2.0000e-03 eta: 10:54:17 time: 0.3784 data_time: 0.0620 memory: 6717 grad_norm: 2.9005 loss: 1.2500 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2500 2023/04/14 05:07:31 - mmengine - INFO - Epoch(train) [44][1140/1879] lr: 2.0000e-03 eta: 10:54:10 time: 0.3704 data_time: 0.1701 memory: 6717 grad_norm: 2.7749 loss: 1.2125 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2125 2023/04/14 05:07:38 - mmengine - INFO - Epoch(train) [44][1160/1879] lr: 2.0000e-03 eta: 10:54:02 time: 0.3722 data_time: 0.0830 memory: 6717 grad_norm: 2.7874 loss: 1.3204 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3204 2023/04/14 05:07:46 - mmengine - INFO - Epoch(train) [44][1180/1879] lr: 2.0000e-03 eta: 10:53:55 time: 0.3820 data_time: 0.1695 memory: 6717 grad_norm: 2.7322 loss: 1.1149 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1149 2023/04/14 05:07:53 - mmengine - INFO - Epoch(train) [44][1200/1879] lr: 2.0000e-03 eta: 10:53:47 time: 0.3465 data_time: 0.1504 memory: 6717 grad_norm: 2.8082 loss: 1.3189 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3189 2023/04/14 05:07:55 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 05:08:01 - mmengine - INFO - Epoch(train) [44][1220/1879] lr: 2.0000e-03 eta: 10:53:41 time: 0.4015 data_time: 0.2543 memory: 6717 grad_norm: 2.7451 loss: 1.1712 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1712 2023/04/14 05:08:07 - mmengine - INFO - Epoch(train) [44][1240/1879] lr: 2.0000e-03 eta: 10:53:32 time: 0.3340 data_time: 0.1944 memory: 6717 grad_norm: 2.8472 loss: 1.2301 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2301 2023/04/14 05:08:16 - mmengine - INFO - Epoch(train) [44][1260/1879] lr: 2.0000e-03 eta: 10:53:26 time: 0.4287 data_time: 0.2912 memory: 6717 grad_norm: 2.8330 loss: 1.3883 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3883 2023/04/14 05:08:22 - mmengine - INFO - Epoch(train) [44][1280/1879] lr: 2.0000e-03 eta: 10:53:17 time: 0.3107 data_time: 0.1679 memory: 6717 grad_norm: 2.8416 loss: 1.3262 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3262 2023/04/14 05:08:30 - mmengine - INFO - Epoch(train) [44][1300/1879] lr: 2.0000e-03 eta: 10:53:10 time: 0.3874 data_time: 0.2449 memory: 6717 grad_norm: 2.8117 loss: 1.3321 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3321 2023/04/14 05:08:36 - mmengine - INFO - Epoch(train) [44][1320/1879] lr: 2.0000e-03 eta: 10:53:02 time: 0.3259 data_time: 0.1775 memory: 6717 grad_norm: 2.8510 loss: 1.3753 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3753 2023/04/14 05:08:44 - mmengine - INFO - Epoch(train) [44][1340/1879] lr: 2.0000e-03 eta: 10:52:55 time: 0.3909 data_time: 0.2219 memory: 6717 grad_norm: 2.8545 loss: 1.4715 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4715 2023/04/14 05:08:51 - mmengine - INFO - Epoch(train) [44][1360/1879] lr: 2.0000e-03 eta: 10:52:47 time: 0.3475 data_time: 0.1692 memory: 6717 grad_norm: 2.8276 loss: 1.1688 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1688 2023/04/14 05:09:00 - mmengine - INFO - Epoch(train) [44][1380/1879] lr: 2.0000e-03 eta: 10:52:41 time: 0.4151 data_time: 0.2718 memory: 6717 grad_norm: 2.7780 loss: 1.2937 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.2937 2023/04/14 05:09:06 - mmengine - INFO - Epoch(train) [44][1400/1879] lr: 2.0000e-03 eta: 10:52:32 time: 0.3070 data_time: 0.1645 memory: 6717 grad_norm: 2.8818 loss: 1.2924 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2924 2023/04/14 05:09:13 - mmengine - INFO - Epoch(train) [44][1420/1879] lr: 2.0000e-03 eta: 10:52:24 time: 0.3805 data_time: 0.2087 memory: 6717 grad_norm: 2.8585 loss: 1.3922 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3922 2023/04/14 05:09:20 - mmengine - INFO - Epoch(train) [44][1440/1879] lr: 2.0000e-03 eta: 10:52:16 time: 0.3444 data_time: 0.1359 memory: 6717 grad_norm: 2.7939 loss: 1.1516 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1516 2023/04/14 05:09:28 - mmengine - INFO - Epoch(train) [44][1460/1879] lr: 2.0000e-03 eta: 10:52:10 time: 0.4147 data_time: 0.1157 memory: 6717 grad_norm: 2.7673 loss: 1.4374 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4374 2023/04/14 05:09:36 - mmengine - INFO - Epoch(train) [44][1480/1879] lr: 2.0000e-03 eta: 10:52:03 time: 0.3654 data_time: 0.0160 memory: 6717 grad_norm: 2.7162 loss: 1.3638 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3638 2023/04/14 05:09:44 - mmengine - INFO - Epoch(train) [44][1500/1879] lr: 2.0000e-03 eta: 10:51:57 time: 0.4232 data_time: 0.0165 memory: 6717 grad_norm: 2.8234 loss: 1.2686 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2686 2023/04/14 05:09:51 - mmengine - INFO - Epoch(train) [44][1520/1879] lr: 2.0000e-03 eta: 10:51:48 time: 0.3199 data_time: 0.0181 memory: 6717 grad_norm: 2.8231 loss: 1.0774 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0774 2023/04/14 05:09:59 - mmengine - INFO - Epoch(train) [44][1540/1879] lr: 2.0000e-03 eta: 10:51:42 time: 0.4235 data_time: 0.0137 memory: 6717 grad_norm: 2.8503 loss: 1.2785 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2785 2023/04/14 05:10:06 - mmengine - INFO - Epoch(train) [44][1560/1879] lr: 2.0000e-03 eta: 10:51:33 time: 0.3230 data_time: 0.0177 memory: 6717 grad_norm: 2.8996 loss: 1.2717 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2717 2023/04/14 05:10:14 - mmengine - INFO - Epoch(train) [44][1580/1879] lr: 2.0000e-03 eta: 10:51:27 time: 0.4147 data_time: 0.0144 memory: 6717 grad_norm: 2.8121 loss: 1.2039 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2039 2023/04/14 05:10:20 - mmengine - INFO - Epoch(train) [44][1600/1879] lr: 2.0000e-03 eta: 10:51:18 time: 0.3267 data_time: 0.0166 memory: 6717 grad_norm: 2.8708 loss: 1.2369 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.2369 2023/04/14 05:10:29 - mmengine - INFO - Epoch(train) [44][1620/1879] lr: 2.0000e-03 eta: 10:51:12 time: 0.4223 data_time: 0.0142 memory: 6717 grad_norm: 2.8486 loss: 1.4405 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4405 2023/04/14 05:10:35 - mmengine - INFO - Epoch(train) [44][1640/1879] lr: 2.0000e-03 eta: 10:51:03 time: 0.3017 data_time: 0.0141 memory: 6717 grad_norm: 2.7677 loss: 1.3354 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3354 2023/04/14 05:10:42 - mmengine - INFO - Epoch(train) [44][1660/1879] lr: 2.0000e-03 eta: 10:50:56 time: 0.3683 data_time: 0.0804 memory: 6717 grad_norm: 2.8537 loss: 1.4766 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4766 2023/04/14 05:10:50 - mmengine - INFO - Epoch(train) [44][1680/1879] lr: 2.0000e-03 eta: 10:50:48 time: 0.3672 data_time: 0.0530 memory: 6717 grad_norm: 2.7763 loss: 1.3787 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3787 2023/04/14 05:10:57 - mmengine - INFO - Epoch(train) [44][1700/1879] lr: 2.0000e-03 eta: 10:50:41 time: 0.3734 data_time: 0.0187 memory: 6717 grad_norm: 2.8694 loss: 1.4279 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4279 2023/04/14 05:11:04 - mmengine - INFO - Epoch(train) [44][1720/1879] lr: 2.0000e-03 eta: 10:50:33 time: 0.3494 data_time: 0.0145 memory: 6717 grad_norm: 2.7792 loss: 1.3218 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3218 2023/04/14 05:11:12 - mmengine - INFO - Epoch(train) [44][1740/1879] lr: 2.0000e-03 eta: 10:50:26 time: 0.3891 data_time: 0.0337 memory: 6717 grad_norm: 2.8079 loss: 1.2634 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2634 2023/04/14 05:11:19 - mmengine - INFO - Epoch(train) [44][1760/1879] lr: 2.0000e-03 eta: 10:50:19 time: 0.3727 data_time: 0.0144 memory: 6717 grad_norm: 2.8541 loss: 1.6383 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.6383 2023/04/14 05:11:27 - mmengine - INFO - Epoch(train) [44][1780/1879] lr: 2.0000e-03 eta: 10:50:11 time: 0.3653 data_time: 0.0144 memory: 6717 grad_norm: 2.8156 loss: 1.5196 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.5196 2023/04/14 05:11:34 - mmengine - INFO - Epoch(train) [44][1800/1879] lr: 2.0000e-03 eta: 10:50:03 time: 0.3448 data_time: 0.0141 memory: 6717 grad_norm: 2.8616 loss: 1.3532 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3532 2023/04/14 05:11:42 - mmengine - INFO - Epoch(train) [44][1820/1879] lr: 2.0000e-03 eta: 10:49:57 time: 0.4089 data_time: 0.0140 memory: 6717 grad_norm: 2.7912 loss: 1.2642 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2642 2023/04/14 05:11:49 - mmengine - INFO - Epoch(train) [44][1840/1879] lr: 2.0000e-03 eta: 10:49:49 time: 0.3459 data_time: 0.0171 memory: 6717 grad_norm: 2.7879 loss: 1.3479 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.3479 2023/04/14 05:11:56 - mmengine - INFO - Epoch(train) [44][1860/1879] lr: 2.0000e-03 eta: 10:49:41 time: 0.3714 data_time: 0.0136 memory: 6717 grad_norm: 2.8545 loss: 1.2261 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2261 2023/04/14 05:12:03 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 05:12:03 - mmengine - INFO - Epoch(train) [44][1879/1879] lr: 2.0000e-03 eta: 10:49:34 time: 0.3698 data_time: 0.0118 memory: 6717 grad_norm: 2.8218 loss: 1.1852 top1_acc: 0.4286 top5_acc: 0.8571 loss_cls: 1.1852 2023/04/14 05:12:12 - mmengine - INFO - Epoch(val) [44][ 20/155] eta: 0:01:02 time: 0.4615 data_time: 0.4286 memory: 1391 2023/04/14 05:12:18 - mmengine - INFO - Epoch(val) [44][ 40/155] eta: 0:00:44 time: 0.3163 data_time: 0.2833 memory: 1391 2023/04/14 05:12:27 - mmengine - INFO - Epoch(val) [44][ 60/155] eta: 0:00:37 time: 0.4214 data_time: 0.3878 memory: 1391 2023/04/14 05:12:33 - mmengine - INFO - Epoch(val) [44][ 80/155] eta: 0:00:28 time: 0.3224 data_time: 0.2891 memory: 1391 2023/04/14 05:12:42 - mmengine - INFO - Epoch(val) [44][100/155] eta: 0:00:21 time: 0.4254 data_time: 0.3923 memory: 1391 2023/04/14 05:12:48 - mmengine - INFO - Epoch(val) [44][120/155] eta: 0:00:13 time: 0.3327 data_time: 0.2998 memory: 1391 2023/04/14 05:12:58 - mmengine - INFO - Epoch(val) [44][140/155] eta: 0:00:05 time: 0.4846 data_time: 0.4517 memory: 1391 2023/04/14 05:13:05 - mmengine - INFO - Epoch(val) [44][155/155] acc/top1: 0.6565 acc/top5: 0.8681 acc/mean1: 0.6564 data_time: 0.4220 time: 0.4538 2023/04/14 05:13:05 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_42.pth is removed 2023/04/14 05:13:06 - mmengine - INFO - The best checkpoint with 0.6565 acc/top1 at 44 epoch is saved to best_acc_top1_epoch_44.pth. 2023/04/14 05:13:15 - mmengine - INFO - Epoch(train) [45][ 20/1879] lr: 2.0000e-03 eta: 10:49:29 time: 0.4744 data_time: 0.3323 memory: 6717 grad_norm: 2.8745 loss: 1.1619 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1619 2023/04/14 05:13:21 - mmengine - INFO - Epoch(train) [45][ 40/1879] lr: 2.0000e-03 eta: 10:49:20 time: 0.3155 data_time: 0.1526 memory: 6717 grad_norm: 2.7575 loss: 1.3432 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3432 2023/04/14 05:13:30 - mmengine - INFO - Epoch(train) [45][ 60/1879] lr: 2.0000e-03 eta: 10:49:14 time: 0.4167 data_time: 0.1237 memory: 6717 grad_norm: 2.8418 loss: 1.1409 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1409 2023/04/14 05:13:37 - mmengine - INFO - Epoch(train) [45][ 80/1879] lr: 2.0000e-03 eta: 10:49:06 time: 0.3378 data_time: 0.0778 memory: 6717 grad_norm: 2.8996 loss: 1.1560 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1560 2023/04/14 05:13:45 - mmengine - INFO - Epoch(train) [45][ 100/1879] lr: 2.0000e-03 eta: 10:48:59 time: 0.3999 data_time: 0.0276 memory: 6717 grad_norm: 2.7721 loss: 1.3103 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.3103 2023/04/14 05:13:51 - mmengine - INFO - Epoch(train) [45][ 120/1879] lr: 2.0000e-03 eta: 10:48:51 time: 0.3483 data_time: 0.0994 memory: 6717 grad_norm: 2.8183 loss: 1.2685 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2685 2023/04/14 05:13:59 - mmengine - INFO - Epoch(train) [45][ 140/1879] lr: 2.0000e-03 eta: 10:48:44 time: 0.3797 data_time: 0.1262 memory: 6717 grad_norm: 2.8433 loss: 1.2393 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2393 2023/04/14 05:14:06 - mmengine - INFO - Epoch(train) [45][ 160/1879] lr: 2.0000e-03 eta: 10:48:36 time: 0.3656 data_time: 0.1691 memory: 6717 grad_norm: 2.8681 loss: 1.2127 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2127 2023/04/14 05:14:13 - mmengine - INFO - Epoch(train) [45][ 180/1879] lr: 2.0000e-03 eta: 10:48:28 time: 0.3497 data_time: 0.0911 memory: 6717 grad_norm: 2.7719 loss: 1.1464 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1464 2023/04/14 05:14:21 - mmengine - INFO - Epoch(train) [45][ 200/1879] lr: 2.0000e-03 eta: 10:48:21 time: 0.3900 data_time: 0.0877 memory: 6717 grad_norm: 2.8281 loss: 1.2072 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2072 2023/04/14 05:14:28 - mmengine - INFO - Epoch(train) [45][ 220/1879] lr: 2.0000e-03 eta: 10:48:14 time: 0.3547 data_time: 0.0935 memory: 6717 grad_norm: 2.8502 loss: 1.3240 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3240 2023/04/14 05:14:36 - mmengine - INFO - Epoch(train) [45][ 240/1879] lr: 2.0000e-03 eta: 10:48:06 time: 0.3634 data_time: 0.0480 memory: 6717 grad_norm: 2.8188 loss: 1.2784 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2784 2023/04/14 05:14:43 - mmengine - INFO - Epoch(train) [45][ 260/1879] lr: 2.0000e-03 eta: 10:47:59 time: 0.3763 data_time: 0.0182 memory: 6717 grad_norm: 2.8113 loss: 1.3572 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3572 2023/04/14 05:14:50 - mmengine - INFO - Epoch(train) [45][ 280/1879] lr: 2.0000e-03 eta: 10:47:50 time: 0.3271 data_time: 0.0498 memory: 6717 grad_norm: 2.8655 loss: 1.4999 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4999 2023/04/14 05:14:58 - mmengine - INFO - Epoch(train) [45][ 300/1879] lr: 2.0000e-03 eta: 10:47:44 time: 0.4133 data_time: 0.0145 memory: 6717 grad_norm: 2.8947 loss: 1.3802 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3802 2023/04/14 05:15:05 - mmengine - INFO - Epoch(train) [45][ 320/1879] lr: 2.0000e-03 eta: 10:47:36 time: 0.3477 data_time: 0.0129 memory: 6717 grad_norm: 2.7842 loss: 1.2730 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2730 2023/04/14 05:15:07 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 05:15:13 - mmengine - INFO - Epoch(train) [45][ 340/1879] lr: 2.0000e-03 eta: 10:47:30 time: 0.4161 data_time: 0.0160 memory: 6717 grad_norm: 2.8919 loss: 1.4554 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.4554 2023/04/14 05:15:20 - mmengine - INFO - Epoch(train) [45][ 360/1879] lr: 2.0000e-03 eta: 10:47:22 time: 0.3428 data_time: 0.0823 memory: 6717 grad_norm: 2.8212 loss: 1.3705 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3705 2023/04/14 05:15:26 - mmengine - INFO - Epoch(train) [45][ 380/1879] lr: 2.0000e-03 eta: 10:47:13 time: 0.3222 data_time: 0.0704 memory: 6717 grad_norm: 2.7683 loss: 1.2939 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2939 2023/04/14 05:15:34 - mmengine - INFO - Epoch(train) [45][ 400/1879] lr: 2.0000e-03 eta: 10:47:06 time: 0.3760 data_time: 0.0285 memory: 6717 grad_norm: 2.8329 loss: 1.2922 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2922 2023/04/14 05:15:41 - mmengine - INFO - Epoch(train) [45][ 420/1879] lr: 2.0000e-03 eta: 10:46:58 time: 0.3442 data_time: 0.0245 memory: 6717 grad_norm: 2.7786 loss: 1.3199 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3199 2023/04/14 05:15:49 - mmengine - INFO - Epoch(train) [45][ 440/1879] lr: 2.0000e-03 eta: 10:46:52 time: 0.4245 data_time: 0.0151 memory: 6717 grad_norm: 2.8320 loss: 1.1589 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1589 2023/04/14 05:15:56 - mmengine - INFO - Epoch(train) [45][ 460/1879] lr: 2.0000e-03 eta: 10:46:43 time: 0.3263 data_time: 0.0135 memory: 6717 grad_norm: 2.7856 loss: 1.2614 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2614 2023/04/14 05:16:05 - mmengine - INFO - Epoch(train) [45][ 480/1879] lr: 2.0000e-03 eta: 10:46:37 time: 0.4303 data_time: 0.0149 memory: 6717 grad_norm: 2.8315 loss: 1.3803 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3803 2023/04/14 05:16:11 - mmengine - INFO - Epoch(train) [45][ 500/1879] lr: 2.0000e-03 eta: 10:46:28 time: 0.3110 data_time: 0.0131 memory: 6717 grad_norm: 2.8216 loss: 1.2571 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2571 2023/04/14 05:16:19 - mmengine - INFO - Epoch(train) [45][ 520/1879] lr: 2.0000e-03 eta: 10:46:21 time: 0.3945 data_time: 0.0149 memory: 6717 grad_norm: 2.8981 loss: 1.5289 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.5289 2023/04/14 05:16:25 - mmengine - INFO - Epoch(train) [45][ 540/1879] lr: 2.0000e-03 eta: 10:46:13 time: 0.3275 data_time: 0.0152 memory: 6717 grad_norm: 2.8006 loss: 1.1600 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1600 2023/04/14 05:16:33 - mmengine - INFO - Epoch(train) [45][ 560/1879] lr: 2.0000e-03 eta: 10:46:06 time: 0.4015 data_time: 0.0134 memory: 6717 grad_norm: 2.8847 loss: 1.2214 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2214 2023/04/14 05:16:40 - mmengine - INFO - Epoch(train) [45][ 580/1879] lr: 2.0000e-03 eta: 10:45:59 time: 0.3614 data_time: 0.0140 memory: 6717 grad_norm: 2.8141 loss: 1.2404 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2404 2023/04/14 05:16:49 - mmengine - INFO - Epoch(train) [45][ 600/1879] lr: 2.0000e-03 eta: 10:45:52 time: 0.4172 data_time: 0.0144 memory: 6717 grad_norm: 2.8532 loss: 1.1799 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1799 2023/04/14 05:16:56 - mmengine - INFO - Epoch(train) [45][ 620/1879] lr: 2.0000e-03 eta: 10:45:45 time: 0.3544 data_time: 0.0122 memory: 6717 grad_norm: 2.8309 loss: 1.2264 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2264 2023/04/14 05:17:05 - mmengine - INFO - Epoch(train) [45][ 640/1879] lr: 2.0000e-03 eta: 10:45:40 time: 0.4658 data_time: 0.0154 memory: 6717 grad_norm: 2.7676 loss: 1.1863 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1863 2023/04/14 05:17:11 - mmengine - INFO - Epoch(train) [45][ 660/1879] lr: 2.0000e-03 eta: 10:45:30 time: 0.2969 data_time: 0.0127 memory: 6717 grad_norm: 2.8124 loss: 1.3411 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3411 2023/04/14 05:17:20 - mmengine - INFO - Epoch(train) [45][ 680/1879] lr: 2.0000e-03 eta: 10:45:24 time: 0.4195 data_time: 0.0152 memory: 6717 grad_norm: 2.7794 loss: 1.4063 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.4063 2023/04/14 05:17:26 - mmengine - INFO - Epoch(train) [45][ 700/1879] lr: 2.0000e-03 eta: 10:45:16 time: 0.3245 data_time: 0.0131 memory: 6717 grad_norm: 2.8758 loss: 1.2759 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2759 2023/04/14 05:17:35 - mmengine - INFO - Epoch(train) [45][ 720/1879] lr: 2.0000e-03 eta: 10:45:10 time: 0.4471 data_time: 0.0149 memory: 6717 grad_norm: 2.8695 loss: 1.3495 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3495 2023/04/14 05:17:41 - mmengine - INFO - Epoch(train) [45][ 740/1879] lr: 2.0000e-03 eta: 10:45:00 time: 0.2794 data_time: 0.0125 memory: 6717 grad_norm: 2.7692 loss: 1.2090 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2090 2023/04/14 05:17:49 - mmengine - INFO - Epoch(train) [45][ 760/1879] lr: 2.0000e-03 eta: 10:44:55 time: 0.4450 data_time: 0.0156 memory: 6717 grad_norm: 2.9103 loss: 1.4370 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4370 2023/04/14 05:17:56 - mmengine - INFO - Epoch(train) [45][ 780/1879] lr: 2.0000e-03 eta: 10:44:46 time: 0.3067 data_time: 0.0123 memory: 6717 grad_norm: 2.8653 loss: 1.2916 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2916 2023/04/14 05:18:03 - mmengine - INFO - Epoch(train) [45][ 800/1879] lr: 2.0000e-03 eta: 10:44:38 time: 0.3602 data_time: 0.0147 memory: 6717 grad_norm: 2.8521 loss: 1.2172 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2172 2023/04/14 05:18:11 - mmengine - INFO - Epoch(train) [45][ 820/1879] lr: 2.0000e-03 eta: 10:44:32 time: 0.4209 data_time: 0.0141 memory: 6717 grad_norm: 2.8058 loss: 1.2672 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2672 2023/04/14 05:18:18 - mmengine - INFO - Epoch(train) [45][ 840/1879] lr: 2.0000e-03 eta: 10:44:24 time: 0.3442 data_time: 0.0142 memory: 6717 grad_norm: 2.7998 loss: 1.1695 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1695 2023/04/14 05:18:26 - mmengine - INFO - Epoch(train) [45][ 860/1879] lr: 2.0000e-03 eta: 10:44:17 time: 0.3727 data_time: 0.0136 memory: 6717 grad_norm: 2.8116 loss: 1.4646 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4646 2023/04/14 05:18:32 - mmengine - INFO - Epoch(train) [45][ 880/1879] lr: 2.0000e-03 eta: 10:44:09 time: 0.3412 data_time: 0.0153 memory: 6717 grad_norm: 2.8048 loss: 1.3051 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3051 2023/04/14 05:18:41 - mmengine - INFO - Epoch(train) [45][ 900/1879] lr: 2.0000e-03 eta: 10:44:02 time: 0.4071 data_time: 0.0130 memory: 6717 grad_norm: 2.8673 loss: 1.5304 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.5304 2023/04/14 05:18:47 - mmengine - INFO - Epoch(train) [45][ 920/1879] lr: 2.0000e-03 eta: 10:43:53 time: 0.3202 data_time: 0.0212 memory: 6717 grad_norm: 2.7919 loss: 1.4515 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4515 2023/04/14 05:18:55 - mmengine - INFO - Epoch(train) [45][ 940/1879] lr: 2.0000e-03 eta: 10:43:46 time: 0.3866 data_time: 0.0125 memory: 6717 grad_norm: 2.7911 loss: 1.3341 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3341 2023/04/14 05:19:01 - mmengine - INFO - Epoch(train) [45][ 960/1879] lr: 2.0000e-03 eta: 10:43:38 time: 0.3200 data_time: 0.0154 memory: 6717 grad_norm: 2.7944 loss: 1.2879 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2879 2023/04/14 05:19:09 - mmengine - INFO - Epoch(train) [45][ 980/1879] lr: 2.0000e-03 eta: 10:43:31 time: 0.3993 data_time: 0.0182 memory: 6717 grad_norm: 2.8364 loss: 1.2960 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2960 2023/04/14 05:19:16 - mmengine - INFO - Epoch(train) [45][1000/1879] lr: 2.0000e-03 eta: 10:43:23 time: 0.3416 data_time: 0.0230 memory: 6717 grad_norm: 2.8768 loss: 1.2951 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2951 2023/04/14 05:19:24 - mmengine - INFO - Epoch(train) [45][1020/1879] lr: 2.0000e-03 eta: 10:43:17 time: 0.4130 data_time: 0.0530 memory: 6717 grad_norm: 2.8372 loss: 1.2301 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2301 2023/04/14 05:19:31 - mmengine - INFO - Epoch(train) [45][1040/1879] lr: 2.0000e-03 eta: 10:43:09 time: 0.3601 data_time: 0.0963 memory: 6717 grad_norm: 2.9153 loss: 1.4870 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4870 2023/04/14 05:19:39 - mmengine - INFO - Epoch(train) [45][1060/1879] lr: 2.0000e-03 eta: 10:43:01 time: 0.3647 data_time: 0.1186 memory: 6717 grad_norm: 2.8230 loss: 1.5288 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.5288 2023/04/14 05:19:46 - mmengine - INFO - Epoch(train) [45][1080/1879] lr: 2.0000e-03 eta: 10:42:54 time: 0.3868 data_time: 0.2028 memory: 6717 grad_norm: 2.7830 loss: 1.1806 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1806 2023/04/14 05:19:54 - mmengine - INFO - Epoch(train) [45][1100/1879] lr: 2.0000e-03 eta: 10:42:47 time: 0.3735 data_time: 0.1136 memory: 6717 grad_norm: 2.8405 loss: 1.3641 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3641 2023/04/14 05:20:01 - mmengine - INFO - Epoch(train) [45][1120/1879] lr: 2.0000e-03 eta: 10:42:40 time: 0.3725 data_time: 0.1736 memory: 6717 grad_norm: 2.7656 loss: 1.1437 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1437 2023/04/14 05:20:08 - mmengine - INFO - Epoch(train) [45][1140/1879] lr: 2.0000e-03 eta: 10:42:32 time: 0.3442 data_time: 0.1292 memory: 6717 grad_norm: 2.8262 loss: 1.5972 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.5972 2023/04/14 05:20:15 - mmengine - INFO - Epoch(train) [45][1160/1879] lr: 2.0000e-03 eta: 10:42:24 time: 0.3538 data_time: 0.0829 memory: 6717 grad_norm: 2.7856 loss: 1.3418 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3418 2023/04/14 05:20:23 - mmengine - INFO - Epoch(train) [45][1180/1879] lr: 2.0000e-03 eta: 10:42:17 time: 0.4037 data_time: 0.0327 memory: 6717 grad_norm: 2.7967 loss: 1.1719 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1719 2023/04/14 05:20:30 - mmengine - INFO - Epoch(train) [45][1200/1879] lr: 2.0000e-03 eta: 10:42:09 time: 0.3476 data_time: 0.0341 memory: 6717 grad_norm: 2.7925 loss: 1.3473 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3473 2023/04/14 05:20:38 - mmengine - INFO - Epoch(train) [45][1220/1879] lr: 2.0000e-03 eta: 10:42:03 time: 0.3959 data_time: 0.0139 memory: 6717 grad_norm: 2.7480 loss: 1.2892 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2892 2023/04/14 05:20:45 - mmengine - INFO - Epoch(train) [45][1240/1879] lr: 2.0000e-03 eta: 10:41:54 time: 0.3185 data_time: 0.0166 memory: 6717 grad_norm: 2.8318 loss: 1.2779 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2779 2023/04/14 05:20:53 - mmengine - INFO - Epoch(train) [45][1260/1879] lr: 2.0000e-03 eta: 10:41:48 time: 0.4156 data_time: 0.0136 memory: 6717 grad_norm: 2.9041 loss: 1.3239 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3239 2023/04/14 05:21:00 - mmengine - INFO - Epoch(train) [45][1280/1879] lr: 2.0000e-03 eta: 10:41:39 time: 0.3293 data_time: 0.0173 memory: 6717 grad_norm: 2.7755 loss: 0.9968 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9968 2023/04/14 05:21:08 - mmengine - INFO - Epoch(train) [45][1300/1879] lr: 2.0000e-03 eta: 10:41:33 time: 0.4281 data_time: 0.0127 memory: 6717 grad_norm: 2.8654 loss: 1.3061 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3061 2023/04/14 05:21:14 - mmengine - INFO - Epoch(train) [45][1320/1879] lr: 2.0000e-03 eta: 10:41:24 time: 0.2921 data_time: 0.0159 memory: 6717 grad_norm: 2.8736 loss: 1.2414 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2414 2023/04/14 05:21:16 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 05:21:23 - mmengine - INFO - Epoch(train) [45][1340/1879] lr: 2.0000e-03 eta: 10:41:18 time: 0.4419 data_time: 0.0143 memory: 6717 grad_norm: 2.8217 loss: 1.3018 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3018 2023/04/14 05:21:29 - mmengine - INFO - Epoch(train) [45][1360/1879] lr: 2.0000e-03 eta: 10:41:10 time: 0.3274 data_time: 0.0148 memory: 6717 grad_norm: 2.8916 loss: 1.4391 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4391 2023/04/14 05:21:37 - mmengine - INFO - Epoch(train) [45][1380/1879] lr: 2.0000e-03 eta: 10:41:03 time: 0.3922 data_time: 0.0137 memory: 6717 grad_norm: 2.8725 loss: 1.4708 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4708 2023/04/14 05:21:44 - mmengine - INFO - Epoch(train) [45][1400/1879] lr: 2.0000e-03 eta: 10:40:55 time: 0.3357 data_time: 0.0163 memory: 6717 grad_norm: 2.8003 loss: 1.2892 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2892 2023/04/14 05:21:52 - mmengine - INFO - Epoch(train) [45][1420/1879] lr: 2.0000e-03 eta: 10:40:48 time: 0.3890 data_time: 0.0138 memory: 6717 grad_norm: 2.8455 loss: 1.4002 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4002 2023/04/14 05:21:59 - mmengine - INFO - Epoch(train) [45][1440/1879] lr: 2.0000e-03 eta: 10:40:40 time: 0.3739 data_time: 0.0176 memory: 6717 grad_norm: 2.8243 loss: 1.3693 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3693 2023/04/14 05:22:07 - mmengine - INFO - Epoch(train) [45][1460/1879] lr: 2.0000e-03 eta: 10:40:33 time: 0.3698 data_time: 0.0132 memory: 6717 grad_norm: 2.8434 loss: 1.3480 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3480 2023/04/14 05:22:14 - mmengine - INFO - Epoch(train) [45][1480/1879] lr: 2.0000e-03 eta: 10:40:26 time: 0.3780 data_time: 0.0157 memory: 6717 grad_norm: 2.8414 loss: 1.4771 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4771 2023/04/14 05:22:21 - mmengine - INFO - Epoch(train) [45][1500/1879] lr: 2.0000e-03 eta: 10:40:18 time: 0.3550 data_time: 0.0142 memory: 6717 grad_norm: 2.8869 loss: 1.2821 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2821 2023/04/14 05:22:28 - mmengine - INFO - Epoch(train) [45][1520/1879] lr: 2.0000e-03 eta: 10:40:10 time: 0.3605 data_time: 0.0153 memory: 6717 grad_norm: 2.7977 loss: 1.2153 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2153 2023/04/14 05:22:35 - mmengine - INFO - Epoch(train) [45][1540/1879] lr: 2.0000e-03 eta: 10:40:01 time: 0.3084 data_time: 0.0137 memory: 6717 grad_norm: 2.8652 loss: 1.3604 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.3604 2023/04/14 05:22:42 - mmengine - INFO - Epoch(train) [45][1560/1879] lr: 2.0000e-03 eta: 10:39:54 time: 0.3835 data_time: 0.0357 memory: 6717 grad_norm: 2.8479 loss: 1.3857 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3857 2023/04/14 05:22:50 - mmengine - INFO - Epoch(train) [45][1580/1879] lr: 2.0000e-03 eta: 10:39:47 time: 0.3875 data_time: 0.0314 memory: 6717 grad_norm: 2.8067 loss: 1.2824 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2824 2023/04/14 05:22:57 - mmengine - INFO - Epoch(train) [45][1600/1879] lr: 2.0000e-03 eta: 10:39:40 time: 0.3690 data_time: 0.0877 memory: 6717 grad_norm: 2.8131 loss: 1.4578 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.4578 2023/04/14 05:23:05 - mmengine - INFO - Epoch(train) [45][1620/1879] lr: 2.0000e-03 eta: 10:39:33 time: 0.3835 data_time: 0.0719 memory: 6717 grad_norm: 2.7565 loss: 1.0863 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0863 2023/04/14 05:23:12 - mmengine - INFO - Epoch(train) [45][1640/1879] lr: 2.0000e-03 eta: 10:39:25 time: 0.3423 data_time: 0.0671 memory: 6717 grad_norm: 2.8207 loss: 1.0806 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0806 2023/04/14 05:23:20 - mmengine - INFO - Epoch(train) [45][1660/1879] lr: 2.0000e-03 eta: 10:39:17 time: 0.3782 data_time: 0.0132 memory: 6717 grad_norm: 2.9133 loss: 1.3045 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3045 2023/04/14 05:23:27 - mmengine - INFO - Epoch(train) [45][1680/1879] lr: 2.0000e-03 eta: 10:39:10 time: 0.3883 data_time: 0.0284 memory: 6717 grad_norm: 2.9129 loss: 1.3628 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3628 2023/04/14 05:23:34 - mmengine - INFO - Epoch(train) [45][1700/1879] lr: 2.0000e-03 eta: 10:39:02 time: 0.3282 data_time: 0.1250 memory: 6717 grad_norm: 2.8087 loss: 1.1919 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1919 2023/04/14 05:23:42 - mmengine - INFO - Epoch(train) [45][1720/1879] lr: 2.0000e-03 eta: 10:38:56 time: 0.4191 data_time: 0.1263 memory: 6717 grad_norm: 2.9345 loss: 1.2278 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2278 2023/04/14 05:23:49 - mmengine - INFO - Epoch(train) [45][1740/1879] lr: 2.0000e-03 eta: 10:38:48 time: 0.3507 data_time: 0.0534 memory: 6717 grad_norm: 2.8133 loss: 1.2858 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2858 2023/04/14 05:23:57 - mmengine - INFO - Epoch(train) [45][1760/1879] lr: 2.0000e-03 eta: 10:38:41 time: 0.3860 data_time: 0.0930 memory: 6717 grad_norm: 2.7887 loss: 1.4475 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.4475 2023/04/14 05:24:04 - mmengine - INFO - Epoch(train) [45][1780/1879] lr: 2.0000e-03 eta: 10:38:33 time: 0.3380 data_time: 0.0180 memory: 6717 grad_norm: 2.8454 loss: 1.4150 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4150 2023/04/14 05:24:12 - mmengine - INFO - Epoch(train) [45][1800/1879] lr: 2.0000e-03 eta: 10:38:26 time: 0.3994 data_time: 0.0176 memory: 6717 grad_norm: 2.8666 loss: 1.2828 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2828 2023/04/14 05:24:18 - mmengine - INFO - Epoch(train) [45][1820/1879] lr: 2.0000e-03 eta: 10:38:17 time: 0.3257 data_time: 0.0173 memory: 6717 grad_norm: 2.8643 loss: 1.3286 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3286 2023/04/14 05:24:27 - mmengine - INFO - Epoch(train) [45][1840/1879] lr: 2.0000e-03 eta: 10:38:11 time: 0.4155 data_time: 0.0193 memory: 6717 grad_norm: 2.9365 loss: 1.4620 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.4620 2023/04/14 05:24:33 - mmengine - INFO - Epoch(train) [45][1860/1879] lr: 2.0000e-03 eta: 10:38:03 time: 0.3274 data_time: 0.0384 memory: 6717 grad_norm: 2.8497 loss: 1.5526 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5526 2023/04/14 05:24:39 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 05:24:39 - mmengine - INFO - Epoch(train) [45][1879/1879] lr: 2.0000e-03 eta: 10:37:55 time: 0.3216 data_time: 0.0531 memory: 6717 grad_norm: 2.8760 loss: 1.2997 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 1.2997 2023/04/14 05:24:39 - mmengine - INFO - Saving checkpoint at 45 epochs 2023/04/14 05:24:49 - mmengine - INFO - Epoch(val) [45][ 20/155] eta: 0:00:58 time: 0.4360 data_time: 0.4022 memory: 1391 2023/04/14 05:24:55 - mmengine - INFO - Epoch(val) [45][ 40/155] eta: 0:00:44 time: 0.3400 data_time: 0.3062 memory: 1391 2023/04/14 05:25:03 - mmengine - INFO - Epoch(val) [45][ 60/155] eta: 0:00:36 time: 0.3708 data_time: 0.3372 memory: 1391 2023/04/14 05:25:10 - mmengine - INFO - Epoch(val) [45][ 80/155] eta: 0:00:28 time: 0.3779 data_time: 0.3444 memory: 1391 2023/04/14 05:25:19 - mmengine - INFO - Epoch(val) [45][100/155] eta: 0:00:21 time: 0.4234 data_time: 0.3893 memory: 1391 2023/04/14 05:25:25 - mmengine - INFO - Epoch(val) [45][120/155] eta: 0:00:13 time: 0.3123 data_time: 0.2783 memory: 1391 2023/04/14 05:25:33 - mmengine - INFO - Epoch(val) [45][140/155] eta: 0:00:05 time: 0.3758 data_time: 0.3417 memory: 1391 2023/04/14 05:25:41 - mmengine - INFO - Epoch(val) [45][155/155] acc/top1: 0.6567 acc/top5: 0.8677 acc/mean1: 0.6566 data_time: 0.3200 time: 0.3533 2023/04/14 05:25:41 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_44.pth is removed 2023/04/14 05:25:42 - mmengine - INFO - The best checkpoint with 0.6567 acc/top1 at 45 epoch is saved to best_acc_top1_epoch_45.pth. 2023/04/14 05:25:52 - mmengine - INFO - Epoch(train) [46][ 20/1879] lr: 2.0000e-03 eta: 10:37:50 time: 0.4805 data_time: 0.2924 memory: 6717 grad_norm: 2.8494 loss: 1.3563 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3563 2023/04/14 05:25:58 - mmengine - INFO - Epoch(train) [46][ 40/1879] lr: 2.0000e-03 eta: 10:37:41 time: 0.3262 data_time: 0.1577 memory: 6717 grad_norm: 2.8678 loss: 1.1711 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1711 2023/04/14 05:26:07 - mmengine - INFO - Epoch(train) [46][ 60/1879] lr: 2.0000e-03 eta: 10:37:36 time: 0.4412 data_time: 0.1638 memory: 6717 grad_norm: 2.9278 loss: 1.2648 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2648 2023/04/14 05:26:13 - mmengine - INFO - Epoch(train) [46][ 80/1879] lr: 2.0000e-03 eta: 10:37:27 time: 0.3218 data_time: 0.0206 memory: 6717 grad_norm: 2.8366 loss: 1.1757 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1757 2023/04/14 05:26:21 - mmengine - INFO - Epoch(train) [46][ 100/1879] lr: 2.0000e-03 eta: 10:37:21 time: 0.4013 data_time: 0.0543 memory: 6717 grad_norm: 2.8566 loss: 1.2401 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2401 2023/04/14 05:26:27 - mmengine - INFO - Epoch(train) [46][ 120/1879] lr: 2.0000e-03 eta: 10:37:11 time: 0.2957 data_time: 0.0829 memory: 6717 grad_norm: 2.7928 loss: 1.3120 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3120 2023/04/14 05:26:36 - mmengine - INFO - Epoch(train) [46][ 140/1879] lr: 2.0000e-03 eta: 10:37:06 time: 0.4503 data_time: 0.1966 memory: 6717 grad_norm: 2.7588 loss: 1.1237 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1237 2023/04/14 05:26:42 - mmengine - INFO - Epoch(train) [46][ 160/1879] lr: 2.0000e-03 eta: 10:36:57 time: 0.3101 data_time: 0.0620 memory: 6717 grad_norm: 2.8832 loss: 1.3474 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3474 2023/04/14 05:26:51 - mmengine - INFO - Epoch(train) [46][ 180/1879] lr: 2.0000e-03 eta: 10:36:51 time: 0.4327 data_time: 0.1775 memory: 6717 grad_norm: 2.8064 loss: 1.2439 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2439 2023/04/14 05:26:57 - mmengine - INFO - Epoch(train) [46][ 200/1879] lr: 2.0000e-03 eta: 10:36:42 time: 0.2990 data_time: 0.1121 memory: 6717 grad_norm: 2.8522 loss: 1.4677 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4677 2023/04/14 05:27:05 - mmengine - INFO - Epoch(train) [46][ 220/1879] lr: 2.0000e-03 eta: 10:36:36 time: 0.4174 data_time: 0.1572 memory: 6717 grad_norm: 2.8785 loss: 1.3975 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3975 2023/04/14 05:27:12 - mmengine - INFO - Epoch(train) [46][ 240/1879] lr: 2.0000e-03 eta: 10:36:28 time: 0.3393 data_time: 0.0639 memory: 6717 grad_norm: 2.9185 loss: 1.5187 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.5187 2023/04/14 05:27:20 - mmengine - INFO - Epoch(train) [46][ 260/1879] lr: 2.0000e-03 eta: 10:36:21 time: 0.4091 data_time: 0.0585 memory: 6717 grad_norm: 2.7941 loss: 1.3056 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3056 2023/04/14 05:27:27 - mmengine - INFO - Epoch(train) [46][ 280/1879] lr: 2.0000e-03 eta: 10:36:12 time: 0.3212 data_time: 0.0235 memory: 6717 grad_norm: 2.8644 loss: 1.3316 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3316 2023/04/14 05:27:35 - mmengine - INFO - Epoch(train) [46][ 300/1879] lr: 2.0000e-03 eta: 10:36:06 time: 0.4236 data_time: 0.0159 memory: 6717 grad_norm: 2.8932 loss: 1.2969 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2969 2023/04/14 05:27:42 - mmengine - INFO - Epoch(train) [46][ 320/1879] lr: 2.0000e-03 eta: 10:35:58 time: 0.3216 data_time: 0.0138 memory: 6717 grad_norm: 2.9243 loss: 1.3332 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.3332 2023/04/14 05:27:51 - mmengine - INFO - Epoch(train) [46][ 340/1879] lr: 2.0000e-03 eta: 10:35:52 time: 0.4429 data_time: 0.0145 memory: 6717 grad_norm: 2.8710 loss: 1.1678 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1678 2023/04/14 05:27:57 - mmengine - INFO - Epoch(train) [46][ 360/1879] lr: 2.0000e-03 eta: 10:35:43 time: 0.2980 data_time: 0.0154 memory: 6717 grad_norm: 2.8017 loss: 1.2180 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2180 2023/04/14 05:28:05 - mmengine - INFO - Epoch(train) [46][ 380/1879] lr: 2.0000e-03 eta: 10:35:36 time: 0.4077 data_time: 0.0151 memory: 6717 grad_norm: 2.8533 loss: 1.1332 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1332 2023/04/14 05:28:11 - mmengine - INFO - Epoch(train) [46][ 400/1879] lr: 2.0000e-03 eta: 10:35:28 time: 0.3130 data_time: 0.0147 memory: 6717 grad_norm: 2.8877 loss: 1.2188 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2188 2023/04/14 05:28:20 - mmengine - INFO - Epoch(train) [46][ 420/1879] lr: 2.0000e-03 eta: 10:35:22 time: 0.4263 data_time: 0.0146 memory: 6717 grad_norm: 2.7920 loss: 1.4773 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4773 2023/04/14 05:28:26 - mmengine - INFO - Epoch(train) [46][ 440/1879] lr: 2.0000e-03 eta: 10:35:13 time: 0.3297 data_time: 0.0151 memory: 6717 grad_norm: 2.8334 loss: 1.5103 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.5103 2023/04/14 05:28:29 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 05:28:35 - mmengine - INFO - Epoch(train) [46][ 460/1879] lr: 2.0000e-03 eta: 10:35:07 time: 0.4203 data_time: 0.0133 memory: 6717 grad_norm: 2.8380 loss: 1.2817 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2817 2023/04/14 05:28:41 - mmengine - INFO - Epoch(train) [46][ 480/1879] lr: 2.0000e-03 eta: 10:34:58 time: 0.3064 data_time: 0.0147 memory: 6717 grad_norm: 2.8608 loss: 1.2391 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2391 2023/04/14 05:28:49 - mmengine - INFO - Epoch(train) [46][ 500/1879] lr: 2.0000e-03 eta: 10:34:52 time: 0.4068 data_time: 0.0150 memory: 6717 grad_norm: 2.8746 loss: 1.3101 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3101 2023/04/14 05:28:56 - mmengine - INFO - Epoch(train) [46][ 520/1879] lr: 2.0000e-03 eta: 10:34:44 time: 0.3574 data_time: 0.0137 memory: 6717 grad_norm: 2.8310 loss: 1.2269 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2269 2023/04/14 05:29:05 - mmengine - INFO - Epoch(train) [46][ 540/1879] lr: 2.0000e-03 eta: 10:34:38 time: 0.4295 data_time: 0.0167 memory: 6717 grad_norm: 2.8851 loss: 1.3345 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3345 2023/04/14 05:29:11 - mmengine - INFO - Epoch(train) [46][ 560/1879] lr: 2.0000e-03 eta: 10:34:29 time: 0.3171 data_time: 0.0160 memory: 6717 grad_norm: 2.8640 loss: 1.2548 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2548 2023/04/14 05:29:19 - mmengine - INFO - Epoch(train) [46][ 580/1879] lr: 2.0000e-03 eta: 10:34:22 time: 0.3859 data_time: 0.0143 memory: 6717 grad_norm: 2.9035 loss: 1.2210 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2210 2023/04/14 05:29:25 - mmengine - INFO - Epoch(train) [46][ 600/1879] lr: 2.0000e-03 eta: 10:34:14 time: 0.3218 data_time: 0.0157 memory: 6717 grad_norm: 2.8380 loss: 1.2927 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2927 2023/04/14 05:29:34 - mmengine - INFO - Epoch(train) [46][ 620/1879] lr: 2.0000e-03 eta: 10:34:08 time: 0.4306 data_time: 0.0143 memory: 6717 grad_norm: 2.8411 loss: 1.2833 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2833 2023/04/14 05:29:40 - mmengine - INFO - Epoch(train) [46][ 640/1879] lr: 2.0000e-03 eta: 10:33:59 time: 0.3302 data_time: 0.0146 memory: 6717 grad_norm: 2.8479 loss: 1.2612 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2612 2023/04/14 05:29:48 - mmengine - INFO - Epoch(train) [46][ 660/1879] lr: 2.0000e-03 eta: 10:33:53 time: 0.4050 data_time: 0.0139 memory: 6717 grad_norm: 2.7442 loss: 1.2742 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2742 2023/04/14 05:29:56 - mmengine - INFO - Epoch(train) [46][ 680/1879] lr: 2.0000e-03 eta: 10:33:45 time: 0.3570 data_time: 0.0278 memory: 6717 grad_norm: 2.9044 loss: 1.3447 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3447 2023/04/14 05:30:03 - mmengine - INFO - Epoch(train) [46][ 700/1879] lr: 2.0000e-03 eta: 10:33:38 time: 0.3806 data_time: 0.0129 memory: 6717 grad_norm: 2.8922 loss: 1.4326 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4326 2023/04/14 05:30:10 - mmengine - INFO - Epoch(train) [46][ 720/1879] lr: 2.0000e-03 eta: 10:33:29 time: 0.3246 data_time: 0.0195 memory: 6717 grad_norm: 2.8369 loss: 1.3150 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3150 2023/04/14 05:30:18 - mmengine - INFO - Epoch(train) [46][ 740/1879] lr: 2.0000e-03 eta: 10:33:22 time: 0.3950 data_time: 0.0858 memory: 6717 grad_norm: 2.8271 loss: 1.3515 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3515 2023/04/14 05:30:24 - mmengine - INFO - Epoch(train) [46][ 760/1879] lr: 2.0000e-03 eta: 10:33:14 time: 0.3459 data_time: 0.0329 memory: 6717 grad_norm: 2.8108 loss: 1.4104 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4104 2023/04/14 05:30:32 - mmengine - INFO - Epoch(train) [46][ 780/1879] lr: 2.0000e-03 eta: 10:33:07 time: 0.3872 data_time: 0.0126 memory: 6717 grad_norm: 2.9032 loss: 1.2140 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2140 2023/04/14 05:30:39 - mmengine - INFO - Epoch(train) [46][ 800/1879] lr: 2.0000e-03 eta: 10:32:59 time: 0.3227 data_time: 0.0146 memory: 6717 grad_norm: 2.8101 loss: 1.3294 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3294 2023/04/14 05:30:47 - mmengine - INFO - Epoch(train) [46][ 820/1879] lr: 2.0000e-03 eta: 10:32:52 time: 0.4029 data_time: 0.0147 memory: 6717 grad_norm: 2.8261 loss: 1.3318 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3318 2023/04/14 05:30:53 - mmengine - INFO - Epoch(train) [46][ 840/1879] lr: 2.0000e-03 eta: 10:32:44 time: 0.3272 data_time: 0.0143 memory: 6717 grad_norm: 2.8782 loss: 1.3004 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3004 2023/04/14 05:31:02 - mmengine - INFO - Epoch(train) [46][ 860/1879] lr: 2.0000e-03 eta: 10:32:37 time: 0.4172 data_time: 0.0159 memory: 6717 grad_norm: 2.7737 loss: 1.2296 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2296 2023/04/14 05:31:08 - mmengine - INFO - Epoch(train) [46][ 880/1879] lr: 2.0000e-03 eta: 10:32:29 time: 0.3264 data_time: 0.0139 memory: 6717 grad_norm: 2.8552 loss: 1.3814 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.3814 2023/04/14 05:31:17 - mmengine - INFO - Epoch(train) [46][ 900/1879] lr: 2.0000e-03 eta: 10:32:23 time: 0.4193 data_time: 0.0134 memory: 6717 grad_norm: 2.8453 loss: 1.2765 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2765 2023/04/14 05:31:23 - mmengine - INFO - Epoch(train) [46][ 920/1879] lr: 2.0000e-03 eta: 10:32:14 time: 0.3251 data_time: 0.0156 memory: 6717 grad_norm: 2.8783 loss: 1.1464 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1464 2023/04/14 05:31:31 - mmengine - INFO - Epoch(train) [46][ 940/1879] lr: 2.0000e-03 eta: 10:32:07 time: 0.3946 data_time: 0.0159 memory: 6717 grad_norm: 2.8107 loss: 1.2397 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.2397 2023/04/14 05:31:38 - mmengine - INFO - Epoch(train) [46][ 960/1879] lr: 2.0000e-03 eta: 10:31:59 time: 0.3377 data_time: 0.0127 memory: 6717 grad_norm: 2.8161 loss: 1.3575 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3575 2023/04/14 05:31:45 - mmengine - INFO - Epoch(train) [46][ 980/1879] lr: 2.0000e-03 eta: 10:31:52 time: 0.3781 data_time: 0.0168 memory: 6717 grad_norm: 2.8745 loss: 1.4137 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.4137 2023/04/14 05:31:53 - mmengine - INFO - Epoch(train) [46][1000/1879] lr: 2.0000e-03 eta: 10:31:45 time: 0.3781 data_time: 0.0129 memory: 6717 grad_norm: 2.8733 loss: 1.0838 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0838 2023/04/14 05:32:00 - mmengine - INFO - Epoch(train) [46][1020/1879] lr: 2.0000e-03 eta: 10:31:37 time: 0.3390 data_time: 0.0189 memory: 6717 grad_norm: 2.9020 loss: 1.2223 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2223 2023/04/14 05:32:08 - mmengine - INFO - Epoch(train) [46][1040/1879] lr: 2.0000e-03 eta: 10:31:31 time: 0.4377 data_time: 0.0129 memory: 6717 grad_norm: 2.8652 loss: 1.2598 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2598 2023/04/14 05:32:15 - mmengine - INFO - Epoch(train) [46][1060/1879] lr: 2.0000e-03 eta: 10:31:22 time: 0.3249 data_time: 0.0144 memory: 6717 grad_norm: 2.8195 loss: 1.4575 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4575 2023/04/14 05:32:23 - mmengine - INFO - Epoch(train) [46][1080/1879] lr: 2.0000e-03 eta: 10:31:15 time: 0.3890 data_time: 0.0144 memory: 6717 grad_norm: 2.7804 loss: 1.2529 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2529 2023/04/14 05:32:29 - mmengine - INFO - Epoch(train) [46][1100/1879] lr: 2.0000e-03 eta: 10:31:07 time: 0.3351 data_time: 0.0152 memory: 6717 grad_norm: 2.8553 loss: 1.2708 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2708 2023/04/14 05:32:37 - mmengine - INFO - Epoch(train) [46][1120/1879] lr: 2.0000e-03 eta: 10:31:00 time: 0.3987 data_time: 0.0133 memory: 6717 grad_norm: 2.8474 loss: 1.3100 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3100 2023/04/14 05:32:44 - mmengine - INFO - Epoch(train) [46][1140/1879] lr: 2.0000e-03 eta: 10:30:52 time: 0.3261 data_time: 0.0144 memory: 6717 grad_norm: 2.8075 loss: 1.1291 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1291 2023/04/14 05:32:52 - mmengine - INFO - Epoch(train) [46][1160/1879] lr: 2.0000e-03 eta: 10:30:45 time: 0.3879 data_time: 0.0337 memory: 6717 grad_norm: 2.8806 loss: 1.2635 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2635 2023/04/14 05:32:58 - mmengine - INFO - Epoch(train) [46][1180/1879] lr: 2.0000e-03 eta: 10:30:37 time: 0.3274 data_time: 0.0536 memory: 6717 grad_norm: 2.8483 loss: 1.2797 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2797 2023/04/14 05:33:07 - mmengine - INFO - Epoch(train) [46][1200/1879] lr: 2.0000e-03 eta: 10:30:31 time: 0.4332 data_time: 0.0159 memory: 6717 grad_norm: 2.8258 loss: 1.3343 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3343 2023/04/14 05:33:13 - mmengine - INFO - Epoch(train) [46][1220/1879] lr: 2.0000e-03 eta: 10:30:22 time: 0.3254 data_time: 0.0141 memory: 6717 grad_norm: 2.8718 loss: 1.2655 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2655 2023/04/14 05:33:21 - mmengine - INFO - Epoch(train) [46][1240/1879] lr: 2.0000e-03 eta: 10:30:16 time: 0.4043 data_time: 0.0138 memory: 6717 grad_norm: 2.7842 loss: 1.2572 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2572 2023/04/14 05:33:28 - mmengine - INFO - Epoch(train) [46][1260/1879] lr: 2.0000e-03 eta: 10:30:07 time: 0.3346 data_time: 0.0155 memory: 6717 grad_norm: 2.8345 loss: 1.3437 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3437 2023/04/14 05:33:36 - mmengine - INFO - Epoch(train) [46][1280/1879] lr: 2.0000e-03 eta: 10:30:01 time: 0.4094 data_time: 0.0131 memory: 6717 grad_norm: 2.8649 loss: 1.2737 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2737 2023/04/14 05:33:43 - mmengine - INFO - Epoch(train) [46][1300/1879] lr: 2.0000e-03 eta: 10:29:52 time: 0.3297 data_time: 0.0145 memory: 6717 grad_norm: 2.8276 loss: 1.1951 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1951 2023/04/14 05:33:51 - mmengine - INFO - Epoch(train) [46][1320/1879] lr: 2.0000e-03 eta: 10:29:46 time: 0.4115 data_time: 0.0125 memory: 6717 grad_norm: 2.9404 loss: 1.2119 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2119 2023/04/14 05:33:58 - mmengine - INFO - Epoch(train) [46][1340/1879] lr: 2.0000e-03 eta: 10:29:38 time: 0.3476 data_time: 0.0149 memory: 6717 grad_norm: 2.8580 loss: 1.2753 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2753 2023/04/14 05:34:06 - mmengine - INFO - Epoch(train) [46][1360/1879] lr: 2.0000e-03 eta: 10:29:31 time: 0.3896 data_time: 0.0138 memory: 6717 grad_norm: 2.8033 loss: 1.1184 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1184 2023/04/14 05:34:12 - mmengine - INFO - Epoch(train) [46][1380/1879] lr: 2.0000e-03 eta: 10:29:22 time: 0.2890 data_time: 0.0149 memory: 6717 grad_norm: 2.9214 loss: 1.3067 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3067 2023/04/14 05:34:19 - mmengine - INFO - Epoch(train) [46][1400/1879] lr: 2.0000e-03 eta: 10:29:15 time: 0.3775 data_time: 0.0138 memory: 6717 grad_norm: 2.8841 loss: 1.4748 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4748 2023/04/14 05:34:26 - mmengine - INFO - Epoch(train) [46][1420/1879] lr: 2.0000e-03 eta: 10:29:06 time: 0.3323 data_time: 0.0138 memory: 6717 grad_norm: 2.7970 loss: 1.2335 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2335 2023/04/14 05:34:34 - mmengine - INFO - Epoch(train) [46][1440/1879] lr: 2.0000e-03 eta: 10:29:00 time: 0.4185 data_time: 0.0134 memory: 6717 grad_norm: 2.8482 loss: 1.2366 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2366 2023/04/14 05:34:35 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 05:34:41 - mmengine - INFO - Epoch(train) [46][1460/1879] lr: 2.0000e-03 eta: 10:28:51 time: 0.3144 data_time: 0.0148 memory: 6717 grad_norm: 2.8320 loss: 1.4008 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.4008 2023/04/14 05:34:48 - mmengine - INFO - Epoch(train) [46][1480/1879] lr: 2.0000e-03 eta: 10:28:44 time: 0.3916 data_time: 0.0141 memory: 6717 grad_norm: 2.8552 loss: 1.2774 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2774 2023/04/14 05:34:56 - mmengine - INFO - Epoch(train) [46][1500/1879] lr: 2.0000e-03 eta: 10:28:37 time: 0.3756 data_time: 0.0153 memory: 6717 grad_norm: 2.9223 loss: 1.3573 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3573 2023/04/14 05:35:03 - mmengine - INFO - Epoch(train) [46][1520/1879] lr: 2.0000e-03 eta: 10:28:30 time: 0.3723 data_time: 0.0143 memory: 6717 grad_norm: 2.9055 loss: 1.1370 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1370 2023/04/14 05:35:10 - mmengine - INFO - Epoch(train) [46][1540/1879] lr: 2.0000e-03 eta: 10:28:22 time: 0.3561 data_time: 0.0148 memory: 6717 grad_norm: 2.9710 loss: 1.2602 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2602 2023/04/14 05:35:18 - mmengine - INFO - Epoch(train) [46][1560/1879] lr: 2.0000e-03 eta: 10:28:15 time: 0.3802 data_time: 0.0145 memory: 6717 grad_norm: 2.9249 loss: 1.2762 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2762 2023/04/14 05:35:25 - mmengine - INFO - Epoch(train) [46][1580/1879] lr: 2.0000e-03 eta: 10:28:06 time: 0.3251 data_time: 0.0143 memory: 6717 grad_norm: 2.8224 loss: 1.2724 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2724 2023/04/14 05:35:33 - mmengine - INFO - Epoch(train) [46][1600/1879] lr: 2.0000e-03 eta: 10:28:00 time: 0.4150 data_time: 0.0148 memory: 6717 grad_norm: 2.8613 loss: 1.3524 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3524 2023/04/14 05:35:39 - mmengine - INFO - Epoch(train) [46][1620/1879] lr: 2.0000e-03 eta: 10:27:52 time: 0.3283 data_time: 0.0143 memory: 6717 grad_norm: 2.8864 loss: 1.2362 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2362 2023/04/14 05:35:47 - mmengine - INFO - Epoch(train) [46][1640/1879] lr: 2.0000e-03 eta: 10:27:45 time: 0.3946 data_time: 0.0152 memory: 6717 grad_norm: 2.9123 loss: 1.3555 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3555 2023/04/14 05:35:54 - mmengine - INFO - Epoch(train) [46][1660/1879] lr: 2.0000e-03 eta: 10:27:36 time: 0.3323 data_time: 0.0143 memory: 6717 grad_norm: 2.9003 loss: 1.4906 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.4906 2023/04/14 05:36:02 - mmengine - INFO - Epoch(train) [46][1680/1879] lr: 2.0000e-03 eta: 10:27:29 time: 0.3796 data_time: 0.0239 memory: 6717 grad_norm: 2.8766 loss: 1.2369 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2369 2023/04/14 05:36:08 - mmengine - INFO - Epoch(train) [46][1700/1879] lr: 2.0000e-03 eta: 10:27:21 time: 0.3357 data_time: 0.0176 memory: 6717 grad_norm: 2.8134 loss: 1.4229 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4229 2023/04/14 05:36:17 - mmengine - INFO - Epoch(train) [46][1720/1879] lr: 2.0000e-03 eta: 10:27:15 time: 0.4178 data_time: 0.0136 memory: 6717 grad_norm: 2.9154 loss: 1.1554 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1554 2023/04/14 05:36:24 - mmengine - INFO - Epoch(train) [46][1740/1879] lr: 2.0000e-03 eta: 10:27:07 time: 0.3432 data_time: 0.0145 memory: 6717 grad_norm: 2.8387 loss: 1.3992 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3992 2023/04/14 05:36:31 - mmengine - INFO - Epoch(train) [46][1760/1879] lr: 2.0000e-03 eta: 10:26:59 time: 0.3709 data_time: 0.0150 memory: 6717 grad_norm: 2.8734 loss: 1.2321 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2321 2023/04/14 05:36:38 - mmengine - INFO - Epoch(train) [46][1780/1879] lr: 2.0000e-03 eta: 10:26:52 time: 0.3655 data_time: 0.0144 memory: 6717 grad_norm: 2.9029 loss: 1.3365 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3365 2023/04/14 05:36:46 - mmengine - INFO - Epoch(train) [46][1800/1879] lr: 2.0000e-03 eta: 10:26:44 time: 0.3713 data_time: 0.0155 memory: 6717 grad_norm: 2.8589 loss: 1.2531 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2531 2023/04/14 05:36:53 - mmengine - INFO - Epoch(train) [46][1820/1879] lr: 2.0000e-03 eta: 10:26:37 time: 0.3619 data_time: 0.0128 memory: 6717 grad_norm: 2.8057 loss: 1.1788 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1788 2023/04/14 05:37:00 - mmengine - INFO - Epoch(train) [46][1840/1879] lr: 2.0000e-03 eta: 10:26:29 time: 0.3628 data_time: 0.0162 memory: 6717 grad_norm: 2.8632 loss: 1.1514 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1514 2023/04/14 05:37:07 - mmengine - INFO - Epoch(train) [46][1860/1879] lr: 2.0000e-03 eta: 10:26:22 time: 0.3608 data_time: 0.0131 memory: 6717 grad_norm: 2.8250 loss: 1.1850 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1850 2023/04/14 05:37:15 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 05:37:15 - mmengine - INFO - Epoch(train) [46][1879/1879] lr: 2.0000e-03 eta: 10:26:15 time: 0.3691 data_time: 0.0138 memory: 6717 grad_norm: 3.6515 loss: 1.5386 top1_acc: 0.4286 top5_acc: 0.8571 loss_cls: 1.5386 2023/04/14 05:37:24 - mmengine - INFO - Epoch(val) [46][ 20/155] eta: 0:01:00 time: 0.4479 data_time: 0.4146 memory: 1391 2023/04/14 05:37:30 - mmengine - INFO - Epoch(val) [46][ 40/155] eta: 0:00:44 time: 0.3242 data_time: 0.2903 memory: 1391 2023/04/14 05:37:39 - mmengine - INFO - Epoch(val) [46][ 60/155] eta: 0:00:38 time: 0.4386 data_time: 0.4056 memory: 1391 2023/04/14 05:37:45 - mmengine - INFO - Epoch(val) [46][ 80/155] eta: 0:00:28 time: 0.3159 data_time: 0.2819 memory: 1391 2023/04/14 05:37:54 - mmengine - INFO - Epoch(val) [46][100/155] eta: 0:00:21 time: 0.4530 data_time: 0.4192 memory: 1391 2023/04/14 05:38:00 - mmengine - INFO - Epoch(val) [46][120/155] eta: 0:00:13 time: 0.3071 data_time: 0.2735 memory: 1391 2023/04/14 05:38:10 - mmengine - INFO - Epoch(val) [46][140/155] eta: 0:00:05 time: 0.4821 data_time: 0.4495 memory: 1391 2023/04/14 05:38:17 - mmengine - INFO - Epoch(val) [46][155/155] acc/top1: 0.6558 acc/top5: 0.8682 acc/mean1: 0.6557 data_time: 0.4177 time: 0.4498 2023/04/14 05:38:27 - mmengine - INFO - Epoch(train) [47][ 20/1879] lr: 2.0000e-03 eta: 10:26:10 time: 0.5008 data_time: 0.3582 memory: 6717 grad_norm: 2.7940 loss: 1.2370 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2370 2023/04/14 05:38:34 - mmengine - INFO - Epoch(train) [47][ 40/1879] lr: 2.0000e-03 eta: 10:26:02 time: 0.3162 data_time: 0.0803 memory: 6717 grad_norm: 2.8883 loss: 1.3619 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3619 2023/04/14 05:38:42 - mmengine - INFO - Epoch(train) [47][ 60/1879] lr: 2.0000e-03 eta: 10:25:55 time: 0.4149 data_time: 0.1303 memory: 6717 grad_norm: 2.9172 loss: 1.2955 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 1.2955 2023/04/14 05:38:48 - mmengine - INFO - Epoch(train) [47][ 80/1879] lr: 2.0000e-03 eta: 10:25:47 time: 0.3198 data_time: 0.0416 memory: 6717 grad_norm: 2.8002 loss: 0.9936 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9936 2023/04/14 05:38:57 - mmengine - INFO - Epoch(train) [47][ 100/1879] lr: 2.0000e-03 eta: 10:25:40 time: 0.4139 data_time: 0.0399 memory: 6717 grad_norm: 2.8340 loss: 1.2677 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2677 2023/04/14 05:39:04 - mmengine - INFO - Epoch(train) [47][ 120/1879] lr: 2.0000e-03 eta: 10:25:33 time: 0.3517 data_time: 0.0778 memory: 6717 grad_norm: 2.9571 loss: 1.3282 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3282 2023/04/14 05:39:12 - mmengine - INFO - Epoch(train) [47][ 140/1879] lr: 2.0000e-03 eta: 10:25:26 time: 0.4132 data_time: 0.1094 memory: 6717 grad_norm: 2.8976 loss: 1.1816 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1816 2023/04/14 05:39:18 - mmengine - INFO - Epoch(train) [47][ 160/1879] lr: 2.0000e-03 eta: 10:25:17 time: 0.2976 data_time: 0.0305 memory: 6717 grad_norm: 2.9331 loss: 1.2304 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2304 2023/04/14 05:39:26 - mmengine - INFO - Epoch(train) [47][ 180/1879] lr: 2.0000e-03 eta: 10:25:10 time: 0.3932 data_time: 0.0146 memory: 6717 grad_norm: 2.8707 loss: 1.1553 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1553 2023/04/14 05:39:32 - mmengine - INFO - Epoch(train) [47][ 200/1879] lr: 2.0000e-03 eta: 10:25:02 time: 0.3303 data_time: 0.0140 memory: 6717 grad_norm: 2.9240 loss: 1.1622 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1622 2023/04/14 05:39:40 - mmengine - INFO - Epoch(train) [47][ 220/1879] lr: 2.0000e-03 eta: 10:24:55 time: 0.3802 data_time: 0.0263 memory: 6717 grad_norm: 2.8905 loss: 1.3576 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3576 2023/04/14 05:39:47 - mmengine - INFO - Epoch(train) [47][ 240/1879] lr: 2.0000e-03 eta: 10:24:47 time: 0.3504 data_time: 0.0753 memory: 6717 grad_norm: 2.8821 loss: 1.2332 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2332 2023/04/14 05:39:55 - mmengine - INFO - Epoch(train) [47][ 260/1879] lr: 2.0000e-03 eta: 10:24:40 time: 0.4151 data_time: 0.0292 memory: 6717 grad_norm: 2.9026 loss: 1.1689 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1689 2023/04/14 05:40:02 - mmengine - INFO - Epoch(train) [47][ 280/1879] lr: 2.0000e-03 eta: 10:24:32 time: 0.3163 data_time: 0.0131 memory: 6717 grad_norm: 2.8628 loss: 1.1042 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1042 2023/04/14 05:40:10 - mmengine - INFO - Epoch(train) [47][ 300/1879] lr: 2.0000e-03 eta: 10:24:25 time: 0.4111 data_time: 0.0965 memory: 6717 grad_norm: 2.9157 loss: 1.3209 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3209 2023/04/14 05:40:17 - mmengine - INFO - Epoch(train) [47][ 320/1879] lr: 2.0000e-03 eta: 10:24:17 time: 0.3423 data_time: 0.1477 memory: 6717 grad_norm: 2.8244 loss: 1.1810 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1810 2023/04/14 05:40:24 - mmengine - INFO - Epoch(train) [47][ 340/1879] lr: 2.0000e-03 eta: 10:24:10 time: 0.3709 data_time: 0.1473 memory: 6717 grad_norm: 2.8723 loss: 1.2464 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2464 2023/04/14 05:40:31 - mmengine - INFO - Epoch(train) [47][ 360/1879] lr: 2.0000e-03 eta: 10:24:02 time: 0.3406 data_time: 0.1884 memory: 6717 grad_norm: 2.9183 loss: 1.3397 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.3397 2023/04/14 05:40:39 - mmengine - INFO - Epoch(train) [47][ 380/1879] lr: 2.0000e-03 eta: 10:23:55 time: 0.4115 data_time: 0.1975 memory: 6717 grad_norm: 2.8380 loss: 1.2805 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2805 2023/04/14 05:40:45 - mmengine - INFO - Epoch(train) [47][ 400/1879] lr: 2.0000e-03 eta: 10:23:47 time: 0.3173 data_time: 0.0787 memory: 6717 grad_norm: 2.8692 loss: 1.1034 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1034 2023/04/14 05:40:54 - mmengine - INFO - Epoch(train) [47][ 420/1879] lr: 2.0000e-03 eta: 10:23:41 time: 0.4500 data_time: 0.2150 memory: 6717 grad_norm: 2.9107 loss: 1.1745 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1745 2023/04/14 05:41:01 - mmengine - INFO - Epoch(train) [47][ 440/1879] lr: 2.0000e-03 eta: 10:23:33 time: 0.3289 data_time: 0.1740 memory: 6717 grad_norm: 2.9443 loss: 1.2307 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2307 2023/04/14 05:41:09 - mmengine - INFO - Epoch(train) [47][ 460/1879] lr: 2.0000e-03 eta: 10:23:26 time: 0.4187 data_time: 0.2739 memory: 6717 grad_norm: 2.8764 loss: 1.4248 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4248 2023/04/14 05:41:16 - mmengine - INFO - Epoch(train) [47][ 480/1879] lr: 2.0000e-03 eta: 10:23:18 time: 0.3403 data_time: 0.1934 memory: 6717 grad_norm: 2.9128 loss: 1.2441 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.2441 2023/04/14 05:41:23 - mmengine - INFO - Epoch(train) [47][ 500/1879] lr: 2.0000e-03 eta: 10:23:11 time: 0.3626 data_time: 0.2235 memory: 6717 grad_norm: 2.8502 loss: 1.3793 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3793 2023/04/14 05:41:30 - mmengine - INFO - Epoch(train) [47][ 520/1879] lr: 2.0000e-03 eta: 10:23:02 time: 0.3221 data_time: 0.1360 memory: 6717 grad_norm: 2.8613 loss: 1.1670 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1670 2023/04/14 05:41:38 - mmengine - INFO - Epoch(train) [47][ 540/1879] lr: 2.0000e-03 eta: 10:22:56 time: 0.4018 data_time: 0.2373 memory: 6717 grad_norm: 2.8611 loss: 1.1440 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1440 2023/04/14 05:41:45 - mmengine - INFO - Epoch(train) [47][ 560/1879] lr: 2.0000e-03 eta: 10:22:48 time: 0.3497 data_time: 0.2067 memory: 6717 grad_norm: 2.9532 loss: 1.4296 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.4296 2023/04/14 05:41:47 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 05:41:53 - mmengine - INFO - Epoch(train) [47][ 580/1879] lr: 2.0000e-03 eta: 10:22:41 time: 0.4075 data_time: 0.2568 memory: 6717 grad_norm: 2.8612 loss: 1.2041 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2041 2023/04/14 05:42:00 - mmengine - INFO - Epoch(train) [47][ 600/1879] lr: 2.0000e-03 eta: 10:22:33 time: 0.3332 data_time: 0.1917 memory: 6717 grad_norm: 2.9098 loss: 1.2188 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2188 2023/04/14 05:42:07 - mmengine - INFO - Epoch(train) [47][ 620/1879] lr: 2.0000e-03 eta: 10:22:25 time: 0.3581 data_time: 0.2196 memory: 6717 grad_norm: 2.9323 loss: 1.3028 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3028 2023/04/14 05:42:14 - mmengine - INFO - Epoch(train) [47][ 640/1879] lr: 2.0000e-03 eta: 10:22:18 time: 0.3594 data_time: 0.2270 memory: 6717 grad_norm: 2.8658 loss: 1.3102 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3102 2023/04/14 05:42:21 - mmengine - INFO - Epoch(train) [47][ 660/1879] lr: 2.0000e-03 eta: 10:22:09 time: 0.3250 data_time: 0.1830 memory: 6717 grad_norm: 2.8851 loss: 1.1538 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1538 2023/04/14 05:42:29 - mmengine - INFO - Epoch(train) [47][ 680/1879] lr: 2.0000e-03 eta: 10:22:03 time: 0.4326 data_time: 0.1463 memory: 6717 grad_norm: 2.8972 loss: 1.2179 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2179 2023/04/14 05:42:36 - mmengine - INFO - Epoch(train) [47][ 700/1879] lr: 2.0000e-03 eta: 10:21:55 time: 0.3368 data_time: 0.0604 memory: 6717 grad_norm: 2.9152 loss: 1.2239 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2239 2023/04/14 05:42:45 - mmengine - INFO - Epoch(train) [47][ 720/1879] lr: 2.0000e-03 eta: 10:21:49 time: 0.4443 data_time: 0.0985 memory: 6717 grad_norm: 2.8604 loss: 1.2662 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2662 2023/04/14 05:42:51 - mmengine - INFO - Epoch(train) [47][ 740/1879] lr: 2.0000e-03 eta: 10:21:40 time: 0.2866 data_time: 0.0265 memory: 6717 grad_norm: 2.8562 loss: 1.4266 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4266 2023/04/14 05:42:59 - mmengine - INFO - Epoch(train) [47][ 760/1879] lr: 2.0000e-03 eta: 10:21:33 time: 0.4142 data_time: 0.1549 memory: 6717 grad_norm: 2.8191 loss: 1.4306 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4306 2023/04/14 05:43:05 - mmengine - INFO - Epoch(train) [47][ 780/1879] lr: 2.0000e-03 eta: 10:21:25 time: 0.3228 data_time: 0.0687 memory: 6717 grad_norm: 2.8191 loss: 1.1279 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1279 2023/04/14 05:43:13 - mmengine - INFO - Epoch(train) [47][ 800/1879] lr: 2.0000e-03 eta: 10:21:18 time: 0.3856 data_time: 0.0998 memory: 6717 grad_norm: 2.8154 loss: 1.1936 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1936 2023/04/14 05:43:20 - mmengine - INFO - Epoch(train) [47][ 820/1879] lr: 2.0000e-03 eta: 10:21:10 time: 0.3519 data_time: 0.0251 memory: 6717 grad_norm: 2.9289 loss: 1.3448 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3448 2023/04/14 05:43:28 - mmengine - INFO - Epoch(train) [47][ 840/1879] lr: 2.0000e-03 eta: 10:21:03 time: 0.3929 data_time: 0.0716 memory: 6717 grad_norm: 2.8647 loss: 1.3095 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3095 2023/04/14 05:43:35 - mmengine - INFO - Epoch(train) [47][ 860/1879] lr: 2.0000e-03 eta: 10:20:56 time: 0.3580 data_time: 0.1281 memory: 6717 grad_norm: 2.8817 loss: 1.2279 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2279 2023/04/14 05:43:43 - mmengine - INFO - Epoch(train) [47][ 880/1879] lr: 2.0000e-03 eta: 10:20:49 time: 0.4060 data_time: 0.1714 memory: 6717 grad_norm: 2.9466 loss: 1.1302 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1302 2023/04/14 05:43:50 - mmengine - INFO - Epoch(train) [47][ 900/1879] lr: 2.0000e-03 eta: 10:20:40 time: 0.3105 data_time: 0.1665 memory: 6717 grad_norm: 2.8782 loss: 1.3949 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3949 2023/04/14 05:43:58 - mmengine - INFO - Epoch(train) [47][ 920/1879] lr: 2.0000e-03 eta: 10:20:33 time: 0.4016 data_time: 0.2662 memory: 6717 grad_norm: 2.8410 loss: 1.1677 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.1677 2023/04/14 05:44:04 - mmengine - INFO - Epoch(train) [47][ 940/1879] lr: 2.0000e-03 eta: 10:20:25 time: 0.3175 data_time: 0.1789 memory: 6717 grad_norm: 2.8933 loss: 1.2534 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2534 2023/04/14 05:44:12 - mmengine - INFO - Epoch(train) [47][ 960/1879] lr: 2.0000e-03 eta: 10:20:19 time: 0.4264 data_time: 0.1944 memory: 6717 grad_norm: 2.9356 loss: 1.2104 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2104 2023/04/14 05:44:19 - mmengine - INFO - Epoch(train) [47][ 980/1879] lr: 2.0000e-03 eta: 10:20:11 time: 0.3407 data_time: 0.1363 memory: 6717 grad_norm: 2.9092 loss: 1.3739 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3739 2023/04/14 05:44:27 - mmengine - INFO - Epoch(train) [47][1000/1879] lr: 2.0000e-03 eta: 10:20:04 time: 0.4119 data_time: 0.1433 memory: 6717 grad_norm: 2.8981 loss: 1.4996 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4996 2023/04/14 05:44:34 - mmengine - INFO - Epoch(train) [47][1020/1879] lr: 2.0000e-03 eta: 10:19:55 time: 0.3129 data_time: 0.1115 memory: 6717 grad_norm: 2.8948 loss: 1.4347 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4347 2023/04/14 05:44:43 - mmengine - INFO - Epoch(train) [47][1040/1879] lr: 2.0000e-03 eta: 10:19:50 time: 0.4417 data_time: 0.0984 memory: 6717 grad_norm: 2.8975 loss: 1.4827 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.4827 2023/04/14 05:44:49 - mmengine - INFO - Epoch(train) [47][1060/1879] lr: 2.0000e-03 eta: 10:19:41 time: 0.2980 data_time: 0.0123 memory: 6717 grad_norm: 2.7907 loss: 1.2396 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2396 2023/04/14 05:44:57 - mmengine - INFO - Epoch(train) [47][1080/1879] lr: 2.0000e-03 eta: 10:19:35 time: 0.4289 data_time: 0.0158 memory: 6717 grad_norm: 2.8652 loss: 1.1737 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1737 2023/04/14 05:45:04 - mmengine - INFO - Epoch(train) [47][1100/1879] lr: 2.0000e-03 eta: 10:19:26 time: 0.3358 data_time: 0.0140 memory: 6717 grad_norm: 2.8646 loss: 1.2661 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2661 2023/04/14 05:45:12 - mmengine - INFO - Epoch(train) [47][1120/1879] lr: 2.0000e-03 eta: 10:19:20 time: 0.4224 data_time: 0.0184 memory: 6717 grad_norm: 2.8342 loss: 1.2606 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.2606 2023/04/14 05:45:19 - mmengine - INFO - Epoch(train) [47][1140/1879] lr: 2.0000e-03 eta: 10:19:12 time: 0.3381 data_time: 0.0140 memory: 6717 grad_norm: 2.8922 loss: 1.3951 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3951 2023/04/14 05:45:27 - mmengine - INFO - Epoch(train) [47][1160/1879] lr: 2.0000e-03 eta: 10:19:05 time: 0.3884 data_time: 0.0146 memory: 6717 grad_norm: 2.9117 loss: 1.2407 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2407 2023/04/14 05:45:33 - mmengine - INFO - Epoch(train) [47][1180/1879] lr: 2.0000e-03 eta: 10:18:56 time: 0.3151 data_time: 0.0146 memory: 6717 grad_norm: 2.8399 loss: 1.3023 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3023 2023/04/14 05:45:42 - mmengine - INFO - Epoch(train) [47][1200/1879] lr: 2.0000e-03 eta: 10:18:50 time: 0.4283 data_time: 0.0141 memory: 6717 grad_norm: 2.8974 loss: 1.4576 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4576 2023/04/14 05:45:47 - mmengine - INFO - Epoch(train) [47][1220/1879] lr: 2.0000e-03 eta: 10:18:41 time: 0.2832 data_time: 0.0151 memory: 6717 grad_norm: 2.8500 loss: 0.9975 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9975 2023/04/14 05:45:56 - mmengine - INFO - Epoch(train) [47][1240/1879] lr: 2.0000e-03 eta: 10:18:35 time: 0.4160 data_time: 0.0128 memory: 6717 grad_norm: 2.8924 loss: 1.1823 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1823 2023/04/14 05:46:02 - mmengine - INFO - Epoch(train) [47][1260/1879] lr: 2.0000e-03 eta: 10:18:26 time: 0.3068 data_time: 0.0153 memory: 6717 grad_norm: 2.8851 loss: 1.3949 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3949 2023/04/14 05:46:10 - mmengine - INFO - Epoch(train) [47][1280/1879] lr: 2.0000e-03 eta: 10:18:20 time: 0.4312 data_time: 0.0135 memory: 6717 grad_norm: 2.8680 loss: 1.1646 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1646 2023/04/14 05:46:17 - mmengine - INFO - Epoch(train) [47][1300/1879] lr: 2.0000e-03 eta: 10:18:11 time: 0.3235 data_time: 0.0208 memory: 6717 grad_norm: 2.9614 loss: 1.3963 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3963 2023/04/14 05:46:25 - mmengine - INFO - Epoch(train) [47][1320/1879] lr: 2.0000e-03 eta: 10:18:05 time: 0.4174 data_time: 0.0852 memory: 6717 grad_norm: 2.8103 loss: 1.2775 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2775 2023/04/14 05:46:32 - mmengine - INFO - Epoch(train) [47][1340/1879] lr: 2.0000e-03 eta: 10:17:56 time: 0.3273 data_time: 0.0459 memory: 6717 grad_norm: 2.9673 loss: 1.3634 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3634 2023/04/14 05:46:40 - mmengine - INFO - Epoch(train) [47][1360/1879] lr: 2.0000e-03 eta: 10:17:50 time: 0.4170 data_time: 0.0437 memory: 6717 grad_norm: 2.8386 loss: 1.1936 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1936 2023/04/14 05:46:47 - mmengine - INFO - Epoch(train) [47][1380/1879] lr: 2.0000e-03 eta: 10:17:42 time: 0.3287 data_time: 0.0140 memory: 6717 grad_norm: 2.9704 loss: 1.3786 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3786 2023/04/14 05:46:55 - mmengine - INFO - Epoch(train) [47][1400/1879] lr: 2.0000e-03 eta: 10:17:35 time: 0.4048 data_time: 0.0146 memory: 6717 grad_norm: 2.8873 loss: 1.4408 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4408 2023/04/14 05:47:01 - mmengine - INFO - Epoch(train) [47][1420/1879] lr: 2.0000e-03 eta: 10:17:27 time: 0.3287 data_time: 0.0126 memory: 6717 grad_norm: 2.8754 loss: 1.4263 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4263 2023/04/14 05:47:10 - mmengine - INFO - Epoch(train) [47][1440/1879] lr: 2.0000e-03 eta: 10:17:21 time: 0.4205 data_time: 0.0157 memory: 6717 grad_norm: 2.9113 loss: 1.2388 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2388 2023/04/14 05:47:16 - mmengine - INFO - Epoch(train) [47][1460/1879] lr: 2.0000e-03 eta: 10:17:12 time: 0.3111 data_time: 0.0118 memory: 6717 grad_norm: 2.7839 loss: 1.0507 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0507 2023/04/14 05:47:24 - mmengine - INFO - Epoch(train) [47][1480/1879] lr: 2.0000e-03 eta: 10:17:05 time: 0.4013 data_time: 0.0302 memory: 6717 grad_norm: 2.8955 loss: 1.2884 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2884 2023/04/14 05:47:31 - mmengine - INFO - Epoch(train) [47][1500/1879] lr: 2.0000e-03 eta: 10:16:57 time: 0.3490 data_time: 0.0256 memory: 6717 grad_norm: 2.9247 loss: 1.3258 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.3258 2023/04/14 05:47:39 - mmengine - INFO - Epoch(train) [47][1520/1879] lr: 2.0000e-03 eta: 10:16:50 time: 0.3895 data_time: 0.0241 memory: 6717 grad_norm: 2.9265 loss: 1.2046 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2046 2023/04/14 05:47:46 - mmengine - INFO - Epoch(train) [47][1540/1879] lr: 2.0000e-03 eta: 10:16:42 time: 0.3313 data_time: 0.0509 memory: 6717 grad_norm: 2.9160 loss: 1.3729 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3729 2023/04/14 05:47:53 - mmengine - INFO - Epoch(train) [47][1560/1879] lr: 2.0000e-03 eta: 10:16:35 time: 0.3837 data_time: 0.1469 memory: 6717 grad_norm: 2.8605 loss: 1.0672 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0672 2023/04/14 05:47:54 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 05:48:01 - mmengine - INFO - Epoch(train) [47][1580/1879] lr: 2.0000e-03 eta: 10:16:27 time: 0.3661 data_time: 0.0916 memory: 6717 grad_norm: 2.8743 loss: 1.2998 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2998 2023/04/14 05:48:09 - mmengine - INFO - Epoch(train) [47][1600/1879] lr: 2.0000e-03 eta: 10:16:21 time: 0.4366 data_time: 0.0177 memory: 6717 grad_norm: 2.8270 loss: 1.2709 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2709 2023/04/14 05:48:16 - mmengine - INFO - Epoch(train) [47][1620/1879] lr: 2.0000e-03 eta: 10:16:13 time: 0.3450 data_time: 0.0123 memory: 6717 grad_norm: 2.8550 loss: 1.4324 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4324 2023/04/14 05:48:24 - mmengine - INFO - Epoch(train) [47][1640/1879] lr: 2.0000e-03 eta: 10:16:06 time: 0.3834 data_time: 0.0149 memory: 6717 grad_norm: 2.9631 loss: 1.2817 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2817 2023/04/14 05:48:30 - mmengine - INFO - Epoch(train) [47][1660/1879] lr: 2.0000e-03 eta: 10:15:58 time: 0.3320 data_time: 0.0147 memory: 6717 grad_norm: 2.9522 loss: 1.1640 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1640 2023/04/14 05:48:39 - mmengine - INFO - Epoch(train) [47][1680/1879] lr: 2.0000e-03 eta: 10:15:52 time: 0.4133 data_time: 0.0154 memory: 6717 grad_norm: 2.8627 loss: 1.2684 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2684 2023/04/14 05:48:45 - mmengine - INFO - Epoch(train) [47][1700/1879] lr: 2.0000e-03 eta: 10:15:43 time: 0.3123 data_time: 0.0290 memory: 6717 grad_norm: 2.8649 loss: 1.1147 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1147 2023/04/14 05:48:53 - mmengine - INFO - Epoch(train) [47][1720/1879] lr: 2.0000e-03 eta: 10:15:36 time: 0.4094 data_time: 0.0167 memory: 6717 grad_norm: 2.9420 loss: 1.1570 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1570 2023/04/14 05:49:00 - mmengine - INFO - Epoch(train) [47][1740/1879] lr: 2.0000e-03 eta: 10:15:28 time: 0.3174 data_time: 0.0126 memory: 6717 grad_norm: 2.8380 loss: 1.2522 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2522 2023/04/14 05:49:08 - mmengine - INFO - Epoch(train) [47][1760/1879] lr: 2.0000e-03 eta: 10:15:21 time: 0.3997 data_time: 0.0157 memory: 6717 grad_norm: 2.9324 loss: 1.3565 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.3565 2023/04/14 05:49:14 - mmengine - INFO - Epoch(train) [47][1780/1879] lr: 2.0000e-03 eta: 10:15:13 time: 0.3385 data_time: 0.0137 memory: 6717 grad_norm: 2.8536 loss: 1.2199 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.2199 2023/04/14 05:49:24 - mmengine - INFO - Epoch(train) [47][1800/1879] lr: 2.0000e-03 eta: 10:15:08 time: 0.4700 data_time: 0.0128 memory: 6717 grad_norm: 2.8925 loss: 1.1976 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1976 2023/04/14 05:49:30 - mmengine - INFO - Epoch(train) [47][1820/1879] lr: 2.0000e-03 eta: 10:14:59 time: 0.3007 data_time: 0.0152 memory: 6717 grad_norm: 2.8753 loss: 1.1276 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1276 2023/04/14 05:49:37 - mmengine - INFO - Epoch(train) [47][1840/1879] lr: 2.0000e-03 eta: 10:14:52 time: 0.3873 data_time: 0.0468 memory: 6717 grad_norm: 2.9503 loss: 1.1609 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1609 2023/04/14 05:49:44 - mmengine - INFO - Epoch(train) [47][1860/1879] lr: 2.0000e-03 eta: 10:14:44 time: 0.3361 data_time: 0.0542 memory: 6717 grad_norm: 2.8663 loss: 1.2395 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2395 2023/04/14 05:49:50 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 05:49:50 - mmengine - INFO - Epoch(train) [47][1879/1879] lr: 2.0000e-03 eta: 10:14:36 time: 0.3140 data_time: 0.0378 memory: 6717 grad_norm: 2.9483 loss: 1.2932 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.2932 2023/04/14 05:50:00 - mmengine - INFO - Epoch(val) [47][ 20/155] eta: 0:01:01 time: 0.4575 data_time: 0.4239 memory: 1391 2023/04/14 05:50:06 - mmengine - INFO - Epoch(val) [47][ 40/155] eta: 0:00:44 time: 0.3118 data_time: 0.2788 memory: 1391 2023/04/14 05:50:14 - mmengine - INFO - Epoch(val) [47][ 60/155] eta: 0:00:38 time: 0.4328 data_time: 0.3995 memory: 1391 2023/04/14 05:50:21 - mmengine - INFO - Epoch(val) [47][ 80/155] eta: 0:00:28 time: 0.3189 data_time: 0.2857 memory: 1391 2023/04/14 05:50:29 - mmengine - INFO - Epoch(val) [47][100/155] eta: 0:00:21 time: 0.4212 data_time: 0.3884 memory: 1391 2023/04/14 05:50:36 - mmengine - INFO - Epoch(val) [47][120/155] eta: 0:00:13 time: 0.3406 data_time: 0.3077 memory: 1391 2023/04/14 05:50:46 - mmengine - INFO - Epoch(val) [47][140/155] eta: 0:00:05 time: 0.4848 data_time: 0.4520 memory: 1391 2023/04/14 05:50:53 - mmengine - INFO - Epoch(val) [47][155/155] acc/top1: 0.6565 acc/top5: 0.8680 acc/mean1: 0.6565 data_time: 0.4214 time: 0.4534 2023/04/14 05:51:03 - mmengine - INFO - Epoch(train) [48][ 20/1879] lr: 2.0000e-03 eta: 10:14:31 time: 0.4941 data_time: 0.3256 memory: 6717 grad_norm: 2.8799 loss: 1.3579 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3579 2023/04/14 05:51:09 - mmengine - INFO - Epoch(train) [48][ 40/1879] lr: 2.0000e-03 eta: 10:14:22 time: 0.3243 data_time: 0.1732 memory: 6717 grad_norm: 2.8929 loss: 1.4275 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.4275 2023/04/14 05:51:18 - mmengine - INFO - Epoch(train) [48][ 60/1879] lr: 2.0000e-03 eta: 10:14:16 time: 0.4188 data_time: 0.2499 memory: 6717 grad_norm: 2.8993 loss: 1.2122 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2122 2023/04/14 05:51:25 - mmengine - INFO - Epoch(train) [48][ 80/1879] lr: 2.0000e-03 eta: 10:14:08 time: 0.3442 data_time: 0.2113 memory: 6717 grad_norm: 2.8323 loss: 1.2520 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.2520 2023/04/14 05:51:32 - mmengine - INFO - Epoch(train) [48][ 100/1879] lr: 2.0000e-03 eta: 10:14:01 time: 0.3923 data_time: 0.2558 memory: 6717 grad_norm: 2.9620 loss: 1.2191 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2191 2023/04/14 05:51:39 - mmengine - INFO - Epoch(train) [48][ 120/1879] lr: 2.0000e-03 eta: 10:13:53 time: 0.3516 data_time: 0.2011 memory: 6717 grad_norm: 2.9213 loss: 1.2143 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2143 2023/04/14 05:51:48 - mmengine - INFO - Epoch(train) [48][ 140/1879] lr: 2.0000e-03 eta: 10:13:47 time: 0.4174 data_time: 0.2594 memory: 6717 grad_norm: 2.8707 loss: 1.2722 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2722 2023/04/14 05:51:55 - mmengine - INFO - Epoch(train) [48][ 160/1879] lr: 2.0000e-03 eta: 10:13:39 time: 0.3422 data_time: 0.1649 memory: 6717 grad_norm: 2.8987 loss: 1.2291 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.2291 2023/04/14 05:52:02 - mmengine - INFO - Epoch(train) [48][ 180/1879] lr: 2.0000e-03 eta: 10:13:32 time: 0.3874 data_time: 0.2148 memory: 6717 grad_norm: 2.8205 loss: 1.1799 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1799 2023/04/14 05:52:10 - mmengine - INFO - Epoch(train) [48][ 200/1879] lr: 2.0000e-03 eta: 10:13:24 time: 0.3650 data_time: 0.1841 memory: 6717 grad_norm: 2.9543 loss: 1.4126 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.4126 2023/04/14 05:52:17 - mmengine - INFO - Epoch(train) [48][ 220/1879] lr: 2.0000e-03 eta: 10:13:17 time: 0.3487 data_time: 0.2027 memory: 6717 grad_norm: 2.9676 loss: 0.9949 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9949 2023/04/14 05:52:24 - mmengine - INFO - Epoch(train) [48][ 240/1879] lr: 2.0000e-03 eta: 10:13:10 time: 0.3852 data_time: 0.2336 memory: 6717 grad_norm: 2.9031 loss: 1.1848 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1848 2023/04/14 05:52:31 - mmengine - INFO - Epoch(train) [48][ 260/1879] lr: 2.0000e-03 eta: 10:13:01 time: 0.3424 data_time: 0.1929 memory: 6717 grad_norm: 2.8636 loss: 1.3853 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3853 2023/04/14 05:52:39 - mmengine - INFO - Epoch(train) [48][ 280/1879] lr: 2.0000e-03 eta: 10:12:54 time: 0.3623 data_time: 0.0708 memory: 6717 grad_norm: 2.9635 loss: 1.3662 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3662 2023/04/14 05:52:46 - mmengine - INFO - Epoch(train) [48][ 300/1879] lr: 2.0000e-03 eta: 10:12:46 time: 0.3577 data_time: 0.1567 memory: 6717 grad_norm: 2.8720 loss: 1.2261 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2261 2023/04/14 05:52:53 - mmengine - INFO - Epoch(train) [48][ 320/1879] lr: 2.0000e-03 eta: 10:12:39 time: 0.3681 data_time: 0.1191 memory: 6717 grad_norm: 2.9003 loss: 1.2262 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2262 2023/04/14 05:53:01 - mmengine - INFO - Epoch(train) [48][ 340/1879] lr: 2.0000e-03 eta: 10:12:32 time: 0.4184 data_time: 0.1117 memory: 6717 grad_norm: 2.9002 loss: 1.1671 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1671 2023/04/14 05:53:08 - mmengine - INFO - Epoch(train) [48][ 360/1879] lr: 2.0000e-03 eta: 10:12:24 time: 0.3198 data_time: 0.0960 memory: 6717 grad_norm: 2.8477 loss: 1.1547 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1547 2023/04/14 05:53:16 - mmengine - INFO - Epoch(train) [48][ 380/1879] lr: 2.0000e-03 eta: 10:12:18 time: 0.4281 data_time: 0.1184 memory: 6717 grad_norm: 2.9648 loss: 1.3796 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3796 2023/04/14 05:53:23 - mmengine - INFO - Epoch(train) [48][ 400/1879] lr: 2.0000e-03 eta: 10:12:09 time: 0.3164 data_time: 0.0357 memory: 6717 grad_norm: 2.8765 loss: 1.3662 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.3662 2023/04/14 05:53:31 - mmengine - INFO - Epoch(train) [48][ 420/1879] lr: 2.0000e-03 eta: 10:12:03 time: 0.4069 data_time: 0.0326 memory: 6717 grad_norm: 2.8462 loss: 1.3483 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.3483 2023/04/14 05:53:37 - mmengine - INFO - Epoch(train) [48][ 440/1879] lr: 2.0000e-03 eta: 10:11:54 time: 0.3083 data_time: 0.0686 memory: 6717 grad_norm: 2.9352 loss: 1.2049 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2049 2023/04/14 05:53:46 - mmengine - INFO - Epoch(train) [48][ 460/1879] lr: 2.0000e-03 eta: 10:11:48 time: 0.4241 data_time: 0.0881 memory: 6717 grad_norm: 2.9403 loss: 1.1567 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1567 2023/04/14 05:53:53 - mmengine - INFO - Epoch(train) [48][ 480/1879] lr: 2.0000e-03 eta: 10:11:40 time: 0.3637 data_time: 0.0137 memory: 6717 grad_norm: 2.9098 loss: 1.2168 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2168 2023/04/14 05:54:02 - mmengine - INFO - Epoch(train) [48][ 500/1879] lr: 2.0000e-03 eta: 10:11:34 time: 0.4413 data_time: 0.0143 memory: 6717 grad_norm: 2.8389 loss: 1.2331 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2331 2023/04/14 05:54:08 - mmengine - INFO - Epoch(train) [48][ 520/1879] lr: 2.0000e-03 eta: 10:11:25 time: 0.2978 data_time: 0.0125 memory: 6717 grad_norm: 2.9076 loss: 1.2779 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2779 2023/04/14 05:54:16 - mmengine - INFO - Epoch(train) [48][ 540/1879] lr: 2.0000e-03 eta: 10:11:19 time: 0.4079 data_time: 0.0157 memory: 6717 grad_norm: 2.9361 loss: 1.0936 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0936 2023/04/14 05:54:22 - mmengine - INFO - Epoch(train) [48][ 560/1879] lr: 2.0000e-03 eta: 10:11:10 time: 0.3294 data_time: 0.0130 memory: 6717 grad_norm: 2.8934 loss: 1.1994 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1994 2023/04/14 05:54:30 - mmengine - INFO - Epoch(train) [48][ 580/1879] lr: 2.0000e-03 eta: 10:11:03 time: 0.3827 data_time: 0.0159 memory: 6717 grad_norm: 2.9377 loss: 1.2726 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2726 2023/04/14 05:54:37 - mmengine - INFO - Epoch(train) [48][ 600/1879] lr: 2.0000e-03 eta: 10:10:55 time: 0.3455 data_time: 0.0125 memory: 6717 grad_norm: 2.9583 loss: 1.1825 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1825 2023/04/14 05:54:45 - mmengine - INFO - Epoch(train) [48][ 620/1879] lr: 2.0000e-03 eta: 10:10:48 time: 0.3989 data_time: 0.0154 memory: 6717 grad_norm: 2.9414 loss: 1.4626 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4626 2023/04/14 05:54:52 - mmengine - INFO - Epoch(train) [48][ 640/1879] lr: 2.0000e-03 eta: 10:10:40 time: 0.3445 data_time: 0.0123 memory: 6717 grad_norm: 2.8561 loss: 1.2324 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2324 2023/04/14 05:55:00 - mmengine - INFO - Epoch(train) [48][ 660/1879] lr: 2.0000e-03 eta: 10:10:34 time: 0.4224 data_time: 0.0164 memory: 6717 grad_norm: 2.8540 loss: 1.0819 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0819 2023/04/14 05:55:06 - mmengine - INFO - Epoch(train) [48][ 680/1879] lr: 2.0000e-03 eta: 10:10:25 time: 0.2875 data_time: 0.0185 memory: 6717 grad_norm: 2.9542 loss: 1.3878 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3878 2023/04/14 05:55:09 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 05:55:15 - mmengine - INFO - Epoch(train) [48][ 700/1879] lr: 2.0000e-03 eta: 10:10:19 time: 0.4574 data_time: 0.0162 memory: 6717 grad_norm: 2.9432 loss: 1.4275 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4275 2023/04/14 05:55:21 - mmengine - INFO - Epoch(train) [48][ 720/1879] lr: 2.0000e-03 eta: 10:10:10 time: 0.2888 data_time: 0.0125 memory: 6717 grad_norm: 2.8974 loss: 1.3496 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3496 2023/04/14 05:55:29 - mmengine - INFO - Epoch(train) [48][ 740/1879] lr: 2.0000e-03 eta: 10:10:04 time: 0.3994 data_time: 0.0158 memory: 6717 grad_norm: 2.8672 loss: 1.2303 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2303 2023/04/14 05:55:36 - mmengine - INFO - Epoch(train) [48][ 760/1879] lr: 2.0000e-03 eta: 10:09:55 time: 0.3407 data_time: 0.0118 memory: 6717 grad_norm: 2.8583 loss: 1.3224 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3224 2023/04/14 05:55:44 - mmengine - INFO - Epoch(train) [48][ 780/1879] lr: 2.0000e-03 eta: 10:09:49 time: 0.4107 data_time: 0.0165 memory: 6717 grad_norm: 2.8384 loss: 1.1651 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1651 2023/04/14 05:55:51 - mmengine - INFO - Epoch(train) [48][ 800/1879] lr: 2.0000e-03 eta: 10:09:41 time: 0.3386 data_time: 0.0122 memory: 6717 grad_norm: 2.8881 loss: 1.0490 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0490 2023/04/14 05:55:59 - mmengine - INFO - Epoch(train) [48][ 820/1879] lr: 2.0000e-03 eta: 10:09:35 time: 0.4288 data_time: 0.0159 memory: 6717 grad_norm: 2.9692 loss: 1.2025 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2025 2023/04/14 05:56:06 - mmengine - INFO - Epoch(train) [48][ 840/1879] lr: 2.0000e-03 eta: 10:09:26 time: 0.3328 data_time: 0.0145 memory: 6717 grad_norm: 2.9282 loss: 1.2961 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2961 2023/04/14 05:56:14 - mmengine - INFO - Epoch(train) [48][ 860/1879] lr: 2.0000e-03 eta: 10:09:20 time: 0.4204 data_time: 0.0152 memory: 6717 grad_norm: 2.9043 loss: 1.3014 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3014 2023/04/14 05:56:21 - mmengine - INFO - Epoch(train) [48][ 880/1879] lr: 2.0000e-03 eta: 10:09:12 time: 0.3330 data_time: 0.0142 memory: 6717 grad_norm: 2.9524 loss: 1.4382 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4382 2023/04/14 05:56:29 - mmengine - INFO - Epoch(train) [48][ 900/1879] lr: 2.0000e-03 eta: 10:09:06 time: 0.4210 data_time: 0.0136 memory: 6717 grad_norm: 2.8780 loss: 1.2916 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.2916 2023/04/14 05:56:35 - mmengine - INFO - Epoch(train) [48][ 920/1879] lr: 2.0000e-03 eta: 10:08:57 time: 0.2923 data_time: 0.0139 memory: 6717 grad_norm: 2.9673 loss: 1.3249 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3249 2023/04/14 05:56:43 - mmengine - INFO - Epoch(train) [48][ 940/1879] lr: 2.0000e-03 eta: 10:08:50 time: 0.3963 data_time: 0.0153 memory: 6717 grad_norm: 2.9819 loss: 1.3371 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3371 2023/04/14 05:56:50 - mmengine - INFO - Epoch(train) [48][ 960/1879] lr: 2.0000e-03 eta: 10:08:42 time: 0.3385 data_time: 0.0143 memory: 6717 grad_norm: 2.9407 loss: 1.2279 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2279 2023/04/14 05:56:58 - mmengine - INFO - Epoch(train) [48][ 980/1879] lr: 2.0000e-03 eta: 10:08:34 time: 0.3818 data_time: 0.0150 memory: 6717 grad_norm: 2.8647 loss: 1.4000 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4000 2023/04/14 05:57:04 - mmengine - INFO - Epoch(train) [48][1000/1879] lr: 2.0000e-03 eta: 10:08:26 time: 0.3246 data_time: 0.0134 memory: 6717 grad_norm: 2.9159 loss: 1.3022 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3022 2023/04/14 05:57:12 - mmengine - INFO - Epoch(train) [48][1020/1879] lr: 2.0000e-03 eta: 10:08:19 time: 0.3938 data_time: 0.0151 memory: 6717 grad_norm: 2.9739 loss: 1.3210 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3210 2023/04/14 05:57:19 - mmengine - INFO - Epoch(train) [48][1040/1879] lr: 2.0000e-03 eta: 10:08:11 time: 0.3301 data_time: 0.0133 memory: 6717 grad_norm: 2.9006 loss: 1.2585 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2585 2023/04/14 05:57:27 - mmengine - INFO - Epoch(train) [48][1060/1879] lr: 2.0000e-03 eta: 10:08:05 time: 0.4305 data_time: 0.0201 memory: 6717 grad_norm: 2.8935 loss: 1.2808 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2808 2023/04/14 05:57:34 - mmengine - INFO - Epoch(train) [48][1080/1879] lr: 2.0000e-03 eta: 10:07:57 time: 0.3498 data_time: 0.0135 memory: 6717 grad_norm: 2.9644 loss: 1.3891 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3891 2023/04/14 05:57:43 - mmengine - INFO - Epoch(train) [48][1100/1879] lr: 2.0000e-03 eta: 10:07:51 time: 0.4164 data_time: 0.0147 memory: 6717 grad_norm: 2.8943 loss: 1.1727 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1727 2023/04/14 05:57:50 - mmengine - INFO - Epoch(train) [48][1120/1879] lr: 2.0000e-03 eta: 10:07:43 time: 0.3661 data_time: 0.0137 memory: 6717 grad_norm: 2.9052 loss: 1.2739 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2739 2023/04/14 05:57:57 - mmengine - INFO - Epoch(train) [48][1140/1879] lr: 2.0000e-03 eta: 10:07:36 time: 0.3756 data_time: 0.0149 memory: 6717 grad_norm: 2.9043 loss: 1.2821 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.2821 2023/04/14 05:58:04 - mmengine - INFO - Epoch(train) [48][1160/1879] lr: 2.0000e-03 eta: 10:07:27 time: 0.3173 data_time: 0.0144 memory: 6717 grad_norm: 2.9116 loss: 1.3406 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3406 2023/04/14 05:58:13 - mmengine - INFO - Epoch(train) [48][1180/1879] lr: 2.0000e-03 eta: 10:07:21 time: 0.4414 data_time: 0.0144 memory: 6717 grad_norm: 2.8132 loss: 1.1307 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1307 2023/04/14 05:58:18 - mmengine - INFO - Epoch(train) [48][1200/1879] lr: 2.0000e-03 eta: 10:07:12 time: 0.2940 data_time: 0.0150 memory: 6717 grad_norm: 2.8723 loss: 1.2806 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2806 2023/04/14 05:58:26 - mmengine - INFO - Epoch(train) [48][1220/1879] lr: 2.0000e-03 eta: 10:07:05 time: 0.3872 data_time: 0.0148 memory: 6717 grad_norm: 2.8803 loss: 1.1752 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1752 2023/04/14 05:58:33 - mmengine - INFO - Epoch(train) [48][1240/1879] lr: 2.0000e-03 eta: 10:06:57 time: 0.3262 data_time: 0.0137 memory: 6717 grad_norm: 2.9776 loss: 1.4774 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.4774 2023/04/14 05:58:41 - mmengine - INFO - Epoch(train) [48][1260/1879] lr: 2.0000e-03 eta: 10:06:51 time: 0.4290 data_time: 0.0145 memory: 6717 grad_norm: 2.8780 loss: 1.3555 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3555 2023/04/14 05:58:48 - mmengine - INFO - Epoch(train) [48][1280/1879] lr: 2.0000e-03 eta: 10:06:43 time: 0.3442 data_time: 0.0143 memory: 6717 grad_norm: 2.8325 loss: 1.1614 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1614 2023/04/14 05:58:55 - mmengine - INFO - Epoch(train) [48][1300/1879] lr: 2.0000e-03 eta: 10:06:35 time: 0.3637 data_time: 0.0137 memory: 6717 grad_norm: 2.8626 loss: 1.2058 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2058 2023/04/14 05:59:02 - mmengine - INFO - Epoch(train) [48][1320/1879] lr: 2.0000e-03 eta: 10:06:27 time: 0.3180 data_time: 0.0152 memory: 6717 grad_norm: 2.9078 loss: 1.2204 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2204 2023/04/14 05:59:10 - mmengine - INFO - Epoch(train) [48][1340/1879] lr: 2.0000e-03 eta: 10:06:20 time: 0.3945 data_time: 0.0222 memory: 6717 grad_norm: 2.9738 loss: 1.3573 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3573 2023/04/14 05:59:17 - mmengine - INFO - Epoch(train) [48][1360/1879] lr: 2.0000e-03 eta: 10:06:12 time: 0.3390 data_time: 0.0631 memory: 6717 grad_norm: 2.9613 loss: 1.2606 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2606 2023/04/14 05:59:25 - mmengine - INFO - Epoch(train) [48][1380/1879] lr: 2.0000e-03 eta: 10:06:06 time: 0.4401 data_time: 0.0130 memory: 6717 grad_norm: 2.8682 loss: 1.1081 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1081 2023/04/14 05:59:32 - mmengine - INFO - Epoch(train) [48][1400/1879] lr: 2.0000e-03 eta: 10:05:57 time: 0.3207 data_time: 0.0143 memory: 6717 grad_norm: 2.9261 loss: 1.2473 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2473 2023/04/14 05:59:40 - mmengine - INFO - Epoch(train) [48][1420/1879] lr: 2.0000e-03 eta: 10:05:50 time: 0.3927 data_time: 0.0137 memory: 6717 grad_norm: 2.8454 loss: 1.2816 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2816 2023/04/14 05:59:46 - mmengine - INFO - Epoch(train) [48][1440/1879] lr: 2.0000e-03 eta: 10:05:42 time: 0.3366 data_time: 0.0147 memory: 6717 grad_norm: 2.9167 loss: 1.2727 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2727 2023/04/14 05:59:54 - mmengine - INFO - Epoch(train) [48][1460/1879] lr: 2.0000e-03 eta: 10:05:35 time: 0.3769 data_time: 0.0160 memory: 6717 grad_norm: 2.9258 loss: 1.3100 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3100 2023/04/14 06:00:01 - mmengine - INFO - Epoch(train) [48][1480/1879] lr: 2.0000e-03 eta: 10:05:28 time: 0.3805 data_time: 0.0126 memory: 6717 grad_norm: 2.8772 loss: 1.1183 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1183 2023/04/14 06:00:09 - mmengine - INFO - Epoch(train) [48][1500/1879] lr: 2.0000e-03 eta: 10:05:20 time: 0.3610 data_time: 0.0161 memory: 6717 grad_norm: 2.9546 loss: 1.3394 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3394 2023/04/14 06:00:16 - mmengine - INFO - Epoch(train) [48][1520/1879] lr: 2.0000e-03 eta: 10:05:13 time: 0.3845 data_time: 0.0132 memory: 6717 grad_norm: 2.8981 loss: 1.2593 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2593 2023/04/14 06:00:23 - mmengine - INFO - Epoch(train) [48][1540/1879] lr: 2.0000e-03 eta: 10:05:05 time: 0.3502 data_time: 0.0151 memory: 6717 grad_norm: 2.8631 loss: 1.0647 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0647 2023/04/14 06:00:31 - mmengine - INFO - Epoch(train) [48][1560/1879] lr: 2.0000e-03 eta: 10:04:58 time: 0.3764 data_time: 0.0136 memory: 6717 grad_norm: 2.9196 loss: 1.2741 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2741 2023/04/14 06:00:38 - mmengine - INFO - Epoch(train) [48][1580/1879] lr: 2.0000e-03 eta: 10:04:50 time: 0.3381 data_time: 0.0144 memory: 6717 grad_norm: 2.9192 loss: 1.3602 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3602 2023/04/14 06:00:46 - mmengine - INFO - Epoch(train) [48][1600/1879] lr: 2.0000e-03 eta: 10:04:43 time: 0.4046 data_time: 0.0132 memory: 6717 grad_norm: 2.8944 loss: 1.3136 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3136 2023/04/14 06:00:53 - mmengine - INFO - Epoch(train) [48][1620/1879] lr: 2.0000e-03 eta: 10:04:36 time: 0.3572 data_time: 0.0149 memory: 6717 grad_norm: 2.8058 loss: 1.2865 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2865 2023/04/14 06:01:01 - mmengine - INFO - Epoch(train) [48][1640/1879] lr: 2.0000e-03 eta: 10:04:29 time: 0.3894 data_time: 0.0155 memory: 6717 grad_norm: 2.9507 loss: 1.2319 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2319 2023/04/14 06:01:08 - mmengine - INFO - Epoch(train) [48][1660/1879] lr: 2.0000e-03 eta: 10:04:21 time: 0.3662 data_time: 0.0141 memory: 6717 grad_norm: 2.9259 loss: 1.2938 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2938 2023/04/14 06:01:15 - mmengine - INFO - Epoch(train) [48][1680/1879] lr: 2.0000e-03 eta: 10:04:13 time: 0.3283 data_time: 0.0160 memory: 6717 grad_norm: 2.9733 loss: 1.2745 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.2745 2023/04/14 06:01:17 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 06:01:23 - mmengine - INFO - Epoch(train) [48][1700/1879] lr: 2.0000e-03 eta: 10:04:07 time: 0.4351 data_time: 0.0142 memory: 6717 grad_norm: 2.8910 loss: 1.2929 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2929 2023/04/14 06:01:30 - mmengine - INFO - Epoch(train) [48][1720/1879] lr: 2.0000e-03 eta: 10:03:59 time: 0.3422 data_time: 0.0150 memory: 6717 grad_norm: 2.9333 loss: 1.2503 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2503 2023/04/14 06:01:38 - mmengine - INFO - Epoch(train) [48][1740/1879] lr: 2.0000e-03 eta: 10:03:52 time: 0.4053 data_time: 0.0160 memory: 6717 grad_norm: 2.9929 loss: 1.2688 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2688 2023/04/14 06:01:45 - mmengine - INFO - Epoch(train) [48][1760/1879] lr: 2.0000e-03 eta: 10:03:44 time: 0.3215 data_time: 0.0159 memory: 6717 grad_norm: 2.9152 loss: 1.3193 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.3193 2023/04/14 06:01:53 - mmengine - INFO - Epoch(train) [48][1780/1879] lr: 2.0000e-03 eta: 10:03:37 time: 0.4004 data_time: 0.0130 memory: 6717 grad_norm: 2.9263 loss: 1.4471 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4471 2023/04/14 06:02:00 - mmengine - INFO - Epoch(train) [48][1800/1879] lr: 2.0000e-03 eta: 10:03:29 time: 0.3531 data_time: 0.0161 memory: 6717 grad_norm: 2.9846 loss: 1.1644 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1644 2023/04/14 06:02:08 - mmengine - INFO - Epoch(train) [48][1820/1879] lr: 2.0000e-03 eta: 10:03:22 time: 0.4079 data_time: 0.0131 memory: 6717 grad_norm: 2.8869 loss: 1.2894 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2894 2023/04/14 06:02:14 - mmengine - INFO - Epoch(train) [48][1840/1879] lr: 2.0000e-03 eta: 10:03:14 time: 0.3120 data_time: 0.0158 memory: 6717 grad_norm: 2.9503 loss: 1.2335 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2335 2023/04/14 06:02:22 - mmengine - INFO - Epoch(train) [48][1860/1879] lr: 2.0000e-03 eta: 10:03:07 time: 0.3853 data_time: 0.0134 memory: 6717 grad_norm: 2.9485 loss: 1.3598 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3598 2023/04/14 06:02:28 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 06:02:28 - mmengine - INFO - Epoch(train) [48][1879/1879] lr: 2.0000e-03 eta: 10:02:58 time: 0.3649 data_time: 0.0131 memory: 6717 grad_norm: 3.3349 loss: 1.4454 top1_acc: 0.2857 top5_acc: 0.8571 loss_cls: 1.4454 2023/04/14 06:02:28 - mmengine - INFO - Saving checkpoint at 48 epochs 2023/04/14 06:02:37 - mmengine - INFO - Epoch(val) [48][ 20/155] eta: 0:01:00 time: 0.4475 data_time: 0.4141 memory: 1391 2023/04/14 06:02:44 - mmengine - INFO - Epoch(val) [48][ 40/155] eta: 0:00:44 time: 0.3332 data_time: 0.2998 memory: 1391 2023/04/14 06:02:53 - mmengine - INFO - Epoch(val) [48][ 60/155] eta: 0:00:38 time: 0.4292 data_time: 0.3961 memory: 1391 2023/04/14 06:02:59 - mmengine - INFO - Epoch(val) [48][ 80/155] eta: 0:00:28 time: 0.3172 data_time: 0.2837 memory: 1391 2023/04/14 06:03:08 - mmengine - INFO - Epoch(val) [48][100/155] eta: 0:00:21 time: 0.4593 data_time: 0.4260 memory: 1391 2023/04/14 06:03:14 - mmengine - INFO - Epoch(val) [48][120/155] eta: 0:00:13 time: 0.2975 data_time: 0.2643 memory: 1391 2023/04/14 06:03:24 - mmengine - INFO - Epoch(val) [48][140/155] eta: 0:00:05 time: 0.4820 data_time: 0.4485 memory: 1391 2023/04/14 06:03:31 - mmengine - INFO - Epoch(val) [48][155/155] acc/top1: 0.6575 acc/top5: 0.8685 acc/mean1: 0.6574 data_time: 0.4162 time: 0.4488 2023/04/14 06:03:31 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_45.pth is removed 2023/04/14 06:03:31 - mmengine - INFO - The best checkpoint with 0.6575 acc/top1 at 48 epoch is saved to best_acc_top1_epoch_48.pth. 2023/04/14 06:03:40 - mmengine - INFO - Epoch(train) [49][ 20/1879] lr: 2.0000e-03 eta: 10:02:53 time: 0.4508 data_time: 0.3114 memory: 6717 grad_norm: 2.8872 loss: 1.2435 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2435 2023/04/14 06:03:47 - mmengine - INFO - Epoch(train) [49][ 40/1879] lr: 2.0000e-03 eta: 10:02:45 time: 0.3315 data_time: 0.1460 memory: 6717 grad_norm: 2.8689 loss: 1.1246 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1246 2023/04/14 06:03:56 - mmengine - INFO - Epoch(train) [49][ 60/1879] lr: 2.0000e-03 eta: 10:02:39 time: 0.4400 data_time: 0.1323 memory: 6717 grad_norm: 2.8860 loss: 1.0639 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0639 2023/04/14 06:04:03 - mmengine - INFO - Epoch(train) [49][ 80/1879] lr: 2.0000e-03 eta: 10:02:31 time: 0.3352 data_time: 0.1123 memory: 6717 grad_norm: 2.8818 loss: 1.1734 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1734 2023/04/14 06:04:11 - mmengine - INFO - Epoch(train) [49][ 100/1879] lr: 2.0000e-03 eta: 10:02:24 time: 0.4100 data_time: 0.0627 memory: 6717 grad_norm: 2.9312 loss: 1.2591 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2591 2023/04/14 06:04:17 - mmengine - INFO - Epoch(train) [49][ 120/1879] lr: 2.0000e-03 eta: 10:02:16 time: 0.3231 data_time: 0.0819 memory: 6717 grad_norm: 2.9403 loss: 1.4223 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.4223 2023/04/14 06:04:25 - mmengine - INFO - Epoch(train) [49][ 140/1879] lr: 2.0000e-03 eta: 10:02:09 time: 0.3940 data_time: 0.1476 memory: 6717 grad_norm: 2.9013 loss: 1.1686 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1686 2023/04/14 06:04:32 - mmengine - INFO - Epoch(train) [49][ 160/1879] lr: 2.0000e-03 eta: 10:02:01 time: 0.3609 data_time: 0.0855 memory: 6717 grad_norm: 2.9247 loss: 1.3626 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3626 2023/04/14 06:04:41 - mmengine - INFO - Epoch(train) [49][ 180/1879] lr: 2.0000e-03 eta: 10:01:55 time: 0.4445 data_time: 0.0195 memory: 6717 grad_norm: 2.8337 loss: 1.2066 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2066 2023/04/14 06:04:47 - mmengine - INFO - Epoch(train) [49][ 200/1879] lr: 2.0000e-03 eta: 10:01:47 time: 0.3108 data_time: 0.0129 memory: 6717 grad_norm: 2.8779 loss: 1.4249 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4249 2023/04/14 06:04:55 - mmengine - INFO - Epoch(train) [49][ 220/1879] lr: 2.0000e-03 eta: 10:01:40 time: 0.4040 data_time: 0.0136 memory: 6717 grad_norm: 2.9468 loss: 1.3568 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3568 2023/04/14 06:05:02 - mmengine - INFO - Epoch(train) [49][ 240/1879] lr: 2.0000e-03 eta: 10:01:32 time: 0.3303 data_time: 0.0136 memory: 6717 grad_norm: 2.9461 loss: 1.2784 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2784 2023/04/14 06:05:10 - mmengine - INFO - Epoch(train) [49][ 260/1879] lr: 2.0000e-03 eta: 10:01:25 time: 0.4029 data_time: 0.0148 memory: 6717 grad_norm: 2.8989 loss: 1.1837 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1837 2023/04/14 06:05:16 - mmengine - INFO - Epoch(train) [49][ 280/1879] lr: 2.0000e-03 eta: 10:01:16 time: 0.2796 data_time: 0.0129 memory: 6717 grad_norm: 2.9160 loss: 1.2734 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2734 2023/04/14 06:05:24 - mmengine - INFO - Epoch(train) [49][ 300/1879] lr: 2.0000e-03 eta: 10:01:09 time: 0.4139 data_time: 0.0431 memory: 6717 grad_norm: 2.9330 loss: 1.2309 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2309 2023/04/14 06:05:31 - mmengine - INFO - Epoch(train) [49][ 320/1879] lr: 2.0000e-03 eta: 10:01:01 time: 0.3261 data_time: 0.0967 memory: 6717 grad_norm: 2.8476 loss: 1.1910 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1910 2023/04/14 06:05:38 - mmengine - INFO - Epoch(train) [49][ 340/1879] lr: 2.0000e-03 eta: 10:00:54 time: 0.3832 data_time: 0.1550 memory: 6717 grad_norm: 2.9147 loss: 1.1363 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1363 2023/04/14 06:05:45 - mmengine - INFO - Epoch(train) [49][ 360/1879] lr: 2.0000e-03 eta: 10:00:46 time: 0.3426 data_time: 0.1243 memory: 6717 grad_norm: 2.9332 loss: 1.2026 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2026 2023/04/14 06:05:54 - mmengine - INFO - Epoch(train) [49][ 380/1879] lr: 2.0000e-03 eta: 10:00:39 time: 0.4303 data_time: 0.0293 memory: 6717 grad_norm: 2.9022 loss: 1.2167 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2167 2023/04/14 06:06:00 - mmengine - INFO - Epoch(train) [49][ 400/1879] lr: 2.0000e-03 eta: 10:00:31 time: 0.3395 data_time: 0.0200 memory: 6717 grad_norm: 3.0618 loss: 1.3791 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3791 2023/04/14 06:06:08 - mmengine - INFO - Epoch(train) [49][ 420/1879] lr: 2.0000e-03 eta: 10:00:24 time: 0.3812 data_time: 0.0214 memory: 6717 grad_norm: 2.8637 loss: 1.3586 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3586 2023/04/14 06:06:15 - mmengine - INFO - Epoch(train) [49][ 440/1879] lr: 2.0000e-03 eta: 10:00:16 time: 0.3437 data_time: 0.0141 memory: 6717 grad_norm: 2.8893 loss: 1.3227 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3227 2023/04/14 06:06:23 - mmengine - INFO - Epoch(train) [49][ 460/1879] lr: 2.0000e-03 eta: 10:00:09 time: 0.4003 data_time: 0.0141 memory: 6717 grad_norm: 2.9789 loss: 1.2918 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2918 2023/04/14 06:06:29 - mmengine - INFO - Epoch(train) [49][ 480/1879] lr: 2.0000e-03 eta: 10:00:01 time: 0.3168 data_time: 0.0311 memory: 6717 grad_norm: 2.9611 loss: 1.4099 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.4099 2023/04/14 06:06:37 - mmengine - INFO - Epoch(train) [49][ 500/1879] lr: 2.0000e-03 eta: 9:59:54 time: 0.3893 data_time: 0.1471 memory: 6717 grad_norm: 2.9682 loss: 1.2199 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2199 2023/04/14 06:06:43 - mmengine - INFO - Epoch(train) [49][ 520/1879] lr: 2.0000e-03 eta: 9:59:45 time: 0.3082 data_time: 0.0914 memory: 6717 grad_norm: 2.9307 loss: 1.1974 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1974 2023/04/14 06:06:52 - mmengine - INFO - Epoch(train) [49][ 540/1879] lr: 2.0000e-03 eta: 9:59:39 time: 0.4362 data_time: 0.1635 memory: 6717 grad_norm: 2.9402 loss: 1.2657 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2657 2023/04/14 06:06:58 - mmengine - INFO - Epoch(train) [49][ 560/1879] lr: 2.0000e-03 eta: 9:59:31 time: 0.3222 data_time: 0.1092 memory: 6717 grad_norm: 2.9040 loss: 1.1782 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1782 2023/04/14 06:07:07 - mmengine - INFO - Epoch(train) [49][ 580/1879] lr: 2.0000e-03 eta: 9:59:24 time: 0.4041 data_time: 0.1876 memory: 6717 grad_norm: 2.9063 loss: 1.1834 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1834 2023/04/14 06:07:13 - mmengine - INFO - Epoch(train) [49][ 600/1879] lr: 2.0000e-03 eta: 9:59:16 time: 0.3218 data_time: 0.0854 memory: 6717 grad_norm: 2.9395 loss: 1.2048 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2048 2023/04/14 06:07:21 - mmengine - INFO - Epoch(train) [49][ 620/1879] lr: 2.0000e-03 eta: 9:59:09 time: 0.4201 data_time: 0.1342 memory: 6717 grad_norm: 2.9036 loss: 1.2545 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2545 2023/04/14 06:07:28 - mmengine - INFO - Epoch(train) [49][ 640/1879] lr: 2.0000e-03 eta: 9:59:01 time: 0.3301 data_time: 0.0170 memory: 6717 grad_norm: 2.9689 loss: 1.1898 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1898 2023/04/14 06:07:36 - mmengine - INFO - Epoch(train) [49][ 660/1879] lr: 2.0000e-03 eta: 9:58:55 time: 0.4164 data_time: 0.0169 memory: 6717 grad_norm: 2.9042 loss: 1.4169 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4169 2023/04/14 06:07:43 - mmengine - INFO - Epoch(train) [49][ 680/1879] lr: 2.0000e-03 eta: 9:58:47 time: 0.3425 data_time: 0.0422 memory: 6717 grad_norm: 2.9672 loss: 1.2588 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2588 2023/04/14 06:07:50 - mmengine - INFO - Epoch(train) [49][ 700/1879] lr: 2.0000e-03 eta: 9:58:39 time: 0.3646 data_time: 0.0499 memory: 6717 grad_norm: 2.9834 loss: 1.3151 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3151 2023/04/14 06:07:58 - mmengine - INFO - Epoch(train) [49][ 720/1879] lr: 2.0000e-03 eta: 9:58:31 time: 0.3566 data_time: 0.1206 memory: 6717 grad_norm: 2.8273 loss: 1.2513 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2513 2023/04/14 06:08:06 - mmengine - INFO - Epoch(train) [49][ 740/1879] lr: 2.0000e-03 eta: 9:58:25 time: 0.3990 data_time: 0.0539 memory: 6717 grad_norm: 2.9999 loss: 1.3425 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.3425 2023/04/14 06:08:12 - mmengine - INFO - Epoch(train) [49][ 760/1879] lr: 2.0000e-03 eta: 9:58:16 time: 0.3148 data_time: 0.0739 memory: 6717 grad_norm: 2.9112 loss: 1.3598 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3598 2023/04/14 06:08:19 - mmengine - INFO - Epoch(train) [49][ 780/1879] lr: 2.0000e-03 eta: 9:58:08 time: 0.3545 data_time: 0.0875 memory: 6717 grad_norm: 2.8589 loss: 1.1919 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1919 2023/04/14 06:08:27 - mmengine - INFO - Epoch(train) [49][ 800/1879] lr: 2.0000e-03 eta: 9:58:01 time: 0.3819 data_time: 0.1626 memory: 6717 grad_norm: 2.8527 loss: 1.2797 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.2797 2023/04/14 06:08:30 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 06:08:34 - mmengine - INFO - Epoch(train) [49][ 820/1879] lr: 2.0000e-03 eta: 9:57:54 time: 0.3776 data_time: 0.0882 memory: 6717 grad_norm: 2.9274 loss: 1.2056 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.2056 2023/04/14 06:08:42 - mmengine - INFO - Epoch(train) [49][ 840/1879] lr: 2.0000e-03 eta: 9:57:47 time: 0.3763 data_time: 0.1025 memory: 6717 grad_norm: 2.8873 loss: 1.2498 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.2498 2023/04/14 06:08:49 - mmengine - INFO - Epoch(train) [49][ 860/1879] lr: 2.0000e-03 eta: 9:57:39 time: 0.3480 data_time: 0.1011 memory: 6717 grad_norm: 2.9957 loss: 1.3930 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3930 2023/04/14 06:08:57 - mmengine - INFO - Epoch(train) [49][ 880/1879] lr: 2.0000e-03 eta: 9:57:32 time: 0.4124 data_time: 0.1251 memory: 6717 grad_norm: 2.8956 loss: 1.2354 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2354 2023/04/14 06:09:04 - mmengine - INFO - Epoch(train) [49][ 900/1879] lr: 2.0000e-03 eta: 9:57:24 time: 0.3329 data_time: 0.0656 memory: 6717 grad_norm: 2.8725 loss: 1.2222 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2222 2023/04/14 06:09:12 - mmengine - INFO - Epoch(train) [49][ 920/1879] lr: 2.0000e-03 eta: 9:57:17 time: 0.4047 data_time: 0.0578 memory: 6717 grad_norm: 2.8931 loss: 1.3453 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3453 2023/04/14 06:09:19 - mmengine - INFO - Epoch(train) [49][ 940/1879] lr: 2.0000e-03 eta: 9:57:09 time: 0.3437 data_time: 0.0547 memory: 6717 grad_norm: 2.9573 loss: 1.2535 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2535 2023/04/14 06:09:25 - mmengine - INFO - Epoch(train) [49][ 960/1879] lr: 2.0000e-03 eta: 9:57:01 time: 0.3329 data_time: 0.0186 memory: 6717 grad_norm: 2.9328 loss: 1.3018 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3018 2023/04/14 06:09:33 - mmengine - INFO - Epoch(train) [49][ 980/1879] lr: 2.0000e-03 eta: 9:56:54 time: 0.4001 data_time: 0.0609 memory: 6717 grad_norm: 2.9363 loss: 1.2731 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2731 2023/04/14 06:09:41 - mmengine - INFO - Epoch(train) [49][1000/1879] lr: 2.0000e-03 eta: 9:56:47 time: 0.3697 data_time: 0.0403 memory: 6717 grad_norm: 2.9405 loss: 1.3391 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3391 2023/04/14 06:09:49 - mmengine - INFO - Epoch(train) [49][1020/1879] lr: 2.0000e-03 eta: 9:56:40 time: 0.4116 data_time: 0.0130 memory: 6717 grad_norm: 2.9060 loss: 1.3366 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3366 2023/04/14 06:09:56 - mmengine - INFO - Epoch(train) [49][1040/1879] lr: 2.0000e-03 eta: 9:56:32 time: 0.3316 data_time: 0.0144 memory: 6717 grad_norm: 2.9037 loss: 1.2272 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.2272 2023/04/14 06:10:03 - mmengine - INFO - Epoch(train) [49][1060/1879] lr: 2.0000e-03 eta: 9:56:25 time: 0.3672 data_time: 0.0145 memory: 6717 grad_norm: 2.8890 loss: 1.2360 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2360 2023/04/14 06:10:11 - mmengine - INFO - Epoch(train) [49][1080/1879] lr: 2.0000e-03 eta: 9:56:18 time: 0.3992 data_time: 0.0148 memory: 6717 grad_norm: 2.8777 loss: 1.2704 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2704 2023/04/14 06:10:18 - mmengine - INFO - Epoch(train) [49][1100/1879] lr: 2.0000e-03 eta: 9:56:10 time: 0.3412 data_time: 0.0140 memory: 6717 grad_norm: 2.9538 loss: 1.1037 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1037 2023/04/14 06:10:26 - mmengine - INFO - Epoch(train) [49][1120/1879] lr: 2.0000e-03 eta: 9:56:04 time: 0.4367 data_time: 0.0154 memory: 6717 grad_norm: 2.9606 loss: 1.2224 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2224 2023/04/14 06:10:33 - mmengine - INFO - Epoch(train) [49][1140/1879] lr: 2.0000e-03 eta: 9:55:56 time: 0.3251 data_time: 0.0127 memory: 6717 grad_norm: 2.8642 loss: 1.1469 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1469 2023/04/14 06:10:41 - mmengine - INFO - Epoch(train) [49][1160/1879] lr: 2.0000e-03 eta: 9:55:49 time: 0.4070 data_time: 0.0149 memory: 6717 grad_norm: 2.9109 loss: 1.2979 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2979 2023/04/14 06:10:47 - mmengine - INFO - Epoch(train) [49][1180/1879] lr: 2.0000e-03 eta: 9:55:40 time: 0.3084 data_time: 0.0149 memory: 6717 grad_norm: 2.9214 loss: 1.2687 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2687 2023/04/14 06:10:55 - mmengine - INFO - Epoch(train) [49][1200/1879] lr: 2.0000e-03 eta: 9:55:34 time: 0.4108 data_time: 0.0142 memory: 6717 grad_norm: 2.9403 loss: 1.2317 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2317 2023/04/14 06:11:02 - mmengine - INFO - Epoch(train) [49][1220/1879] lr: 2.0000e-03 eta: 9:55:26 time: 0.3474 data_time: 0.0148 memory: 6717 grad_norm: 2.9314 loss: 1.2589 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2589 2023/04/14 06:11:11 - mmengine - INFO - Epoch(train) [49][1240/1879] lr: 2.0000e-03 eta: 9:55:20 time: 0.4290 data_time: 0.0157 memory: 6717 grad_norm: 2.9555 loss: 1.3396 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3396 2023/04/14 06:11:17 - mmengine - INFO - Epoch(train) [49][1260/1879] lr: 2.0000e-03 eta: 9:55:11 time: 0.3074 data_time: 0.0135 memory: 6717 grad_norm: 3.0028 loss: 1.2804 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2804 2023/04/14 06:11:25 - mmengine - INFO - Epoch(train) [49][1280/1879] lr: 2.0000e-03 eta: 9:55:04 time: 0.3704 data_time: 0.0169 memory: 6717 grad_norm: 2.8514 loss: 1.2225 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2225 2023/04/14 06:11:32 - mmengine - INFO - Epoch(train) [49][1300/1879] lr: 2.0000e-03 eta: 9:54:56 time: 0.3808 data_time: 0.0128 memory: 6717 grad_norm: 2.9303 loss: 1.3036 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3036 2023/04/14 06:11:39 - mmengine - INFO - Epoch(train) [49][1320/1879] lr: 2.0000e-03 eta: 9:54:48 time: 0.3493 data_time: 0.0138 memory: 6717 grad_norm: 2.8166 loss: 1.1397 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1397 2023/04/14 06:11:46 - mmengine - INFO - Epoch(train) [49][1340/1879] lr: 2.0000e-03 eta: 9:54:41 time: 0.3575 data_time: 0.0149 memory: 6717 grad_norm: 2.9435 loss: 1.1339 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1339 2023/04/14 06:11:53 - mmengine - INFO - Epoch(train) [49][1360/1879] lr: 2.0000e-03 eta: 9:54:33 time: 0.3589 data_time: 0.0144 memory: 6717 grad_norm: 2.8605 loss: 1.3648 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3648 2023/04/14 06:12:01 - mmengine - INFO - Epoch(train) [49][1380/1879] lr: 2.0000e-03 eta: 9:54:26 time: 0.3622 data_time: 0.0147 memory: 6717 grad_norm: 2.9347 loss: 1.1658 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1658 2023/04/14 06:12:08 - mmengine - INFO - Epoch(train) [49][1400/1879] lr: 2.0000e-03 eta: 9:54:18 time: 0.3722 data_time: 0.0150 memory: 6717 grad_norm: 2.8891 loss: 1.4268 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4268 2023/04/14 06:12:15 - mmengine - INFO - Epoch(train) [49][1420/1879] lr: 2.0000e-03 eta: 9:54:10 time: 0.3353 data_time: 0.0144 memory: 6717 grad_norm: 2.9056 loss: 1.3146 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3146 2023/04/14 06:12:23 - mmengine - INFO - Epoch(train) [49][1440/1879] lr: 2.0000e-03 eta: 9:54:03 time: 0.3854 data_time: 0.0149 memory: 6717 grad_norm: 2.9284 loss: 1.2503 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2503 2023/04/14 06:12:30 - mmengine - INFO - Epoch(train) [49][1460/1879] lr: 2.0000e-03 eta: 9:53:55 time: 0.3482 data_time: 0.0144 memory: 6717 grad_norm: 2.8482 loss: 1.1516 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1516 2023/04/14 06:12:37 - mmengine - INFO - Epoch(train) [49][1480/1879] lr: 2.0000e-03 eta: 9:53:48 time: 0.3788 data_time: 0.0148 memory: 6717 grad_norm: 2.9142 loss: 1.1654 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.1654 2023/04/14 06:12:45 - mmengine - INFO - Epoch(train) [49][1500/1879] lr: 2.0000e-03 eta: 9:53:40 time: 0.3700 data_time: 0.0135 memory: 6717 grad_norm: 2.9184 loss: 1.2575 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2575 2023/04/14 06:12:52 - mmengine - INFO - Epoch(train) [49][1520/1879] lr: 2.0000e-03 eta: 9:53:33 time: 0.3799 data_time: 0.0284 memory: 6717 grad_norm: 2.9694 loss: 1.2249 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2249 2023/04/14 06:13:00 - mmengine - INFO - Epoch(train) [49][1540/1879] lr: 2.0000e-03 eta: 9:53:26 time: 0.3834 data_time: 0.0141 memory: 6717 grad_norm: 2.8627 loss: 1.1621 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1621 2023/04/14 06:13:07 - mmengine - INFO - Epoch(train) [49][1560/1879] lr: 2.0000e-03 eta: 9:53:18 time: 0.3364 data_time: 0.0503 memory: 6717 grad_norm: 2.9646 loss: 1.2169 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2169 2023/04/14 06:13:15 - mmengine - INFO - Epoch(train) [49][1580/1879] lr: 2.0000e-03 eta: 9:53:11 time: 0.4089 data_time: 0.0395 memory: 6717 grad_norm: 2.8938 loss: 1.2647 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2647 2023/04/14 06:13:21 - mmengine - INFO - Epoch(train) [49][1600/1879] lr: 2.0000e-03 eta: 9:53:03 time: 0.3031 data_time: 0.0487 memory: 6717 grad_norm: 2.9681 loss: 1.2708 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2708 2023/04/14 06:13:30 - mmengine - INFO - Epoch(train) [49][1620/1879] lr: 2.0000e-03 eta: 9:52:57 time: 0.4428 data_time: 0.0132 memory: 6717 grad_norm: 2.9196 loss: 1.2028 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2028 2023/04/14 06:13:36 - mmengine - INFO - Epoch(train) [49][1640/1879] lr: 2.0000e-03 eta: 9:52:48 time: 0.3283 data_time: 0.0151 memory: 6717 grad_norm: 2.9417 loss: 1.2447 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2447 2023/04/14 06:13:44 - mmengine - INFO - Epoch(train) [49][1660/1879] lr: 2.0000e-03 eta: 9:52:41 time: 0.3689 data_time: 0.0129 memory: 6717 grad_norm: 2.9745 loss: 1.3036 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3036 2023/04/14 06:13:50 - mmengine - INFO - Epoch(train) [49][1680/1879] lr: 2.0000e-03 eta: 9:52:33 time: 0.3432 data_time: 0.0153 memory: 6717 grad_norm: 2.8840 loss: 1.2710 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2710 2023/04/14 06:13:59 - mmengine - INFO - Epoch(train) [49][1700/1879] lr: 2.0000e-03 eta: 9:52:27 time: 0.4196 data_time: 0.0132 memory: 6717 grad_norm: 2.9367 loss: 1.2893 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2893 2023/04/14 06:14:05 - mmengine - INFO - Epoch(train) [49][1720/1879] lr: 2.0000e-03 eta: 9:52:18 time: 0.3117 data_time: 0.0276 memory: 6717 grad_norm: 2.8853 loss: 1.3240 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3240 2023/04/14 06:14:14 - mmengine - INFO - Epoch(train) [49][1740/1879] lr: 2.0000e-03 eta: 9:52:12 time: 0.4259 data_time: 0.0187 memory: 6717 grad_norm: 2.9220 loss: 1.2425 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.2425 2023/04/14 06:14:21 - mmengine - INFO - Epoch(train) [49][1760/1879] lr: 2.0000e-03 eta: 9:52:04 time: 0.3490 data_time: 0.0155 memory: 6717 grad_norm: 2.9202 loss: 1.5202 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.5202 2023/04/14 06:14:29 - mmengine - INFO - Epoch(train) [49][1780/1879] lr: 2.0000e-03 eta: 9:51:58 time: 0.4250 data_time: 0.0123 memory: 6717 grad_norm: 3.0107 loss: 1.4169 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4169 2023/04/14 06:14:35 - mmengine - INFO - Epoch(train) [49][1800/1879] lr: 2.0000e-03 eta: 9:51:49 time: 0.3177 data_time: 0.0148 memory: 6717 grad_norm: 2.8968 loss: 1.1366 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1366 2023/04/14 06:14:38 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 06:14:44 - mmengine - INFO - Epoch(train) [49][1820/1879] lr: 2.0000e-03 eta: 9:51:43 time: 0.4288 data_time: 0.0151 memory: 6717 grad_norm: 2.9885 loss: 1.2467 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2467 2023/04/14 06:14:51 - mmengine - INFO - Epoch(train) [49][1840/1879] lr: 2.0000e-03 eta: 9:51:35 time: 0.3296 data_time: 0.0149 memory: 6717 grad_norm: 2.9451 loss: 1.2754 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2754 2023/04/14 06:14:59 - mmengine - INFO - Epoch(train) [49][1860/1879] lr: 2.0000e-03 eta: 9:51:28 time: 0.4100 data_time: 0.0143 memory: 6717 grad_norm: 2.9327 loss: 1.3252 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3252 2023/04/14 06:15:05 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 06:15:05 - mmengine - INFO - Epoch(train) [49][1879/1879] lr: 2.0000e-03 eta: 9:51:20 time: 0.3130 data_time: 0.0128 memory: 6717 grad_norm: 3.0368 loss: 1.3073 top1_acc: 0.4286 top5_acc: 0.7143 loss_cls: 1.3073 2023/04/14 06:15:15 - mmengine - INFO - Epoch(val) [49][ 20/155] eta: 0:01:05 time: 0.4829 data_time: 0.4497 memory: 1391 2023/04/14 06:15:21 - mmengine - INFO - Epoch(val) [49][ 40/155] eta: 0:00:45 time: 0.3071 data_time: 0.2739 memory: 1391 2023/04/14 06:15:30 - mmengine - INFO - Epoch(val) [49][ 60/155] eta: 0:00:39 time: 0.4443 data_time: 0.4112 memory: 1391 2023/04/14 06:15:36 - mmengine - INFO - Epoch(val) [49][ 80/155] eta: 0:00:28 time: 0.3015 data_time: 0.2686 memory: 1391 2023/04/14 06:15:45 - mmengine - INFO - Epoch(val) [49][100/155] eta: 0:00:21 time: 0.4534 data_time: 0.4206 memory: 1391 2023/04/14 06:15:51 - mmengine - INFO - Epoch(val) [49][120/155] eta: 0:00:13 time: 0.3253 data_time: 0.2921 memory: 1391 2023/04/14 06:16:01 - mmengine - INFO - Epoch(val) [49][140/155] eta: 0:00:05 time: 0.4817 data_time: 0.4490 memory: 1391 2023/04/14 06:16:08 - mmengine - INFO - Epoch(val) [49][155/155] acc/top1: 0.6576 acc/top5: 0.8705 acc/mean1: 0.6575 data_time: 0.4162 time: 0.4485 2023/04/14 06:16:08 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_48.pth is removed 2023/04/14 06:16:09 - mmengine - INFO - The best checkpoint with 0.6576 acc/top1 at 49 epoch is saved to best_acc_top1_epoch_49.pth. 2023/04/14 06:16:18 - mmengine - INFO - Epoch(train) [50][ 20/1879] lr: 2.0000e-03 eta: 9:51:15 time: 0.4596 data_time: 0.2314 memory: 6717 grad_norm: 2.9422 loss: 1.3558 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3558 2023/04/14 06:16:24 - mmengine - INFO - Epoch(train) [50][ 40/1879] lr: 2.0000e-03 eta: 9:51:06 time: 0.3238 data_time: 0.0892 memory: 6717 grad_norm: 2.8162 loss: 1.1694 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1694 2023/04/14 06:16:33 - mmengine - INFO - Epoch(train) [50][ 60/1879] lr: 2.0000e-03 eta: 9:51:00 time: 0.4273 data_time: 0.1226 memory: 6717 grad_norm: 2.8716 loss: 1.1252 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1252 2023/04/14 06:16:39 - mmengine - INFO - Epoch(train) [50][ 80/1879] lr: 2.0000e-03 eta: 9:50:52 time: 0.3328 data_time: 0.0221 memory: 6717 grad_norm: 2.9986 loss: 1.1987 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1987 2023/04/14 06:16:47 - mmengine - INFO - Epoch(train) [50][ 100/1879] lr: 2.0000e-03 eta: 9:50:45 time: 0.4021 data_time: 0.0184 memory: 6717 grad_norm: 2.8495 loss: 1.1354 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1354 2023/04/14 06:16:54 - mmengine - INFO - Epoch(train) [50][ 120/1879] lr: 2.0000e-03 eta: 9:50:37 time: 0.3150 data_time: 0.0133 memory: 6717 grad_norm: 2.8105 loss: 1.2670 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2670 2023/04/14 06:17:02 - mmengine - INFO - Epoch(train) [50][ 140/1879] lr: 2.0000e-03 eta: 9:50:30 time: 0.4184 data_time: 0.0216 memory: 6717 grad_norm: 2.9677 loss: 1.2942 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2942 2023/04/14 06:17:09 - mmengine - INFO - Epoch(train) [50][ 160/1879] lr: 2.0000e-03 eta: 9:50:22 time: 0.3298 data_time: 0.0672 memory: 6717 grad_norm: 2.9307 loss: 1.4166 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4166 2023/04/14 06:17:17 - mmengine - INFO - Epoch(train) [50][ 180/1879] lr: 2.0000e-03 eta: 9:50:16 time: 0.4232 data_time: 0.1036 memory: 6717 grad_norm: 2.9116 loss: 1.3150 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3150 2023/04/14 06:17:24 - mmengine - INFO - Epoch(train) [50][ 200/1879] lr: 2.0000e-03 eta: 9:50:07 time: 0.3288 data_time: 0.1096 memory: 6717 grad_norm: 2.9376 loss: 1.1654 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1654 2023/04/14 06:17:32 - mmengine - INFO - Epoch(train) [50][ 220/1879] lr: 2.0000e-03 eta: 9:50:01 time: 0.4098 data_time: 0.1550 memory: 6717 grad_norm: 2.9507 loss: 1.1991 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1991 2023/04/14 06:17:39 - mmengine - INFO - Epoch(train) [50][ 240/1879] lr: 2.0000e-03 eta: 9:49:53 time: 0.3320 data_time: 0.0768 memory: 6717 grad_norm: 2.9781 loss: 1.2904 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2904 2023/04/14 06:17:47 - mmengine - INFO - Epoch(train) [50][ 260/1879] lr: 2.0000e-03 eta: 9:49:47 time: 0.4430 data_time: 0.0799 memory: 6717 grad_norm: 2.9440 loss: 1.3740 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3740 2023/04/14 06:17:54 - mmengine - INFO - Epoch(train) [50][ 280/1879] lr: 2.0000e-03 eta: 9:49:38 time: 0.3293 data_time: 0.0633 memory: 6717 grad_norm: 2.9116 loss: 1.1607 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1607 2023/04/14 06:18:02 - mmengine - INFO - Epoch(train) [50][ 300/1879] lr: 2.0000e-03 eta: 9:49:32 time: 0.4048 data_time: 0.0183 memory: 6717 grad_norm: 2.9225 loss: 1.4889 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4889 2023/04/14 06:18:09 - mmengine - INFO - Epoch(train) [50][ 320/1879] lr: 2.0000e-03 eta: 9:49:24 time: 0.3546 data_time: 0.0134 memory: 6717 grad_norm: 2.9329 loss: 1.2582 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2582 2023/04/14 06:18:17 - mmengine - INFO - Epoch(train) [50][ 340/1879] lr: 2.0000e-03 eta: 9:49:17 time: 0.4065 data_time: 0.0168 memory: 6717 grad_norm: 2.9636 loss: 1.2445 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2445 2023/04/14 06:18:24 - mmengine - INFO - Epoch(train) [50][ 360/1879] lr: 2.0000e-03 eta: 9:49:09 time: 0.3151 data_time: 0.0141 memory: 6717 grad_norm: 2.8780 loss: 1.2058 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.2058 2023/04/14 06:18:33 - mmengine - INFO - Epoch(train) [50][ 380/1879] lr: 2.0000e-03 eta: 9:49:03 time: 0.4563 data_time: 0.0156 memory: 6717 grad_norm: 2.9384 loss: 1.3964 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3964 2023/04/14 06:18:39 - mmengine - INFO - Epoch(train) [50][ 400/1879] lr: 2.0000e-03 eta: 9:48:54 time: 0.2874 data_time: 0.0128 memory: 6717 grad_norm: 2.9359 loss: 1.2372 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2372 2023/04/14 06:18:47 - mmengine - INFO - Epoch(train) [50][ 420/1879] lr: 2.0000e-03 eta: 9:48:47 time: 0.4064 data_time: 0.0138 memory: 6717 grad_norm: 2.9327 loss: 1.3865 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3865 2023/04/14 06:18:53 - mmengine - INFO - Epoch(train) [50][ 440/1879] lr: 2.0000e-03 eta: 9:48:38 time: 0.2968 data_time: 0.0141 memory: 6717 grad_norm: 2.9588 loss: 1.0313 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0313 2023/04/14 06:19:01 - mmengine - INFO - Epoch(train) [50][ 460/1879] lr: 2.0000e-03 eta: 9:48:32 time: 0.4224 data_time: 0.0145 memory: 6717 grad_norm: 3.0112 loss: 1.2187 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2187 2023/04/14 06:19:07 - mmengine - INFO - Epoch(train) [50][ 480/1879] lr: 2.0000e-03 eta: 9:48:24 time: 0.3168 data_time: 0.0165 memory: 6717 grad_norm: 2.9351 loss: 1.1252 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1252 2023/04/14 06:19:16 - mmengine - INFO - Epoch(train) [50][ 500/1879] lr: 2.0000e-03 eta: 9:48:18 time: 0.4412 data_time: 0.0118 memory: 6717 grad_norm: 2.9056 loss: 1.3744 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3744 2023/04/14 06:19:23 - mmengine - INFO - Epoch(train) [50][ 520/1879] lr: 2.0000e-03 eta: 9:48:10 time: 0.3530 data_time: 0.0143 memory: 6717 grad_norm: 2.9914 loss: 1.1891 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1891 2023/04/14 06:19:31 - mmengine - INFO - Epoch(train) [50][ 540/1879] lr: 2.0000e-03 eta: 9:48:03 time: 0.4003 data_time: 0.0144 memory: 6717 grad_norm: 2.9535 loss: 1.2180 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2180 2023/04/14 06:19:38 - mmengine - INFO - Epoch(train) [50][ 560/1879] lr: 2.0000e-03 eta: 9:47:55 time: 0.3148 data_time: 0.0151 memory: 6717 grad_norm: 2.9626 loss: 1.1464 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1464 2023/04/14 06:19:46 - mmengine - INFO - Epoch(train) [50][ 580/1879] lr: 2.0000e-03 eta: 9:47:48 time: 0.4196 data_time: 0.0157 memory: 6717 grad_norm: 2.9905 loss: 1.2207 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2207 2023/04/14 06:19:52 - mmengine - INFO - Epoch(train) [50][ 600/1879] lr: 2.0000e-03 eta: 9:47:40 time: 0.3152 data_time: 0.0136 memory: 6717 grad_norm: 2.9191 loss: 1.2456 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2456 2023/04/14 06:20:01 - mmengine - INFO - Epoch(train) [50][ 620/1879] lr: 2.0000e-03 eta: 9:47:33 time: 0.4140 data_time: 0.0157 memory: 6717 grad_norm: 2.9890 loss: 1.3984 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.3984 2023/04/14 06:20:06 - mmengine - INFO - Epoch(train) [50][ 640/1879] lr: 2.0000e-03 eta: 9:47:24 time: 0.2885 data_time: 0.0471 memory: 6717 grad_norm: 2.9628 loss: 1.2756 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2756 2023/04/14 06:20:16 - mmengine - INFO - Epoch(train) [50][ 660/1879] lr: 2.0000e-03 eta: 9:47:18 time: 0.4582 data_time: 0.0538 memory: 6717 grad_norm: 2.9572 loss: 1.4328 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4328 2023/04/14 06:20:22 - mmengine - INFO - Epoch(train) [50][ 680/1879] lr: 2.0000e-03 eta: 9:47:10 time: 0.3019 data_time: 0.0163 memory: 6717 grad_norm: 2.9788 loss: 1.5041 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.5041 2023/04/14 06:20:30 - mmengine - INFO - Epoch(train) [50][ 700/1879] lr: 2.0000e-03 eta: 9:47:03 time: 0.4296 data_time: 0.0313 memory: 6717 grad_norm: 2.8949 loss: 1.2580 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2580 2023/04/14 06:20:36 - mmengine - INFO - Epoch(train) [50][ 720/1879] lr: 2.0000e-03 eta: 9:46:55 time: 0.2960 data_time: 0.0412 memory: 6717 grad_norm: 2.9215 loss: 1.2315 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2315 2023/04/14 06:20:44 - mmengine - INFO - Epoch(train) [50][ 740/1879] lr: 2.0000e-03 eta: 9:46:48 time: 0.3992 data_time: 0.1378 memory: 6717 grad_norm: 2.9356 loss: 1.0764 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0764 2023/04/14 06:20:51 - mmengine - INFO - Epoch(train) [50][ 760/1879] lr: 2.0000e-03 eta: 9:46:40 time: 0.3334 data_time: 0.0857 memory: 6717 grad_norm: 2.9637 loss: 1.1718 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1718 2023/04/14 06:20:59 - mmengine - INFO - Epoch(train) [50][ 780/1879] lr: 2.0000e-03 eta: 9:46:33 time: 0.4186 data_time: 0.0844 memory: 6717 grad_norm: 2.9129 loss: 1.1923 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1923 2023/04/14 06:21:05 - mmengine - INFO - Epoch(train) [50][ 800/1879] lr: 2.0000e-03 eta: 9:46:25 time: 0.3097 data_time: 0.0784 memory: 6717 grad_norm: 2.9455 loss: 1.3305 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.3305 2023/04/14 06:21:14 - mmengine - INFO - Epoch(train) [50][ 820/1879] lr: 2.0000e-03 eta: 9:46:19 time: 0.4453 data_time: 0.0767 memory: 6717 grad_norm: 2.8835 loss: 1.2345 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2345 2023/04/14 06:21:21 - mmengine - INFO - Epoch(train) [50][ 840/1879] lr: 2.0000e-03 eta: 9:46:10 time: 0.3230 data_time: 0.0351 memory: 6717 grad_norm: 2.9132 loss: 1.2023 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2023 2023/04/14 06:21:29 - mmengine - INFO - Epoch(train) [50][ 860/1879] lr: 2.0000e-03 eta: 9:46:04 time: 0.4107 data_time: 0.0528 memory: 6717 grad_norm: 3.0123 loss: 1.4133 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4133 2023/04/14 06:21:36 - mmengine - INFO - Epoch(train) [50][ 880/1879] lr: 2.0000e-03 eta: 9:45:56 time: 0.3368 data_time: 0.0369 memory: 6717 grad_norm: 2.9427 loss: 1.3554 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.3554 2023/04/14 06:21:43 - mmengine - INFO - Epoch(train) [50][ 900/1879] lr: 2.0000e-03 eta: 9:45:49 time: 0.3894 data_time: 0.0364 memory: 6717 grad_norm: 3.0547 loss: 1.2519 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2519 2023/04/14 06:21:50 - mmengine - INFO - Epoch(train) [50][ 920/1879] lr: 2.0000e-03 eta: 9:45:40 time: 0.3348 data_time: 0.0403 memory: 6717 grad_norm: 2.9254 loss: 1.2671 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2671 2023/04/14 06:21:54 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 06:21:58 - mmengine - INFO - Epoch(train) [50][ 940/1879] lr: 2.0000e-03 eta: 9:45:34 time: 0.4027 data_time: 0.0935 memory: 6717 grad_norm: 2.9420 loss: 1.2542 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2542 2023/04/14 06:22:04 - mmengine - INFO - Epoch(train) [50][ 960/1879] lr: 2.0000e-03 eta: 9:45:25 time: 0.2980 data_time: 0.0341 memory: 6717 grad_norm: 2.8981 loss: 1.3369 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.3369 2023/04/14 06:22:13 - mmengine - INFO - Epoch(train) [50][ 980/1879] lr: 2.0000e-03 eta: 9:45:18 time: 0.4179 data_time: 0.0960 memory: 6717 grad_norm: 2.8703 loss: 1.1004 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1004 2023/04/14 06:22:19 - mmengine - INFO - Epoch(train) [50][1000/1879] lr: 2.0000e-03 eta: 9:45:10 time: 0.3006 data_time: 0.0681 memory: 6717 grad_norm: 2.9105 loss: 1.2365 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2365 2023/04/14 06:22:26 - mmengine - INFO - Epoch(train) [50][1020/1879] lr: 2.0000e-03 eta: 9:45:03 time: 0.3951 data_time: 0.2046 memory: 6717 grad_norm: 2.9340 loss: 1.3321 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3321 2023/04/14 06:22:34 - mmengine - INFO - Epoch(train) [50][1040/1879] lr: 2.0000e-03 eta: 9:44:55 time: 0.3532 data_time: 0.1765 memory: 6717 grad_norm: 2.9749 loss: 1.3303 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3303 2023/04/14 06:22:42 - mmengine - INFO - Epoch(train) [50][1060/1879] lr: 2.0000e-03 eta: 9:44:48 time: 0.4107 data_time: 0.1822 memory: 6717 grad_norm: 2.9381 loss: 1.3073 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3073 2023/04/14 06:22:48 - mmengine - INFO - Epoch(train) [50][1080/1879] lr: 2.0000e-03 eta: 9:44:40 time: 0.3192 data_time: 0.1103 memory: 6717 grad_norm: 2.8808 loss: 1.1182 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1182 2023/04/14 06:22:56 - mmengine - INFO - Epoch(train) [50][1100/1879] lr: 2.0000e-03 eta: 9:44:33 time: 0.4023 data_time: 0.1709 memory: 6717 grad_norm: 2.8275 loss: 1.2293 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2293 2023/04/14 06:23:03 - mmengine - INFO - Epoch(train) [50][1120/1879] lr: 2.0000e-03 eta: 9:44:25 time: 0.3222 data_time: 0.1160 memory: 6717 grad_norm: 3.0064 loss: 1.2780 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2780 2023/04/14 06:23:11 - mmengine - INFO - Epoch(train) [50][1140/1879] lr: 2.0000e-03 eta: 9:44:19 time: 0.4331 data_time: 0.2891 memory: 6717 grad_norm: 2.8647 loss: 1.1305 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1305 2023/04/14 06:23:18 - mmengine - INFO - Epoch(train) [50][1160/1879] lr: 2.0000e-03 eta: 9:44:10 time: 0.3181 data_time: 0.1780 memory: 6717 grad_norm: 2.9507 loss: 1.3628 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3628 2023/04/14 06:23:26 - mmengine - INFO - Epoch(train) [50][1180/1879] lr: 2.0000e-03 eta: 9:44:04 time: 0.4201 data_time: 0.2756 memory: 6717 grad_norm: 2.9183 loss: 1.3021 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3021 2023/04/14 06:23:33 - mmengine - INFO - Epoch(train) [50][1200/1879] lr: 2.0000e-03 eta: 9:43:55 time: 0.3222 data_time: 0.1835 memory: 6717 grad_norm: 2.9287 loss: 1.3660 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3660 2023/04/14 06:23:41 - mmengine - INFO - Epoch(train) [50][1220/1879] lr: 2.0000e-03 eta: 9:43:49 time: 0.4410 data_time: 0.2959 memory: 6717 grad_norm: 2.9572 loss: 1.1199 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1199 2023/04/14 06:23:48 - mmengine - INFO - Epoch(train) [50][1240/1879] lr: 2.0000e-03 eta: 9:43:41 time: 0.3324 data_time: 0.1945 memory: 6717 grad_norm: 3.0082 loss: 1.3917 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3917 2023/04/14 06:23:56 - mmengine - INFO - Epoch(train) [50][1260/1879] lr: 2.0000e-03 eta: 9:43:34 time: 0.3915 data_time: 0.2498 memory: 6717 grad_norm: 2.9487 loss: 1.2388 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2388 2023/04/14 06:24:02 - mmengine - INFO - Epoch(train) [50][1280/1879] lr: 2.0000e-03 eta: 9:43:26 time: 0.3036 data_time: 0.1665 memory: 6717 grad_norm: 2.9086 loss: 1.3467 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3467 2023/04/14 06:24:10 - mmengine - INFO - Epoch(train) [50][1300/1879] lr: 2.0000e-03 eta: 9:43:19 time: 0.3951 data_time: 0.2548 memory: 6717 grad_norm: 2.9819 loss: 1.2372 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2372 2023/04/14 06:24:16 - mmengine - INFO - Epoch(train) [50][1320/1879] lr: 2.0000e-03 eta: 9:43:10 time: 0.3317 data_time: 0.1952 memory: 6717 grad_norm: 2.9782 loss: 1.2616 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2616 2023/04/14 06:24:25 - mmengine - INFO - Epoch(train) [50][1340/1879] lr: 2.0000e-03 eta: 9:43:04 time: 0.4080 data_time: 0.2684 memory: 6717 grad_norm: 2.9363 loss: 1.3699 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3699 2023/04/14 06:24:31 - mmengine - INFO - Epoch(train) [50][1360/1879] lr: 2.0000e-03 eta: 9:42:55 time: 0.3109 data_time: 0.1613 memory: 6717 grad_norm: 2.9674 loss: 1.1794 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1794 2023/04/14 06:24:40 - mmengine - INFO - Epoch(train) [50][1380/1879] lr: 2.0000e-03 eta: 9:42:49 time: 0.4437 data_time: 0.1286 memory: 6717 grad_norm: 2.9650 loss: 1.2884 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2884 2023/04/14 06:24:47 - mmengine - INFO - Epoch(train) [50][1400/1879] lr: 2.0000e-03 eta: 9:42:41 time: 0.3485 data_time: 0.0543 memory: 6717 grad_norm: 2.9400 loss: 1.1750 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1750 2023/04/14 06:24:56 - mmengine - INFO - Epoch(train) [50][1420/1879] lr: 2.0000e-03 eta: 9:42:35 time: 0.4405 data_time: 0.0156 memory: 6717 grad_norm: 2.9148 loss: 1.2425 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2425 2023/04/14 06:25:02 - mmengine - INFO - Epoch(train) [50][1440/1879] lr: 2.0000e-03 eta: 9:42:27 time: 0.3333 data_time: 0.0132 memory: 6717 grad_norm: 2.9085 loss: 1.1416 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1416 2023/04/14 06:25:10 - mmengine - INFO - Epoch(train) [50][1460/1879] lr: 2.0000e-03 eta: 9:42:21 time: 0.4135 data_time: 0.0136 memory: 6717 grad_norm: 2.9875 loss: 1.1944 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1944 2023/04/14 06:25:17 - mmengine - INFO - Epoch(train) [50][1480/1879] lr: 2.0000e-03 eta: 9:42:13 time: 0.3448 data_time: 0.0136 memory: 6717 grad_norm: 3.0273 loss: 1.3664 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3664 2023/04/14 06:25:25 - mmengine - INFO - Epoch(train) [50][1500/1879] lr: 2.0000e-03 eta: 9:42:06 time: 0.3994 data_time: 0.0172 memory: 6717 grad_norm: 2.9483 loss: 1.0254 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0254 2023/04/14 06:25:32 - mmengine - INFO - Epoch(train) [50][1520/1879] lr: 2.0000e-03 eta: 9:41:57 time: 0.3139 data_time: 0.0136 memory: 6717 grad_norm: 2.9398 loss: 1.2199 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2199 2023/04/14 06:25:40 - mmengine - INFO - Epoch(train) [50][1540/1879] lr: 2.0000e-03 eta: 9:41:51 time: 0.4247 data_time: 0.0164 memory: 6717 grad_norm: 2.9304 loss: 1.2530 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2530 2023/04/14 06:25:46 - mmengine - INFO - Epoch(train) [50][1560/1879] lr: 2.0000e-03 eta: 9:41:43 time: 0.3134 data_time: 0.0194 memory: 6717 grad_norm: 2.9385 loss: 1.1779 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1779 2023/04/14 06:25:54 - mmengine - INFO - Epoch(train) [50][1580/1879] lr: 2.0000e-03 eta: 9:41:35 time: 0.3798 data_time: 0.0865 memory: 6717 grad_norm: 2.9513 loss: 1.1752 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1752 2023/04/14 06:26:01 - mmengine - INFO - Epoch(train) [50][1600/1879] lr: 2.0000e-03 eta: 9:41:27 time: 0.3401 data_time: 0.0655 memory: 6717 grad_norm: 2.9147 loss: 1.0868 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0868 2023/04/14 06:26:09 - mmengine - INFO - Epoch(train) [50][1620/1879] lr: 2.0000e-03 eta: 9:41:21 time: 0.4057 data_time: 0.2133 memory: 6717 grad_norm: 2.9748 loss: 1.2210 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.2210 2023/04/14 06:26:15 - mmengine - INFO - Epoch(train) [50][1640/1879] lr: 2.0000e-03 eta: 9:41:12 time: 0.3293 data_time: 0.1630 memory: 6717 grad_norm: 2.9721 loss: 1.2717 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.2717 2023/04/14 06:26:24 - mmengine - INFO - Epoch(train) [50][1660/1879] lr: 2.0000e-03 eta: 9:41:06 time: 0.4382 data_time: 0.1933 memory: 6717 grad_norm: 2.9400 loss: 1.2684 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.2684 2023/04/14 06:26:30 - mmengine - INFO - Epoch(train) [50][1680/1879] lr: 2.0000e-03 eta: 9:40:58 time: 0.3102 data_time: 0.1166 memory: 6717 grad_norm: 2.8908 loss: 1.2331 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2331 2023/04/14 06:26:38 - mmengine - INFO - Epoch(train) [50][1700/1879] lr: 2.0000e-03 eta: 9:40:51 time: 0.3765 data_time: 0.2260 memory: 6717 grad_norm: 2.9412 loss: 1.3219 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3219 2023/04/14 06:26:46 - mmengine - INFO - Epoch(train) [50][1720/1879] lr: 2.0000e-03 eta: 9:40:43 time: 0.3781 data_time: 0.1069 memory: 6717 grad_norm: 3.0418 loss: 1.4401 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4401 2023/04/14 06:26:53 - mmengine - INFO - Epoch(train) [50][1740/1879] lr: 2.0000e-03 eta: 9:40:36 time: 0.3735 data_time: 0.0293 memory: 6717 grad_norm: 2.9012 loss: 1.2194 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2194 2023/04/14 06:27:00 - mmengine - INFO - Epoch(train) [50][1760/1879] lr: 2.0000e-03 eta: 9:40:28 time: 0.3570 data_time: 0.0140 memory: 6717 grad_norm: 2.9770 loss: 1.3625 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3625 2023/04/14 06:27:08 - mmengine - INFO - Epoch(train) [50][1780/1879] lr: 2.0000e-03 eta: 9:40:21 time: 0.3946 data_time: 0.0177 memory: 6717 grad_norm: 2.9893 loss: 1.2889 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2889 2023/04/14 06:27:16 - mmengine - INFO - Epoch(train) [50][1800/1879] lr: 2.0000e-03 eta: 9:40:14 time: 0.3946 data_time: 0.0131 memory: 6717 grad_norm: 2.9282 loss: 1.2719 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2719 2023/04/14 06:27:23 - mmengine - INFO - Epoch(train) [50][1820/1879] lr: 2.0000e-03 eta: 9:40:06 time: 0.3431 data_time: 0.0146 memory: 6717 grad_norm: 2.9476 loss: 1.1300 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1300 2023/04/14 06:27:30 - mmengine - INFO - Epoch(train) [50][1840/1879] lr: 2.0000e-03 eta: 9:39:59 time: 0.3632 data_time: 0.0145 memory: 6717 grad_norm: 2.9755 loss: 1.3038 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3038 2023/04/14 06:27:38 - mmengine - INFO - Epoch(train) [50][1860/1879] lr: 2.0000e-03 eta: 9:39:52 time: 0.3797 data_time: 0.0149 memory: 6717 grad_norm: 2.9567 loss: 1.3427 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3427 2023/04/14 06:27:45 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 06:27:45 - mmengine - INFO - Epoch(train) [50][1879/1879] lr: 2.0000e-03 eta: 9:39:45 time: 0.3505 data_time: 0.0121 memory: 6717 grad_norm: 2.9999 loss: 1.3039 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.3039 2023/04/14 06:27:54 - mmengine - INFO - Epoch(val) [50][ 20/155] eta: 0:01:02 time: 0.4598 data_time: 0.4268 memory: 1391 2023/04/14 06:28:00 - mmengine - INFO - Epoch(val) [50][ 40/155] eta: 0:00:44 time: 0.3194 data_time: 0.2867 memory: 1391 2023/04/14 06:28:09 - mmengine - INFO - Epoch(val) [50][ 60/155] eta: 0:00:38 time: 0.4290 data_time: 0.3951 memory: 1391 2023/04/14 06:28:15 - mmengine - INFO - Epoch(val) [50][ 80/155] eta: 0:00:28 time: 0.3160 data_time: 0.2828 memory: 1391 2023/04/14 06:28:24 - mmengine - INFO - Epoch(val) [50][100/155] eta: 0:00:21 time: 0.4559 data_time: 0.4229 memory: 1391 2023/04/14 06:28:30 - mmengine - INFO - Epoch(val) [50][120/155] eta: 0:00:13 time: 0.2966 data_time: 0.2639 memory: 1391 2023/04/14 06:28:39 - mmengine - INFO - Epoch(val) [50][140/155] eta: 0:00:05 time: 0.4450 data_time: 0.4120 memory: 1391 2023/04/14 06:28:46 - mmengine - INFO - Epoch(val) [50][155/155] acc/top1: 0.6585 acc/top5: 0.8694 acc/mean1: 0.6585 data_time: 0.3715 time: 0.4036 2023/04/14 06:28:46 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_49.pth is removed 2023/04/14 06:28:47 - mmengine - INFO - The best checkpoint with 0.6585 acc/top1 at 50 epoch is saved to best_acc_top1_epoch_50.pth. 2023/04/14 06:28:56 - mmengine - INFO - Epoch(train) [51][ 20/1879] lr: 2.0000e-03 eta: 9:39:39 time: 0.4498 data_time: 0.3168 memory: 6717 grad_norm: 2.8386 loss: 1.1473 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1473 2023/04/14 06:29:02 - mmengine - INFO - Epoch(train) [51][ 40/1879] lr: 2.0000e-03 eta: 9:39:30 time: 0.3192 data_time: 0.1638 memory: 6717 grad_norm: 2.9538 loss: 1.1502 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1502 2023/04/14 06:29:07 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 06:29:11 - mmengine - INFO - Epoch(train) [51][ 60/1879] lr: 2.0000e-03 eta: 9:39:24 time: 0.4425 data_time: 0.1909 memory: 6717 grad_norm: 2.9536 loss: 1.1702 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1702 2023/04/14 06:29:18 - mmengine - INFO - Epoch(train) [51][ 80/1879] lr: 2.0000e-03 eta: 9:39:16 time: 0.3432 data_time: 0.1064 memory: 6717 grad_norm: 2.9694 loss: 1.1679 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1679 2023/04/14 06:29:26 - mmengine - INFO - Epoch(train) [51][ 100/1879] lr: 2.0000e-03 eta: 9:39:10 time: 0.4352 data_time: 0.0593 memory: 6717 grad_norm: 2.9827 loss: 1.3872 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3872 2023/04/14 06:29:33 - mmengine - INFO - Epoch(train) [51][ 120/1879] lr: 2.0000e-03 eta: 9:39:02 time: 0.3074 data_time: 0.0126 memory: 6717 grad_norm: 2.9189 loss: 1.1357 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1357 2023/04/14 06:29:41 - mmengine - INFO - Epoch(train) [51][ 140/1879] lr: 2.0000e-03 eta: 9:38:55 time: 0.4066 data_time: 0.0155 memory: 6717 grad_norm: 2.8373 loss: 1.2080 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2080 2023/04/14 06:29:47 - mmengine - INFO - Epoch(train) [51][ 160/1879] lr: 2.0000e-03 eta: 9:38:46 time: 0.3060 data_time: 0.0130 memory: 6717 grad_norm: 2.9338 loss: 1.2334 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2334 2023/04/14 06:29:55 - mmengine - INFO - Epoch(train) [51][ 180/1879] lr: 2.0000e-03 eta: 9:38:39 time: 0.3973 data_time: 0.0182 memory: 6717 grad_norm: 2.9751 loss: 1.2245 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2245 2023/04/14 06:30:02 - mmengine - INFO - Epoch(train) [51][ 200/1879] lr: 2.0000e-03 eta: 9:38:32 time: 0.3610 data_time: 0.0744 memory: 6717 grad_norm: 2.8937 loss: 1.3513 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3513 2023/04/14 06:30:11 - mmengine - INFO - Epoch(train) [51][ 220/1879] lr: 2.0000e-03 eta: 9:38:26 time: 0.4634 data_time: 0.1580 memory: 6717 grad_norm: 2.9448 loss: 1.2932 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2932 2023/04/14 06:30:18 - mmengine - INFO - Epoch(train) [51][ 240/1879] lr: 2.0000e-03 eta: 9:38:18 time: 0.3119 data_time: 0.1051 memory: 6717 grad_norm: 2.9921 loss: 1.3555 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3555 2023/04/14 06:30:26 - mmengine - INFO - Epoch(train) [51][ 260/1879] lr: 2.0000e-03 eta: 9:38:11 time: 0.4149 data_time: 0.1935 memory: 6717 grad_norm: 2.9134 loss: 1.2064 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2064 2023/04/14 06:30:32 - mmengine - INFO - Epoch(train) [51][ 280/1879] lr: 2.0000e-03 eta: 9:38:03 time: 0.3295 data_time: 0.1598 memory: 6717 grad_norm: 3.0616 loss: 1.2701 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2701 2023/04/14 06:30:41 - mmengine - INFO - Epoch(train) [51][ 300/1879] lr: 2.0000e-03 eta: 9:37:56 time: 0.4065 data_time: 0.2165 memory: 6717 grad_norm: 2.9403 loss: 1.2130 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2130 2023/04/14 06:30:47 - mmengine - INFO - Epoch(train) [51][ 320/1879] lr: 2.0000e-03 eta: 9:37:48 time: 0.3196 data_time: 0.1760 memory: 6717 grad_norm: 2.9436 loss: 1.2658 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2658 2023/04/14 06:30:55 - mmengine - INFO - Epoch(train) [51][ 340/1879] lr: 2.0000e-03 eta: 9:37:41 time: 0.4202 data_time: 0.2777 memory: 6717 grad_norm: 2.8887 loss: 1.3045 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3045 2023/04/14 06:31:01 - mmengine - INFO - Epoch(train) [51][ 360/1879] lr: 2.0000e-03 eta: 9:37:33 time: 0.3014 data_time: 0.1610 memory: 6717 grad_norm: 2.9327 loss: 1.3525 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3525 2023/04/14 06:31:10 - mmengine - INFO - Epoch(train) [51][ 380/1879] lr: 2.0000e-03 eta: 9:37:26 time: 0.4291 data_time: 0.2852 memory: 6717 grad_norm: 2.9255 loss: 1.2159 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.2159 2023/04/14 06:31:16 - mmengine - INFO - Epoch(train) [51][ 400/1879] lr: 2.0000e-03 eta: 9:37:17 time: 0.2912 data_time: 0.1510 memory: 6717 grad_norm: 2.8618 loss: 1.0146 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0146 2023/04/14 06:31:23 - mmengine - INFO - Epoch(train) [51][ 420/1879] lr: 2.0000e-03 eta: 9:37:10 time: 0.3832 data_time: 0.2318 memory: 6717 grad_norm: 2.9731 loss: 1.3345 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3345 2023/04/14 06:31:30 - mmengine - INFO - Epoch(train) [51][ 440/1879] lr: 2.0000e-03 eta: 9:37:02 time: 0.3495 data_time: 0.2112 memory: 6717 grad_norm: 2.9919 loss: 1.2677 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2677 2023/04/14 06:31:39 - mmengine - INFO - Epoch(train) [51][ 460/1879] lr: 2.0000e-03 eta: 9:36:56 time: 0.4131 data_time: 0.2710 memory: 6717 grad_norm: 2.9926 loss: 1.4623 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4623 2023/04/14 06:31:45 - mmengine - INFO - Epoch(train) [51][ 480/1879] lr: 2.0000e-03 eta: 9:36:48 time: 0.3222 data_time: 0.1717 memory: 6717 grad_norm: 2.9611 loss: 1.1099 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1099 2023/04/14 06:31:53 - mmengine - INFO - Epoch(train) [51][ 500/1879] lr: 2.0000e-03 eta: 9:36:41 time: 0.4014 data_time: 0.2615 memory: 6717 grad_norm: 3.0056 loss: 1.2743 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.2743 2023/04/14 06:32:00 - mmengine - INFO - Epoch(train) [51][ 520/1879] lr: 2.0000e-03 eta: 9:36:33 time: 0.3469 data_time: 0.1493 memory: 6717 grad_norm: 2.9536 loss: 1.2915 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2915 2023/04/14 06:32:08 - mmengine - INFO - Epoch(train) [51][ 540/1879] lr: 2.0000e-03 eta: 9:36:26 time: 0.3871 data_time: 0.1963 memory: 6717 grad_norm: 2.9787 loss: 1.3121 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.3121 2023/04/14 06:32:15 - mmengine - INFO - Epoch(train) [51][ 560/1879] lr: 2.0000e-03 eta: 9:36:18 time: 0.3580 data_time: 0.1863 memory: 6717 grad_norm: 2.9627 loss: 1.2133 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.2133 2023/04/14 06:32:23 - mmengine - INFO - Epoch(train) [51][ 580/1879] lr: 2.0000e-03 eta: 9:36:12 time: 0.4168 data_time: 0.2750 memory: 6717 grad_norm: 2.9932 loss: 1.4164 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4164 2023/04/14 06:32:30 - mmengine - INFO - Epoch(train) [51][ 600/1879] lr: 2.0000e-03 eta: 9:36:03 time: 0.3292 data_time: 0.1891 memory: 6717 grad_norm: 2.9548 loss: 1.2981 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2981 2023/04/14 06:32:38 - mmengine - INFO - Epoch(train) [51][ 620/1879] lr: 2.0000e-03 eta: 9:35:56 time: 0.3754 data_time: 0.2352 memory: 6717 grad_norm: 2.9914 loss: 1.2897 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2897 2023/04/14 06:32:44 - mmengine - INFO - Epoch(train) [51][ 640/1879] lr: 2.0000e-03 eta: 9:35:48 time: 0.3257 data_time: 0.1862 memory: 6717 grad_norm: 2.9223 loss: 1.1098 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1098 2023/04/14 06:32:52 - mmengine - INFO - Epoch(train) [51][ 660/1879] lr: 2.0000e-03 eta: 9:35:41 time: 0.3837 data_time: 0.2397 memory: 6717 grad_norm: 2.9631 loss: 1.2397 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2397 2023/04/14 06:32:58 - mmengine - INFO - Epoch(train) [51][ 680/1879] lr: 2.0000e-03 eta: 9:35:33 time: 0.3376 data_time: 0.1704 memory: 6717 grad_norm: 3.0203 loss: 1.2588 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2588 2023/04/14 06:33:06 - mmengine - INFO - Epoch(train) [51][ 700/1879] lr: 2.0000e-03 eta: 9:35:26 time: 0.3961 data_time: 0.2272 memory: 6717 grad_norm: 2.9161 loss: 1.3523 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3523 2023/04/14 06:33:13 - mmengine - INFO - Epoch(train) [51][ 720/1879] lr: 2.0000e-03 eta: 9:35:18 time: 0.3304 data_time: 0.1970 memory: 6717 grad_norm: 2.9731 loss: 1.1181 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1181 2023/04/14 06:33:21 - mmengine - INFO - Epoch(train) [51][ 740/1879] lr: 2.0000e-03 eta: 9:35:11 time: 0.3899 data_time: 0.2382 memory: 6717 grad_norm: 2.9495 loss: 1.2363 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2363 2023/04/14 06:33:28 - mmengine - INFO - Epoch(train) [51][ 760/1879] lr: 2.0000e-03 eta: 9:35:03 time: 0.3475 data_time: 0.1346 memory: 6717 grad_norm: 2.9446 loss: 1.3048 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3048 2023/04/14 06:33:35 - mmengine - INFO - Epoch(train) [51][ 780/1879] lr: 2.0000e-03 eta: 9:34:55 time: 0.3724 data_time: 0.2258 memory: 6717 grad_norm: 2.9452 loss: 1.1082 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1082 2023/04/14 06:33:43 - mmengine - INFO - Epoch(train) [51][ 800/1879] lr: 2.0000e-03 eta: 9:34:48 time: 0.3846 data_time: 0.2084 memory: 6717 grad_norm: 3.0039 loss: 1.2433 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2433 2023/04/14 06:33:50 - mmengine - INFO - Epoch(train) [51][ 820/1879] lr: 2.0000e-03 eta: 9:34:40 time: 0.3452 data_time: 0.1350 memory: 6717 grad_norm: 3.0340 loss: 1.4844 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4844 2023/04/14 06:33:57 - mmengine - INFO - Epoch(train) [51][ 840/1879] lr: 2.0000e-03 eta: 9:34:33 time: 0.3845 data_time: 0.2015 memory: 6717 grad_norm: 2.9792 loss: 1.2764 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2764 2023/04/14 06:34:05 - mmengine - INFO - Epoch(train) [51][ 860/1879] lr: 2.0000e-03 eta: 9:34:26 time: 0.3744 data_time: 0.0847 memory: 6717 grad_norm: 2.9572 loss: 1.3329 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3329 2023/04/14 06:34:12 - mmengine - INFO - Epoch(train) [51][ 880/1879] lr: 2.0000e-03 eta: 9:34:18 time: 0.3321 data_time: 0.0552 memory: 6717 grad_norm: 2.9334 loss: 1.2547 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2547 2023/04/14 06:34:19 - mmengine - INFO - Epoch(train) [51][ 900/1879] lr: 2.0000e-03 eta: 9:34:11 time: 0.3785 data_time: 0.1079 memory: 6717 grad_norm: 3.0282 loss: 1.5155 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5155 2023/04/14 06:34:26 - mmengine - INFO - Epoch(train) [51][ 920/1879] lr: 2.0000e-03 eta: 9:34:02 time: 0.3357 data_time: 0.1557 memory: 6717 grad_norm: 2.9583 loss: 1.2742 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2742 2023/04/14 06:34:34 - mmengine - INFO - Epoch(train) [51][ 940/1879] lr: 2.0000e-03 eta: 9:33:56 time: 0.4065 data_time: 0.0498 memory: 6717 grad_norm: 2.9533 loss: 1.1811 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1811 2023/04/14 06:34:40 - mmengine - INFO - Epoch(train) [51][ 960/1879] lr: 2.0000e-03 eta: 9:33:47 time: 0.3098 data_time: 0.0500 memory: 6717 grad_norm: 3.0212 loss: 1.3243 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3243 2023/04/14 06:34:48 - mmengine - INFO - Epoch(train) [51][ 980/1879] lr: 2.0000e-03 eta: 9:33:41 time: 0.4116 data_time: 0.1027 memory: 6717 grad_norm: 3.0181 loss: 1.4240 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.4240 2023/04/14 06:34:55 - mmengine - INFO - Epoch(train) [51][1000/1879] lr: 2.0000e-03 eta: 9:33:32 time: 0.3356 data_time: 0.0159 memory: 6717 grad_norm: 3.0017 loss: 1.1941 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1941 2023/04/14 06:35:04 - mmengine - INFO - Epoch(train) [51][1020/1879] lr: 2.0000e-03 eta: 9:33:26 time: 0.4329 data_time: 0.0153 memory: 6717 grad_norm: 2.9511 loss: 1.1121 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1121 2023/04/14 06:35:11 - mmengine - INFO - Epoch(train) [51][1040/1879] lr: 2.0000e-03 eta: 9:33:18 time: 0.3411 data_time: 0.0129 memory: 6717 grad_norm: 2.9411 loss: 1.3264 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3264 2023/04/14 06:35:15 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 06:35:19 - mmengine - INFO - Epoch(train) [51][1060/1879] lr: 2.0000e-03 eta: 9:33:12 time: 0.4096 data_time: 0.0162 memory: 6717 grad_norm: 2.9408 loss: 1.1853 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1853 2023/04/14 06:35:26 - mmengine - INFO - Epoch(train) [51][1080/1879] lr: 2.0000e-03 eta: 9:33:04 time: 0.3383 data_time: 0.0123 memory: 6717 grad_norm: 2.9167 loss: 1.0655 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0655 2023/04/14 06:35:34 - mmengine - INFO - Epoch(train) [51][1100/1879] lr: 2.0000e-03 eta: 9:32:57 time: 0.4169 data_time: 0.0158 memory: 6717 grad_norm: 2.9588 loss: 1.3580 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3580 2023/04/14 06:35:41 - mmengine - INFO - Epoch(train) [51][1120/1879] lr: 2.0000e-03 eta: 9:32:49 time: 0.3299 data_time: 0.0218 memory: 6717 grad_norm: 2.9738 loss: 1.3131 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3131 2023/04/14 06:35:48 - mmengine - INFO - Epoch(train) [51][1140/1879] lr: 2.0000e-03 eta: 9:32:42 time: 0.3857 data_time: 0.0260 memory: 6717 grad_norm: 2.9493 loss: 1.1846 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1846 2023/04/14 06:35:54 - mmengine - INFO - Epoch(train) [51][1160/1879] lr: 2.0000e-03 eta: 9:32:33 time: 0.3045 data_time: 0.0661 memory: 6717 grad_norm: 2.9671 loss: 1.2609 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2609 2023/04/14 06:36:03 - mmengine - INFO - Epoch(train) [51][1180/1879] lr: 2.0000e-03 eta: 9:32:27 time: 0.4116 data_time: 0.0939 memory: 6717 grad_norm: 2.9810 loss: 1.3264 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3264 2023/04/14 06:36:10 - mmengine - INFO - Epoch(train) [51][1200/1879] lr: 2.0000e-03 eta: 9:32:19 time: 0.3427 data_time: 0.1140 memory: 6717 grad_norm: 2.9024 loss: 1.2558 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2558 2023/04/14 06:36:18 - mmengine - INFO - Epoch(train) [51][1220/1879] lr: 2.0000e-03 eta: 9:32:12 time: 0.4339 data_time: 0.1826 memory: 6717 grad_norm: 2.9692 loss: 1.2891 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2891 2023/04/14 06:36:25 - mmengine - INFO - Epoch(train) [51][1240/1879] lr: 2.0000e-03 eta: 9:32:04 time: 0.3194 data_time: 0.1662 memory: 6717 grad_norm: 2.9228 loss: 1.2453 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2453 2023/04/14 06:36:33 - mmengine - INFO - Epoch(train) [51][1260/1879] lr: 2.0000e-03 eta: 9:31:57 time: 0.4068 data_time: 0.2585 memory: 6717 grad_norm: 2.9829 loss: 1.3328 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3328 2023/04/14 06:36:39 - mmengine - INFO - Epoch(train) [51][1280/1879] lr: 2.0000e-03 eta: 9:31:49 time: 0.3358 data_time: 0.1928 memory: 6717 grad_norm: 2.9919 loss: 1.4393 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4393 2023/04/14 06:36:48 - mmengine - INFO - Epoch(train) [51][1300/1879] lr: 2.0000e-03 eta: 9:31:43 time: 0.4077 data_time: 0.2568 memory: 6717 grad_norm: 2.9766 loss: 1.2398 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2398 2023/04/14 06:36:54 - mmengine - INFO - Epoch(train) [51][1320/1879] lr: 2.0000e-03 eta: 9:31:35 time: 0.3446 data_time: 0.1974 memory: 6717 grad_norm: 3.0157 loss: 1.2377 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2377 2023/04/14 06:37:03 - mmengine - INFO - Epoch(train) [51][1340/1879] lr: 2.0000e-03 eta: 9:31:28 time: 0.4258 data_time: 0.2823 memory: 6717 grad_norm: 2.9975 loss: 1.2825 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2825 2023/04/14 06:37:10 - mmengine - INFO - Epoch(train) [51][1360/1879] lr: 2.0000e-03 eta: 9:31:20 time: 0.3328 data_time: 0.1903 memory: 6717 grad_norm: 2.9243 loss: 1.3573 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3573 2023/04/14 06:37:17 - mmengine - INFO - Epoch(train) [51][1380/1879] lr: 2.0000e-03 eta: 9:31:13 time: 0.3792 data_time: 0.2408 memory: 6717 grad_norm: 3.0294 loss: 1.4823 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.4823 2023/04/14 06:37:24 - mmengine - INFO - Epoch(train) [51][1400/1879] lr: 2.0000e-03 eta: 9:31:05 time: 0.3154 data_time: 0.1768 memory: 6717 grad_norm: 2.9259 loss: 1.2444 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2444 2023/04/14 06:37:32 - mmengine - INFO - Epoch(train) [51][1420/1879] lr: 2.0000e-03 eta: 9:30:59 time: 0.4450 data_time: 0.3039 memory: 6717 grad_norm: 2.9199 loss: 1.3481 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3481 2023/04/14 06:37:39 - mmengine - INFO - Epoch(train) [51][1440/1879] lr: 2.0000e-03 eta: 9:30:50 time: 0.3227 data_time: 0.1831 memory: 6717 grad_norm: 3.0102 loss: 1.2333 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2333 2023/04/14 06:37:47 - mmengine - INFO - Epoch(train) [51][1460/1879] lr: 2.0000e-03 eta: 9:30:44 time: 0.4099 data_time: 0.2728 memory: 6717 grad_norm: 3.0424 loss: 1.1909 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1909 2023/04/14 06:37:54 - mmengine - INFO - Epoch(train) [51][1480/1879] lr: 2.0000e-03 eta: 9:30:36 time: 0.3462 data_time: 0.2087 memory: 6717 grad_norm: 2.9708 loss: 1.3511 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.3511 2023/04/14 06:38:02 - mmengine - INFO - Epoch(train) [51][1500/1879] lr: 2.0000e-03 eta: 9:30:29 time: 0.4118 data_time: 0.2713 memory: 6717 grad_norm: 2.9244 loss: 1.2088 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2088 2023/04/14 06:38:09 - mmengine - INFO - Epoch(train) [51][1520/1879] lr: 2.0000e-03 eta: 9:30:21 time: 0.3273 data_time: 0.1877 memory: 6717 grad_norm: 2.9811 loss: 1.1743 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1743 2023/04/14 06:38:17 - mmengine - INFO - Epoch(train) [51][1540/1879] lr: 2.0000e-03 eta: 9:30:14 time: 0.4071 data_time: 0.2725 memory: 6717 grad_norm: 3.0077 loss: 1.3646 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3646 2023/04/14 06:38:23 - mmengine - INFO - Epoch(train) [51][1560/1879] lr: 2.0000e-03 eta: 9:30:06 time: 0.3226 data_time: 0.1829 memory: 6717 grad_norm: 3.0079 loss: 1.3603 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3603 2023/04/14 06:38:31 - mmengine - INFO - Epoch(train) [51][1580/1879] lr: 2.0000e-03 eta: 9:29:59 time: 0.3942 data_time: 0.2545 memory: 6717 grad_norm: 2.9234 loss: 1.1718 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1718 2023/04/14 06:38:38 - mmengine - INFO - Epoch(train) [51][1600/1879] lr: 2.0000e-03 eta: 9:29:51 time: 0.3458 data_time: 0.1770 memory: 6717 grad_norm: 3.0505 loss: 1.1754 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1754 2023/04/14 06:38:46 - mmengine - INFO - Epoch(train) [51][1620/1879] lr: 2.0000e-03 eta: 9:29:44 time: 0.3789 data_time: 0.1838 memory: 6717 grad_norm: 2.9650 loss: 1.1412 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1412 2023/04/14 06:38:53 - mmengine - INFO - Epoch(train) [51][1640/1879] lr: 2.0000e-03 eta: 9:29:37 time: 0.3767 data_time: 0.0811 memory: 6717 grad_norm: 2.9675 loss: 1.2693 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2693 2023/04/14 06:39:01 - mmengine - INFO - Epoch(train) [51][1660/1879] lr: 2.0000e-03 eta: 9:29:29 time: 0.3565 data_time: 0.0994 memory: 6717 grad_norm: 3.0078 loss: 1.3718 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3718 2023/04/14 06:39:08 - mmengine - INFO - Epoch(train) [51][1680/1879] lr: 2.0000e-03 eta: 9:29:21 time: 0.3659 data_time: 0.0364 memory: 6717 grad_norm: 2.9422 loss: 1.2797 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2797 2023/04/14 06:39:15 - mmengine - INFO - Epoch(train) [51][1700/1879] lr: 2.0000e-03 eta: 9:29:14 time: 0.3504 data_time: 0.0298 memory: 6717 grad_norm: 3.0390 loss: 1.3110 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3110 2023/04/14 06:39:22 - mmengine - INFO - Epoch(train) [51][1720/1879] lr: 2.0000e-03 eta: 9:29:06 time: 0.3498 data_time: 0.0631 memory: 6717 grad_norm: 2.9317 loss: 1.0722 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0722 2023/04/14 06:39:30 - mmengine - INFO - Epoch(train) [51][1740/1879] lr: 2.0000e-03 eta: 9:28:59 time: 0.3873 data_time: 0.1531 memory: 6717 grad_norm: 2.9258 loss: 1.2042 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2042 2023/04/14 06:39:37 - mmengine - INFO - Epoch(train) [51][1760/1879] lr: 2.0000e-03 eta: 9:28:51 time: 0.3461 data_time: 0.1140 memory: 6717 grad_norm: 2.9482 loss: 1.1463 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1463 2023/04/14 06:39:45 - mmengine - INFO - Epoch(train) [51][1780/1879] lr: 2.0000e-03 eta: 9:28:45 time: 0.4381 data_time: 0.2700 memory: 6717 grad_norm: 2.9796 loss: 1.2533 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2533 2023/04/14 06:39:52 - mmengine - INFO - Epoch(train) [51][1800/1879] lr: 2.0000e-03 eta: 9:28:36 time: 0.3234 data_time: 0.1845 memory: 6717 grad_norm: 2.9685 loss: 1.1180 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1180 2023/04/14 06:40:00 - mmengine - INFO - Epoch(train) [51][1820/1879] lr: 2.0000e-03 eta: 9:28:30 time: 0.4071 data_time: 0.2641 memory: 6717 grad_norm: 2.8947 loss: 1.0823 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0823 2023/04/14 06:40:07 - mmengine - INFO - Epoch(train) [51][1840/1879] lr: 2.0000e-03 eta: 9:28:22 time: 0.3466 data_time: 0.2061 memory: 6717 grad_norm: 2.9703 loss: 1.1607 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1607 2023/04/14 06:40:15 - mmengine - INFO - Epoch(train) [51][1860/1879] lr: 2.0000e-03 eta: 9:28:15 time: 0.4092 data_time: 0.2680 memory: 6717 grad_norm: 3.0020 loss: 1.4243 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4243 2023/04/14 06:40:21 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 06:40:21 - mmengine - INFO - Epoch(train) [51][1879/1879] lr: 2.0000e-03 eta: 9:28:07 time: 0.2929 data_time: 0.1601 memory: 6717 grad_norm: 3.0573 loss: 1.1054 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 1.1054 2023/04/14 06:40:21 - mmengine - INFO - Saving checkpoint at 51 epochs 2023/04/14 06:40:30 - mmengine - INFO - Epoch(val) [51][ 20/155] eta: 0:01:02 time: 0.4605 data_time: 0.4267 memory: 1391 2023/04/14 06:40:37 - mmengine - INFO - Epoch(val) [51][ 40/155] eta: 0:00:44 time: 0.3151 data_time: 0.2824 memory: 1391 2023/04/14 06:40:45 - mmengine - INFO - Epoch(val) [51][ 60/155] eta: 0:00:38 time: 0.4330 data_time: 0.3995 memory: 1391 2023/04/14 06:40:52 - mmengine - INFO - Epoch(val) [51][ 80/155] eta: 0:00:28 time: 0.3143 data_time: 0.2806 memory: 1391 2023/04/14 06:41:01 - mmengine - INFO - Epoch(val) [51][100/155] eta: 0:00:21 time: 0.4586 data_time: 0.4250 memory: 1391 2023/04/14 06:41:07 - mmengine - INFO - Epoch(val) [51][120/155] eta: 0:00:13 time: 0.2936 data_time: 0.2604 memory: 1391 2023/04/14 06:41:16 - mmengine - INFO - Epoch(val) [51][140/155] eta: 0:00:05 time: 0.4449 data_time: 0.4113 memory: 1391 2023/04/14 06:41:22 - mmengine - INFO - Epoch(val) [51][155/155] acc/top1: 0.6596 acc/top5: 0.8707 acc/mean1: 0.6595 data_time: 0.3722 time: 0.4049 2023/04/14 06:41:22 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_50.pth is removed 2023/04/14 06:41:23 - mmengine - INFO - The best checkpoint with 0.6596 acc/top1 at 51 epoch is saved to best_acc_top1_epoch_51.pth. 2023/04/14 06:41:32 - mmengine - INFO - Epoch(train) [52][ 20/1879] lr: 2.0000e-03 eta: 9:28:01 time: 0.4636 data_time: 0.3138 memory: 6717 grad_norm: 2.9120 loss: 1.1864 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1864 2023/04/14 06:41:39 - mmengine - INFO - Epoch(train) [52][ 40/1879] lr: 2.0000e-03 eta: 9:27:53 time: 0.3391 data_time: 0.2094 memory: 6717 grad_norm: 2.9107 loss: 1.1819 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1819 2023/04/14 06:41:48 - mmengine - INFO - Epoch(train) [52][ 60/1879] lr: 2.0000e-03 eta: 9:27:48 time: 0.4565 data_time: 0.1610 memory: 6717 grad_norm: 2.9736 loss: 1.3189 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3189 2023/04/14 06:41:55 - mmengine - INFO - Epoch(train) [52][ 80/1879] lr: 2.0000e-03 eta: 9:27:39 time: 0.3250 data_time: 0.0900 memory: 6717 grad_norm: 2.9815 loss: 1.2017 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2017 2023/04/14 06:42:03 - mmengine - INFO - Epoch(train) [52][ 100/1879] lr: 2.0000e-03 eta: 9:27:33 time: 0.4072 data_time: 0.1325 memory: 6717 grad_norm: 2.9619 loss: 1.4172 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4172 2023/04/14 06:42:10 - mmengine - INFO - Epoch(train) [52][ 120/1879] lr: 2.0000e-03 eta: 9:27:25 time: 0.3577 data_time: 0.1954 memory: 6717 grad_norm: 2.9196 loss: 1.1511 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.1511 2023/04/14 06:42:19 - mmengine - INFO - Epoch(train) [52][ 140/1879] lr: 2.0000e-03 eta: 9:27:19 time: 0.4419 data_time: 0.3006 memory: 6717 grad_norm: 2.9508 loss: 1.2998 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2998 2023/04/14 06:42:25 - mmengine - INFO - Epoch(train) [52][ 160/1879] lr: 2.0000e-03 eta: 9:27:10 time: 0.3073 data_time: 0.1630 memory: 6717 grad_norm: 2.9565 loss: 1.3279 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3279 2023/04/14 06:42:30 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 06:42:33 - mmengine - INFO - Epoch(train) [52][ 180/1879] lr: 2.0000e-03 eta: 9:27:03 time: 0.3902 data_time: 0.2540 memory: 6717 grad_norm: 2.9695 loss: 1.0496 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0496 2023/04/14 06:42:40 - mmengine - INFO - Epoch(train) [52][ 200/1879] lr: 2.0000e-03 eta: 9:26:55 time: 0.3463 data_time: 0.1255 memory: 6717 grad_norm: 2.9351 loss: 1.2979 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2979 2023/04/14 06:42:47 - mmengine - INFO - Epoch(train) [52][ 220/1879] lr: 2.0000e-03 eta: 9:26:48 time: 0.3573 data_time: 0.0456 memory: 6717 grad_norm: 2.9773 loss: 1.2617 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2617 2023/04/14 06:42:55 - mmengine - INFO - Epoch(train) [52][ 240/1879] lr: 2.0000e-03 eta: 9:26:41 time: 0.4022 data_time: 0.0151 memory: 6717 grad_norm: 2.9545 loss: 1.2445 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2445 2023/04/14 06:43:02 - mmengine - INFO - Epoch(train) [52][ 260/1879] lr: 2.0000e-03 eta: 9:26:33 time: 0.3514 data_time: 0.0395 memory: 6717 grad_norm: 2.9859 loss: 1.1329 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1329 2023/04/14 06:43:10 - mmengine - INFO - Epoch(train) [52][ 280/1879] lr: 2.0000e-03 eta: 9:26:27 time: 0.4098 data_time: 0.0152 memory: 6717 grad_norm: 2.9888 loss: 1.4075 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4075 2023/04/14 06:43:17 - mmengine - INFO - Epoch(train) [52][ 300/1879] lr: 2.0000e-03 eta: 9:26:18 time: 0.3258 data_time: 0.0163 memory: 6717 grad_norm: 2.9920 loss: 1.1340 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1340 2023/04/14 06:43:25 - mmengine - INFO - Epoch(train) [52][ 320/1879] lr: 2.0000e-03 eta: 9:26:12 time: 0.4114 data_time: 0.0143 memory: 6717 grad_norm: 2.9857 loss: 1.2727 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2727 2023/04/14 06:43:32 - mmengine - INFO - Epoch(train) [52][ 340/1879] lr: 2.0000e-03 eta: 9:26:04 time: 0.3334 data_time: 0.0878 memory: 6717 grad_norm: 2.9855 loss: 1.2482 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2482 2023/04/14 06:43:39 - mmengine - INFO - Epoch(train) [52][ 360/1879] lr: 2.0000e-03 eta: 9:25:56 time: 0.3642 data_time: 0.0383 memory: 6717 grad_norm: 3.0107 loss: 1.4406 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4406 2023/04/14 06:43:46 - mmengine - INFO - Epoch(train) [52][ 380/1879] lr: 2.0000e-03 eta: 9:25:49 time: 0.3772 data_time: 0.0991 memory: 6717 grad_norm: 3.0248 loss: 1.3116 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3116 2023/04/14 06:43:54 - mmengine - INFO - Epoch(train) [52][ 400/1879] lr: 2.0000e-03 eta: 9:25:42 time: 0.3815 data_time: 0.0832 memory: 6717 grad_norm: 2.9848 loss: 1.2390 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2390 2023/04/14 06:44:00 - mmengine - INFO - Epoch(train) [52][ 420/1879] lr: 2.0000e-03 eta: 9:25:33 time: 0.3119 data_time: 0.0385 memory: 6717 grad_norm: 2.9318 loss: 1.1097 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1097 2023/04/14 06:44:08 - mmengine - INFO - Epoch(train) [52][ 440/1879] lr: 2.0000e-03 eta: 9:25:26 time: 0.4120 data_time: 0.0863 memory: 6717 grad_norm: 2.9863 loss: 1.3130 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3130 2023/04/14 06:44:15 - mmengine - INFO - Epoch(train) [52][ 460/1879] lr: 2.0000e-03 eta: 9:25:19 time: 0.3421 data_time: 0.0528 memory: 6717 grad_norm: 3.0256 loss: 1.2921 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2921 2023/04/14 06:44:23 - mmengine - INFO - Epoch(train) [52][ 480/1879] lr: 2.0000e-03 eta: 9:25:11 time: 0.3785 data_time: 0.0127 memory: 6717 grad_norm: 2.9182 loss: 1.1490 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1490 2023/04/14 06:44:30 - mmengine - INFO - Epoch(train) [52][ 500/1879] lr: 2.0000e-03 eta: 9:25:03 time: 0.3438 data_time: 0.0522 memory: 6717 grad_norm: 2.9334 loss: 1.2941 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2941 2023/04/14 06:44:38 - mmengine - INFO - Epoch(train) [52][ 520/1879] lr: 2.0000e-03 eta: 9:24:56 time: 0.3958 data_time: 0.0803 memory: 6717 grad_norm: 2.9859 loss: 1.2879 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2879 2023/04/14 06:44:44 - mmengine - INFO - Epoch(train) [52][ 540/1879] lr: 2.0000e-03 eta: 9:24:49 time: 0.3407 data_time: 0.0650 memory: 6717 grad_norm: 2.9061 loss: 1.1315 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1315 2023/04/14 06:44:52 - mmengine - INFO - Epoch(train) [52][ 560/1879] lr: 2.0000e-03 eta: 9:24:41 time: 0.3815 data_time: 0.0132 memory: 6717 grad_norm: 3.0288 loss: 1.2636 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2636 2023/04/14 06:44:59 - mmengine - INFO - Epoch(train) [52][ 580/1879] lr: 2.0000e-03 eta: 9:24:34 time: 0.3548 data_time: 0.0641 memory: 6717 grad_norm: 2.9430 loss: 1.2368 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.2368 2023/04/14 06:45:07 - mmengine - INFO - Epoch(train) [52][ 600/1879] lr: 2.0000e-03 eta: 9:24:26 time: 0.3648 data_time: 0.0880 memory: 6717 grad_norm: 2.9416 loss: 1.1002 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1002 2023/04/14 06:45:14 - mmengine - INFO - Epoch(train) [52][ 620/1879] lr: 2.0000e-03 eta: 9:24:19 time: 0.3626 data_time: 0.1162 memory: 6717 grad_norm: 2.9867 loss: 1.1236 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1236 2023/04/14 06:45:22 - mmengine - INFO - Epoch(train) [52][ 640/1879] lr: 2.0000e-03 eta: 9:24:12 time: 0.4072 data_time: 0.0868 memory: 6717 grad_norm: 2.9843 loss: 1.3008 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3008 2023/04/14 06:45:28 - mmengine - INFO - Epoch(train) [52][ 660/1879] lr: 2.0000e-03 eta: 9:24:03 time: 0.3179 data_time: 0.0202 memory: 6717 grad_norm: 2.9679 loss: 1.2752 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2752 2023/04/14 06:45:37 - mmengine - INFO - Epoch(train) [52][ 680/1879] lr: 2.0000e-03 eta: 9:23:57 time: 0.4276 data_time: 0.0122 memory: 6717 grad_norm: 2.9191 loss: 1.2652 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2652 2023/04/14 06:45:44 - mmengine - INFO - Epoch(train) [52][ 700/1879] lr: 2.0000e-03 eta: 9:23:49 time: 0.3399 data_time: 0.0163 memory: 6717 grad_norm: 2.9988 loss: 1.3380 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.3380 2023/04/14 06:45:52 - mmengine - INFO - Epoch(train) [52][ 720/1879] lr: 2.0000e-03 eta: 9:23:42 time: 0.3927 data_time: 0.0123 memory: 6717 grad_norm: 3.0140 loss: 1.2329 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2329 2023/04/14 06:45:58 - mmengine - INFO - Epoch(train) [52][ 740/1879] lr: 2.0000e-03 eta: 9:23:34 time: 0.3250 data_time: 0.0164 memory: 6717 grad_norm: 2.9402 loss: 1.2989 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2989 2023/04/14 06:46:06 - mmengine - INFO - Epoch(train) [52][ 760/1879] lr: 2.0000e-03 eta: 9:23:27 time: 0.4049 data_time: 0.0141 memory: 6717 grad_norm: 3.0023 loss: 1.2877 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2877 2023/04/14 06:46:13 - mmengine - INFO - Epoch(train) [52][ 780/1879] lr: 2.0000e-03 eta: 9:23:19 time: 0.3417 data_time: 0.0172 memory: 6717 grad_norm: 3.0026 loss: 1.3588 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3588 2023/04/14 06:46:20 - mmengine - INFO - Epoch(train) [52][ 800/1879] lr: 2.0000e-03 eta: 9:23:11 time: 0.3357 data_time: 0.0160 memory: 6717 grad_norm: 2.9878 loss: 1.1821 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1821 2023/04/14 06:46:28 - mmengine - INFO - Epoch(train) [52][ 820/1879] lr: 2.0000e-03 eta: 9:23:04 time: 0.4076 data_time: 0.0449 memory: 6717 grad_norm: 2.9136 loss: 1.2311 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.2311 2023/04/14 06:46:35 - mmengine - INFO - Epoch(train) [52][ 840/1879] lr: 2.0000e-03 eta: 9:22:57 time: 0.3476 data_time: 0.0154 memory: 6717 grad_norm: 2.9639 loss: 1.2550 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2550 2023/04/14 06:46:44 - mmengine - INFO - Epoch(train) [52][ 860/1879] lr: 2.0000e-03 eta: 9:22:51 time: 0.4401 data_time: 0.0471 memory: 6717 grad_norm: 2.9770 loss: 1.2888 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2888 2023/04/14 06:46:50 - mmengine - INFO - Epoch(train) [52][ 880/1879] lr: 2.0000e-03 eta: 9:22:42 time: 0.3013 data_time: 0.0263 memory: 6717 grad_norm: 2.8711 loss: 1.3066 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3066 2023/04/14 06:46:57 - mmengine - INFO - Epoch(train) [52][ 900/1879] lr: 2.0000e-03 eta: 9:22:35 time: 0.3894 data_time: 0.0813 memory: 6717 grad_norm: 2.9743 loss: 1.1380 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1380 2023/04/14 06:47:04 - mmengine - INFO - Epoch(train) [52][ 920/1879] lr: 2.0000e-03 eta: 9:22:27 time: 0.3488 data_time: 0.0606 memory: 6717 grad_norm: 3.0172 loss: 1.2589 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2589 2023/04/14 06:47:12 - mmengine - INFO - Epoch(train) [52][ 940/1879] lr: 2.0000e-03 eta: 9:22:20 time: 0.3749 data_time: 0.1087 memory: 6717 grad_norm: 2.9429 loss: 1.2468 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2468 2023/04/14 06:47:19 - mmengine - INFO - Epoch(train) [52][ 960/1879] lr: 2.0000e-03 eta: 9:22:12 time: 0.3348 data_time: 0.1961 memory: 6717 grad_norm: 2.8999 loss: 1.2814 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2814 2023/04/14 06:47:27 - mmengine - INFO - Epoch(train) [52][ 980/1879] lr: 2.0000e-03 eta: 9:22:05 time: 0.4084 data_time: 0.2699 memory: 6717 grad_norm: 3.0527 loss: 1.2181 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2181 2023/04/14 06:47:34 - mmengine - INFO - Epoch(train) [52][1000/1879] lr: 2.0000e-03 eta: 9:21:57 time: 0.3476 data_time: 0.1952 memory: 6717 grad_norm: 3.0428 loss: 1.2281 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2281 2023/04/14 06:47:42 - mmengine - INFO - Epoch(train) [52][1020/1879] lr: 2.0000e-03 eta: 9:21:50 time: 0.3986 data_time: 0.2488 memory: 6717 grad_norm: 2.9684 loss: 1.1831 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1831 2023/04/14 06:47:48 - mmengine - INFO - Epoch(train) [52][1040/1879] lr: 2.0000e-03 eta: 9:21:42 time: 0.3090 data_time: 0.1657 memory: 6717 grad_norm: 3.0118 loss: 1.1622 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1622 2023/04/14 06:47:56 - mmengine - INFO - Epoch(train) [52][1060/1879] lr: 2.0000e-03 eta: 9:21:35 time: 0.4194 data_time: 0.2724 memory: 6717 grad_norm: 3.0417 loss: 1.3142 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3142 2023/04/14 06:48:03 - mmengine - INFO - Epoch(train) [52][1080/1879] lr: 2.0000e-03 eta: 9:21:27 time: 0.3444 data_time: 0.1987 memory: 6717 grad_norm: 2.9750 loss: 1.1744 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1744 2023/04/14 06:48:11 - mmengine - INFO - Epoch(train) [52][1100/1879] lr: 2.0000e-03 eta: 9:21:20 time: 0.3874 data_time: 0.2284 memory: 6717 grad_norm: 2.9775 loss: 1.2125 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2125 2023/04/14 06:48:18 - mmengine - INFO - Epoch(train) [52][1120/1879] lr: 2.0000e-03 eta: 9:21:12 time: 0.3424 data_time: 0.1727 memory: 6717 grad_norm: 2.9630 loss: 1.3063 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3063 2023/04/14 06:48:26 - mmengine - INFO - Epoch(train) [52][1140/1879] lr: 2.0000e-03 eta: 9:21:06 time: 0.4046 data_time: 0.1318 memory: 6717 grad_norm: 2.9561 loss: 1.1278 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1278 2023/04/14 06:48:33 - mmengine - INFO - Epoch(train) [52][1160/1879] lr: 2.0000e-03 eta: 9:20:58 time: 0.3398 data_time: 0.0526 memory: 6717 grad_norm: 3.0131 loss: 1.3791 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3791 2023/04/14 06:48:38 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 06:48:41 - mmengine - INFO - Epoch(train) [52][1180/1879] lr: 2.0000e-03 eta: 9:20:51 time: 0.4129 data_time: 0.2282 memory: 6717 grad_norm: 2.9969 loss: 1.2376 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2376 2023/04/14 06:48:47 - mmengine - INFO - Epoch(train) [52][1200/1879] lr: 2.0000e-03 eta: 9:20:42 time: 0.3019 data_time: 0.1603 memory: 6717 grad_norm: 3.0093 loss: 1.3031 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.3031 2023/04/14 06:48:56 - mmengine - INFO - Epoch(train) [52][1220/1879] lr: 2.0000e-03 eta: 9:20:36 time: 0.4372 data_time: 0.2456 memory: 6717 grad_norm: 2.9440 loss: 1.1951 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1951 2023/04/14 06:49:02 - mmengine - INFO - Epoch(train) [52][1240/1879] lr: 2.0000e-03 eta: 9:20:28 time: 0.3375 data_time: 0.0712 memory: 6717 grad_norm: 2.9583 loss: 1.0992 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0992 2023/04/14 06:49:11 - mmengine - INFO - Epoch(train) [52][1260/1879] lr: 2.0000e-03 eta: 9:20:21 time: 0.4134 data_time: 0.1112 memory: 6717 grad_norm: 3.0327 loss: 1.3398 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3398 2023/04/14 06:49:18 - mmengine - INFO - Epoch(train) [52][1280/1879] lr: 2.0000e-03 eta: 9:20:14 time: 0.3517 data_time: 0.0664 memory: 6717 grad_norm: 2.9904 loss: 1.1221 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1221 2023/04/14 06:49:26 - mmengine - INFO - Epoch(train) [52][1300/1879] lr: 2.0000e-03 eta: 9:20:07 time: 0.4276 data_time: 0.0139 memory: 6717 grad_norm: 2.9381 loss: 1.2739 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.2739 2023/04/14 06:49:33 - mmengine - INFO - Epoch(train) [52][1320/1879] lr: 2.0000e-03 eta: 9:19:59 time: 0.3092 data_time: 0.0150 memory: 6717 grad_norm: 3.0646 loss: 1.3055 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3055 2023/04/14 06:49:40 - mmengine - INFO - Epoch(train) [52][1340/1879] lr: 2.0000e-03 eta: 9:19:52 time: 0.3938 data_time: 0.0158 memory: 6717 grad_norm: 2.9640 loss: 1.0568 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0568 2023/04/14 06:49:47 - mmengine - INFO - Epoch(train) [52][1360/1879] lr: 2.0000e-03 eta: 9:19:43 time: 0.3099 data_time: 0.0135 memory: 6717 grad_norm: 2.9659 loss: 1.1517 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1517 2023/04/14 06:49:54 - mmengine - INFO - Epoch(train) [52][1380/1879] lr: 2.0000e-03 eta: 9:19:36 time: 0.3851 data_time: 0.0225 memory: 6717 grad_norm: 3.0256 loss: 1.4637 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4637 2023/04/14 06:50:01 - mmengine - INFO - Epoch(train) [52][1400/1879] lr: 2.0000e-03 eta: 9:19:28 time: 0.3418 data_time: 0.0133 memory: 6717 grad_norm: 2.9829 loss: 1.1591 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1591 2023/04/14 06:50:10 - mmengine - INFO - Epoch(train) [52][1420/1879] lr: 2.0000e-03 eta: 9:19:22 time: 0.4413 data_time: 0.0144 memory: 6717 grad_norm: 2.8919 loss: 1.1725 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1725 2023/04/14 06:50:16 - mmengine - INFO - Epoch(train) [52][1440/1879] lr: 2.0000e-03 eta: 9:19:14 time: 0.3231 data_time: 0.0124 memory: 6717 grad_norm: 2.9410 loss: 1.0824 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0824 2023/04/14 06:50:24 - mmengine - INFO - Epoch(train) [52][1460/1879] lr: 2.0000e-03 eta: 9:19:07 time: 0.3981 data_time: 0.0158 memory: 6717 grad_norm: 3.1341 loss: 1.3519 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3519 2023/04/14 06:50:31 - mmengine - INFO - Epoch(train) [52][1480/1879] lr: 2.0000e-03 eta: 9:18:59 time: 0.3301 data_time: 0.0147 memory: 6717 grad_norm: 2.9785 loss: 1.1433 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1433 2023/04/14 06:50:39 - mmengine - INFO - Epoch(train) [52][1500/1879] lr: 2.0000e-03 eta: 9:18:52 time: 0.3899 data_time: 0.0136 memory: 6717 grad_norm: 3.0137 loss: 1.2483 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2483 2023/04/14 06:50:45 - mmengine - INFO - Epoch(train) [52][1520/1879] lr: 2.0000e-03 eta: 9:18:44 time: 0.3236 data_time: 0.0149 memory: 6717 grad_norm: 2.9359 loss: 1.2997 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2997 2023/04/14 06:50:53 - mmengine - INFO - Epoch(train) [52][1540/1879] lr: 2.0000e-03 eta: 9:18:37 time: 0.3979 data_time: 0.0141 memory: 6717 grad_norm: 2.9871 loss: 1.2161 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2161 2023/04/14 06:51:00 - mmengine - INFO - Epoch(train) [52][1560/1879] lr: 2.0000e-03 eta: 9:18:29 time: 0.3437 data_time: 0.0148 memory: 6717 grad_norm: 2.9361 loss: 1.2997 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2997 2023/04/14 06:51:08 - mmengine - INFO - Epoch(train) [52][1580/1879] lr: 2.0000e-03 eta: 9:18:22 time: 0.4009 data_time: 0.0136 memory: 6717 grad_norm: 3.0392 loss: 1.1549 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1549 2023/04/14 06:51:15 - mmengine - INFO - Epoch(train) [52][1600/1879] lr: 2.0000e-03 eta: 9:18:14 time: 0.3334 data_time: 0.0148 memory: 6717 grad_norm: 2.9369 loss: 1.3753 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.3753 2023/04/14 06:51:23 - mmengine - INFO - Epoch(train) [52][1620/1879] lr: 2.0000e-03 eta: 9:18:07 time: 0.4078 data_time: 0.0133 memory: 6717 grad_norm: 2.9927 loss: 1.4619 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.4619 2023/04/14 06:51:29 - mmengine - INFO - Epoch(train) [52][1640/1879] lr: 2.0000e-03 eta: 9:17:59 time: 0.3151 data_time: 0.0153 memory: 6717 grad_norm: 3.0845 loss: 1.4626 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.4626 2023/04/14 06:51:38 - mmengine - INFO - Epoch(train) [52][1660/1879] lr: 2.0000e-03 eta: 9:17:52 time: 0.4133 data_time: 0.0148 memory: 6717 grad_norm: 2.9910 loss: 1.2535 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2535 2023/04/14 06:51:44 - mmengine - INFO - Epoch(train) [52][1680/1879] lr: 2.0000e-03 eta: 9:17:44 time: 0.3118 data_time: 0.0151 memory: 6717 grad_norm: 3.0064 loss: 1.2041 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2041 2023/04/14 06:51:52 - mmengine - INFO - Epoch(train) [52][1700/1879] lr: 2.0000e-03 eta: 9:17:37 time: 0.4196 data_time: 0.0152 memory: 6717 grad_norm: 2.9981 loss: 1.1972 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1972 2023/04/14 06:51:59 - mmengine - INFO - Epoch(train) [52][1720/1879] lr: 2.0000e-03 eta: 9:17:29 time: 0.3537 data_time: 0.0135 memory: 6717 grad_norm: 2.9808 loss: 1.1238 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1238 2023/04/14 06:52:08 - mmengine - INFO - Epoch(train) [52][1740/1879] lr: 2.0000e-03 eta: 9:17:23 time: 0.4130 data_time: 0.0142 memory: 6717 grad_norm: 2.9734 loss: 1.3352 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3352 2023/04/14 06:52:14 - mmengine - INFO - Epoch(train) [52][1760/1879] lr: 2.0000e-03 eta: 9:17:15 time: 0.3441 data_time: 0.0146 memory: 6717 grad_norm: 3.1020 loss: 1.1749 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1749 2023/04/14 06:52:22 - mmengine - INFO - Epoch(train) [52][1780/1879] lr: 2.0000e-03 eta: 9:17:08 time: 0.3987 data_time: 0.0159 memory: 6717 grad_norm: 3.0229 loss: 1.2921 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2921 2023/04/14 06:52:29 - mmengine - INFO - Epoch(train) [52][1800/1879] lr: 2.0000e-03 eta: 9:17:00 time: 0.3349 data_time: 0.0132 memory: 6717 grad_norm: 2.9354 loss: 1.0699 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0699 2023/04/14 06:52:37 - mmengine - INFO - Epoch(train) [52][1820/1879] lr: 2.0000e-03 eta: 9:16:53 time: 0.3839 data_time: 0.0153 memory: 6717 grad_norm: 2.9717 loss: 1.2816 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2816 2023/04/14 06:52:43 - mmengine - INFO - Epoch(train) [52][1840/1879] lr: 2.0000e-03 eta: 9:16:45 time: 0.3313 data_time: 0.0144 memory: 6717 grad_norm: 3.1056 loss: 1.2584 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.2584 2023/04/14 06:52:51 - mmengine - INFO - Epoch(train) [52][1860/1879] lr: 2.0000e-03 eta: 9:16:37 time: 0.3722 data_time: 0.0156 memory: 6717 grad_norm: 3.0170 loss: 1.2654 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2654 2023/04/14 06:52:57 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 06:52:57 - mmengine - INFO - Epoch(train) [52][1879/1879] lr: 2.0000e-03 eta: 9:16:30 time: 0.3257 data_time: 0.0141 memory: 6717 grad_norm: 3.0372 loss: 1.3387 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 1.3387 2023/04/14 06:53:06 - mmengine - INFO - Epoch(val) [52][ 20/155] eta: 0:01:02 time: 0.4607 data_time: 0.4279 memory: 1391 2023/04/14 06:53:13 - mmengine - INFO - Epoch(val) [52][ 40/155] eta: 0:00:45 time: 0.3248 data_time: 0.2919 memory: 1391 2023/04/14 06:53:21 - mmengine - INFO - Epoch(val) [52][ 60/155] eta: 0:00:38 time: 0.4208 data_time: 0.3879 memory: 1391 2023/04/14 06:53:28 - mmengine - INFO - Epoch(val) [52][ 80/155] eta: 0:00:28 time: 0.3235 data_time: 0.2905 memory: 1391 2023/04/14 06:53:36 - mmengine - INFO - Epoch(val) [52][100/155] eta: 0:00:21 time: 0.4191 data_time: 0.3860 memory: 1391 2023/04/14 06:53:43 - mmengine - INFO - Epoch(val) [52][120/155] eta: 0:00:13 time: 0.3346 data_time: 0.3015 memory: 1391 2023/04/14 06:53:53 - mmengine - INFO - Epoch(val) [52][140/155] eta: 0:00:05 time: 0.4851 data_time: 0.4526 memory: 1391 2023/04/14 06:54:00 - mmengine - INFO - Epoch(val) [52][155/155] acc/top1: 0.6610 acc/top5: 0.8697 acc/mean1: 0.6610 data_time: 0.4207 time: 0.4524 2023/04/14 06:54:00 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_51.pth is removed 2023/04/14 06:54:00 - mmengine - INFO - The best checkpoint with 0.6610 acc/top1 at 52 epoch is saved to best_acc_top1_epoch_52.pth. 2023/04/14 06:54:10 - mmengine - INFO - Epoch(train) [53][ 20/1879] lr: 2.0000e-03 eta: 9:16:24 time: 0.4876 data_time: 0.2695 memory: 6717 grad_norm: 2.9544 loss: 1.1662 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1662 2023/04/14 06:54:16 - mmengine - INFO - Epoch(train) [53][ 40/1879] lr: 2.0000e-03 eta: 9:16:16 time: 0.3191 data_time: 0.1057 memory: 6717 grad_norm: 2.9825 loss: 1.1305 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1305 2023/04/14 06:54:24 - mmengine - INFO - Epoch(train) [53][ 60/1879] lr: 2.0000e-03 eta: 9:16:09 time: 0.3915 data_time: 0.1389 memory: 6717 grad_norm: 2.9861 loss: 1.2623 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2623 2023/04/14 06:54:31 - mmengine - INFO - Epoch(train) [53][ 80/1879] lr: 2.0000e-03 eta: 9:16:01 time: 0.3313 data_time: 0.0603 memory: 6717 grad_norm: 3.0306 loss: 1.2361 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2361 2023/04/14 06:54:39 - mmengine - INFO - Epoch(train) [53][ 100/1879] lr: 2.0000e-03 eta: 9:15:54 time: 0.4070 data_time: 0.0333 memory: 6717 grad_norm: 3.0347 loss: 1.1658 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1658 2023/04/14 06:54:46 - mmengine - INFO - Epoch(train) [53][ 120/1879] lr: 2.0000e-03 eta: 9:15:47 time: 0.3651 data_time: 0.0162 memory: 6717 grad_norm: 2.9736 loss: 1.2389 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2389 2023/04/14 06:54:54 - mmengine - INFO - Epoch(train) [53][ 140/1879] lr: 2.0000e-03 eta: 9:15:40 time: 0.4096 data_time: 0.0137 memory: 6717 grad_norm: 3.0880 loss: 1.2687 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2687 2023/04/14 06:55:01 - mmengine - INFO - Epoch(train) [53][ 160/1879] lr: 2.0000e-03 eta: 9:15:32 time: 0.3152 data_time: 0.0135 memory: 6717 grad_norm: 2.9865 loss: 1.2390 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2390 2023/04/14 06:55:09 - mmengine - INFO - Epoch(train) [53][ 180/1879] lr: 2.0000e-03 eta: 9:15:25 time: 0.3983 data_time: 0.0150 memory: 6717 grad_norm: 3.0315 loss: 1.2233 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2233 2023/04/14 06:55:16 - mmengine - INFO - Epoch(train) [53][ 200/1879] lr: 2.0000e-03 eta: 9:15:17 time: 0.3398 data_time: 0.0132 memory: 6717 grad_norm: 2.9819 loss: 1.1727 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1727 2023/04/14 06:55:24 - mmengine - INFO - Epoch(train) [53][ 220/1879] lr: 2.0000e-03 eta: 9:15:11 time: 0.4394 data_time: 0.0151 memory: 6717 grad_norm: 3.0149 loss: 1.3246 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3246 2023/04/14 06:55:31 - mmengine - INFO - Epoch(train) [53][ 240/1879] lr: 2.0000e-03 eta: 9:15:03 time: 0.3525 data_time: 0.0145 memory: 6717 grad_norm: 2.9887 loss: 1.4103 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.4103 2023/04/14 06:55:39 - mmengine - INFO - Epoch(train) [53][ 260/1879] lr: 2.0000e-03 eta: 9:14:56 time: 0.4049 data_time: 0.0145 memory: 6717 grad_norm: 3.0009 loss: 1.2569 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.2569 2023/04/14 06:55:45 - mmengine - INFO - Epoch(train) [53][ 280/1879] lr: 2.0000e-03 eta: 9:14:47 time: 0.2976 data_time: 0.0142 memory: 6717 grad_norm: 2.9646 loss: 1.2262 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2262 2023/04/14 06:55:51 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 06:55:54 - mmengine - INFO - Epoch(train) [53][ 300/1879] lr: 2.0000e-03 eta: 9:14:41 time: 0.4262 data_time: 0.0153 memory: 6717 grad_norm: 3.0522 loss: 1.2358 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2358 2023/04/14 06:56:01 - mmengine - INFO - Epoch(train) [53][ 320/1879] lr: 2.0000e-03 eta: 9:14:33 time: 0.3291 data_time: 0.0123 memory: 6717 grad_norm: 2.9812 loss: 1.1431 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.1431 2023/04/14 06:56:09 - mmengine - INFO - Epoch(train) [53][ 340/1879] lr: 2.0000e-03 eta: 9:14:26 time: 0.4029 data_time: 0.0163 memory: 6717 grad_norm: 3.0083 loss: 1.1763 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1763 2023/04/14 06:56:15 - mmengine - INFO - Epoch(train) [53][ 360/1879] lr: 2.0000e-03 eta: 9:14:18 time: 0.3078 data_time: 0.0130 memory: 6717 grad_norm: 2.9579 loss: 1.2662 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2662 2023/04/14 06:56:23 - mmengine - INFO - Epoch(train) [53][ 380/1879] lr: 2.0000e-03 eta: 9:14:11 time: 0.4207 data_time: 0.0152 memory: 6717 grad_norm: 3.0505 loss: 1.1459 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1459 2023/04/14 06:56:29 - mmengine - INFO - Epoch(train) [53][ 400/1879] lr: 2.0000e-03 eta: 9:14:03 time: 0.3152 data_time: 0.0200 memory: 6717 grad_norm: 3.0706 loss: 1.2515 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2515 2023/04/14 06:56:38 - mmengine - INFO - Epoch(train) [53][ 420/1879] lr: 2.0000e-03 eta: 9:13:56 time: 0.4180 data_time: 0.0170 memory: 6717 grad_norm: 3.0112 loss: 1.1982 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1982 2023/04/14 06:56:44 - mmengine - INFO - Epoch(train) [53][ 440/1879] lr: 2.0000e-03 eta: 9:13:48 time: 0.3068 data_time: 0.0135 memory: 6717 grad_norm: 2.9796 loss: 1.3017 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3017 2023/04/14 06:56:52 - mmengine - INFO - Epoch(train) [53][ 460/1879] lr: 2.0000e-03 eta: 9:13:41 time: 0.4055 data_time: 0.0388 memory: 6717 grad_norm: 2.8752 loss: 1.1296 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1296 2023/04/14 06:56:59 - mmengine - INFO - Epoch(train) [53][ 480/1879] lr: 2.0000e-03 eta: 9:13:33 time: 0.3653 data_time: 0.1009 memory: 6717 grad_norm: 2.9735 loss: 1.3061 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3061 2023/04/14 06:57:07 - mmengine - INFO - Epoch(train) [53][ 500/1879] lr: 2.0000e-03 eta: 9:13:26 time: 0.4009 data_time: 0.1082 memory: 6717 grad_norm: 3.0652 loss: 1.1675 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1675 2023/04/14 06:57:15 - mmengine - INFO - Epoch(train) [53][ 520/1879] lr: 2.0000e-03 eta: 9:13:19 time: 0.3787 data_time: 0.1877 memory: 6717 grad_norm: 3.0918 loss: 1.1917 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1917 2023/04/14 06:57:22 - mmengine - INFO - Epoch(train) [53][ 540/1879] lr: 2.0000e-03 eta: 9:13:12 time: 0.3560 data_time: 0.1352 memory: 6717 grad_norm: 2.9993 loss: 1.4767 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.4767 2023/04/14 06:57:29 - mmengine - INFO - Epoch(train) [53][ 560/1879] lr: 2.0000e-03 eta: 9:13:04 time: 0.3634 data_time: 0.2003 memory: 6717 grad_norm: 3.0433 loss: 1.2146 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2146 2023/04/14 06:57:37 - mmengine - INFO - Epoch(train) [53][ 580/1879] lr: 2.0000e-03 eta: 9:12:57 time: 0.3720 data_time: 0.0501 memory: 6717 grad_norm: 3.0576 loss: 1.2839 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2839 2023/04/14 06:57:43 - mmengine - INFO - Epoch(train) [53][ 600/1879] lr: 2.0000e-03 eta: 9:12:49 time: 0.3306 data_time: 0.0430 memory: 6717 grad_norm: 3.0355 loss: 1.1754 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1754 2023/04/14 06:57:52 - mmengine - INFO - Epoch(train) [53][ 620/1879] lr: 2.0000e-03 eta: 9:12:42 time: 0.4038 data_time: 0.0202 memory: 6717 grad_norm: 3.0084 loss: 1.1243 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1243 2023/04/14 06:57:58 - mmengine - INFO - Epoch(train) [53][ 640/1879] lr: 2.0000e-03 eta: 9:12:34 time: 0.3337 data_time: 0.0131 memory: 6717 grad_norm: 3.0571 loss: 1.2856 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2856 2023/04/14 06:58:07 - mmengine - INFO - Epoch(train) [53][ 660/1879] lr: 2.0000e-03 eta: 9:12:27 time: 0.4177 data_time: 0.0170 memory: 6717 grad_norm: 3.0304 loss: 1.1515 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1515 2023/04/14 06:58:13 - mmengine - INFO - Epoch(train) [53][ 680/1879] lr: 2.0000e-03 eta: 9:12:19 time: 0.3205 data_time: 0.0132 memory: 6717 grad_norm: 3.0107 loss: 1.2306 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2306 2023/04/14 06:58:21 - mmengine - INFO - Epoch(train) [53][ 700/1879] lr: 2.0000e-03 eta: 9:12:12 time: 0.4084 data_time: 0.0156 memory: 6717 grad_norm: 2.9477 loss: 1.2490 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2490 2023/04/14 06:58:28 - mmengine - INFO - Epoch(train) [53][ 720/1879] lr: 2.0000e-03 eta: 9:12:04 time: 0.3432 data_time: 0.0127 memory: 6717 grad_norm: 2.9745 loss: 1.2183 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2183 2023/04/14 06:58:35 - mmengine - INFO - Epoch(train) [53][ 740/1879] lr: 2.0000e-03 eta: 9:11:57 time: 0.3580 data_time: 0.0149 memory: 6717 grad_norm: 3.0357 loss: 1.2700 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2700 2023/04/14 06:58:42 - mmengine - INFO - Epoch(train) [53][ 760/1879] lr: 2.0000e-03 eta: 9:11:48 time: 0.3178 data_time: 0.0134 memory: 6717 grad_norm: 3.0427 loss: 1.2014 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2014 2023/04/14 06:58:50 - mmengine - INFO - Epoch(train) [53][ 780/1879] lr: 2.0000e-03 eta: 9:11:41 time: 0.4062 data_time: 0.0147 memory: 6717 grad_norm: 2.9989 loss: 1.3410 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3410 2023/04/14 06:58:56 - mmengine - INFO - Epoch(train) [53][ 800/1879] lr: 2.0000e-03 eta: 9:11:33 time: 0.3273 data_time: 0.0138 memory: 6717 grad_norm: 2.9580 loss: 1.0918 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0918 2023/04/14 06:59:04 - mmengine - INFO - Epoch(train) [53][ 820/1879] lr: 2.0000e-03 eta: 9:11:26 time: 0.3932 data_time: 0.0157 memory: 6717 grad_norm: 2.9965 loss: 1.1509 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1509 2023/04/14 06:59:11 - mmengine - INFO - Epoch(train) [53][ 840/1879] lr: 2.0000e-03 eta: 9:11:18 time: 0.3256 data_time: 0.0133 memory: 6717 grad_norm: 3.0062 loss: 1.1223 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1223 2023/04/14 06:59:19 - mmengine - INFO - Epoch(train) [53][ 860/1879] lr: 2.0000e-03 eta: 9:11:12 time: 0.4445 data_time: 0.0135 memory: 6717 grad_norm: 3.0450 loss: 1.1407 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1407 2023/04/14 06:59:26 - mmengine - INFO - Epoch(train) [53][ 880/1879] lr: 2.0000e-03 eta: 9:11:04 time: 0.3338 data_time: 0.0146 memory: 6717 grad_norm: 3.0473 loss: 1.1237 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.1237 2023/04/14 06:59:35 - mmengine - INFO - Epoch(train) [53][ 900/1879] lr: 2.0000e-03 eta: 9:10:58 time: 0.4430 data_time: 0.0139 memory: 6717 grad_norm: 2.9847 loss: 1.2722 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2722 2023/04/14 06:59:42 - mmengine - INFO - Epoch(train) [53][ 920/1879] lr: 2.0000e-03 eta: 9:10:50 time: 0.3401 data_time: 0.0147 memory: 6717 grad_norm: 3.0639 loss: 1.1910 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1910 2023/04/14 06:59:49 - mmengine - INFO - Epoch(train) [53][ 940/1879] lr: 2.0000e-03 eta: 9:10:42 time: 0.3491 data_time: 0.0133 memory: 6717 grad_norm: 2.9435 loss: 1.2104 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2104 2023/04/14 06:59:55 - mmengine - INFO - Epoch(train) [53][ 960/1879] lr: 2.0000e-03 eta: 9:10:34 time: 0.3273 data_time: 0.0155 memory: 6717 grad_norm: 2.9418 loss: 1.2844 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2844 2023/04/14 07:00:04 - mmengine - INFO - Epoch(train) [53][ 980/1879] lr: 2.0000e-03 eta: 9:10:27 time: 0.4171 data_time: 0.0133 memory: 6717 grad_norm: 3.0084 loss: 1.2265 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2265 2023/04/14 07:00:10 - mmengine - INFO - Epoch(train) [53][1000/1879] lr: 2.0000e-03 eta: 9:10:19 time: 0.3384 data_time: 0.0157 memory: 6717 grad_norm: 2.9943 loss: 1.3327 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3327 2023/04/14 07:00:18 - mmengine - INFO - Epoch(train) [53][1020/1879] lr: 2.0000e-03 eta: 9:10:12 time: 0.3653 data_time: 0.0137 memory: 6717 grad_norm: 3.0567 loss: 1.2726 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2726 2023/04/14 07:00:25 - mmengine - INFO - Epoch(train) [53][1040/1879] lr: 2.0000e-03 eta: 9:10:04 time: 0.3493 data_time: 0.0139 memory: 6717 grad_norm: 3.0107 loss: 1.2454 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2454 2023/04/14 07:00:33 - mmengine - INFO - Epoch(train) [53][1060/1879] lr: 2.0000e-03 eta: 9:09:58 time: 0.4221 data_time: 0.0150 memory: 6717 grad_norm: 2.9625 loss: 1.1738 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1738 2023/04/14 07:00:40 - mmengine - INFO - Epoch(train) [53][1080/1879] lr: 2.0000e-03 eta: 9:09:50 time: 0.3602 data_time: 0.0132 memory: 6717 grad_norm: 3.0029 loss: 1.1932 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1932 2023/04/14 07:00:48 - mmengine - INFO - Epoch(train) [53][1100/1879] lr: 2.0000e-03 eta: 9:09:43 time: 0.3911 data_time: 0.0122 memory: 6717 grad_norm: 2.9715 loss: 1.3271 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.3271 2023/04/14 07:00:54 - mmengine - INFO - Epoch(train) [53][1120/1879] lr: 2.0000e-03 eta: 9:09:35 time: 0.3083 data_time: 0.0174 memory: 6717 grad_norm: 2.9334 loss: 1.0784 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0784 2023/04/14 07:01:02 - mmengine - INFO - Epoch(train) [53][1140/1879] lr: 2.0000e-03 eta: 9:09:27 time: 0.3811 data_time: 0.0180 memory: 6717 grad_norm: 3.0071 loss: 1.1799 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1799 2023/04/14 07:01:09 - mmengine - INFO - Epoch(train) [53][1160/1879] lr: 2.0000e-03 eta: 9:09:20 time: 0.3591 data_time: 0.0916 memory: 6717 grad_norm: 3.0279 loss: 1.2802 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2802 2023/04/14 07:01:16 - mmengine - INFO - Epoch(train) [53][1180/1879] lr: 2.0000e-03 eta: 9:09:12 time: 0.3605 data_time: 0.0714 memory: 6717 grad_norm: 2.9190 loss: 1.2447 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.2447 2023/04/14 07:01:24 - mmengine - INFO - Epoch(train) [53][1200/1879] lr: 2.0000e-03 eta: 9:09:05 time: 0.3627 data_time: 0.0808 memory: 6717 grad_norm: 2.9537 loss: 1.1892 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1892 2023/04/14 07:01:31 - mmengine - INFO - Epoch(train) [53][1220/1879] lr: 2.0000e-03 eta: 9:08:57 time: 0.3509 data_time: 0.0537 memory: 6717 grad_norm: 2.9445 loss: 1.2094 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2094 2023/04/14 07:01:39 - mmengine - INFO - Epoch(train) [53][1240/1879] lr: 2.0000e-03 eta: 9:08:50 time: 0.4151 data_time: 0.1218 memory: 6717 grad_norm: 3.0355 loss: 1.3512 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3512 2023/04/14 07:01:46 - mmengine - INFO - Epoch(train) [53][1260/1879] lr: 2.0000e-03 eta: 9:08:42 time: 0.3252 data_time: 0.0986 memory: 6717 grad_norm: 2.9959 loss: 1.1904 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1904 2023/04/14 07:01:53 - mmengine - INFO - Epoch(train) [53][1280/1879] lr: 2.0000e-03 eta: 9:08:35 time: 0.3915 data_time: 0.1247 memory: 6717 grad_norm: 3.0672 loss: 1.2932 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2932 2023/04/14 07:01:57 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 07:02:00 - mmengine - INFO - Epoch(train) [53][1300/1879] lr: 2.0000e-03 eta: 9:08:27 time: 0.3344 data_time: 0.0285 memory: 6717 grad_norm: 2.9860 loss: 1.4229 top1_acc: 0.2500 top5_acc: 0.6875 loss_cls: 1.4229 2023/04/14 07:02:09 - mmengine - INFO - Epoch(train) [53][1320/1879] lr: 2.0000e-03 eta: 9:08:21 time: 0.4407 data_time: 0.0206 memory: 6717 grad_norm: 3.0402 loss: 1.2041 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2041 2023/04/14 07:02:15 - mmengine - INFO - Epoch(train) [53][1340/1879] lr: 2.0000e-03 eta: 9:08:12 time: 0.2916 data_time: 0.0121 memory: 6717 grad_norm: 2.9825 loss: 1.3068 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3068 2023/04/14 07:02:24 - mmengine - INFO - Epoch(train) [53][1360/1879] lr: 2.0000e-03 eta: 9:08:06 time: 0.4549 data_time: 0.0223 memory: 6717 grad_norm: 3.0227 loss: 1.2955 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2955 2023/04/14 07:02:30 - mmengine - INFO - Epoch(train) [53][1380/1879] lr: 2.0000e-03 eta: 9:07:58 time: 0.3162 data_time: 0.0122 memory: 6717 grad_norm: 3.0991 loss: 1.1392 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1392 2023/04/14 07:02:38 - mmengine - INFO - Epoch(train) [53][1400/1879] lr: 2.0000e-03 eta: 9:07:51 time: 0.4054 data_time: 0.0137 memory: 6717 grad_norm: 2.9912 loss: 1.1664 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1664 2023/04/14 07:02:45 - mmengine - INFO - Epoch(train) [53][1420/1879] lr: 2.0000e-03 eta: 9:07:43 time: 0.3427 data_time: 0.0144 memory: 6717 grad_norm: 3.0285 loss: 1.2107 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2107 2023/04/14 07:02:53 - mmengine - INFO - Epoch(train) [53][1440/1879] lr: 2.0000e-03 eta: 9:07:37 time: 0.4167 data_time: 0.0154 memory: 6717 grad_norm: 2.9674 loss: 1.2628 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.2628 2023/04/14 07:03:00 - mmengine - INFO - Epoch(train) [53][1460/1879] lr: 2.0000e-03 eta: 9:07:28 time: 0.3053 data_time: 0.0120 memory: 6717 grad_norm: 2.9289 loss: 1.0927 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0927 2023/04/14 07:03:08 - mmengine - INFO - Epoch(train) [53][1480/1879] lr: 2.0000e-03 eta: 9:07:21 time: 0.4135 data_time: 0.0152 memory: 6717 grad_norm: 2.9426 loss: 1.2490 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2490 2023/04/14 07:03:15 - mmengine - INFO - Epoch(train) [53][1500/1879] lr: 2.0000e-03 eta: 9:07:13 time: 0.3386 data_time: 0.0128 memory: 6717 grad_norm: 3.0843 loss: 1.2598 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2598 2023/04/14 07:03:23 - mmengine - INFO - Epoch(train) [53][1520/1879] lr: 2.0000e-03 eta: 9:07:07 time: 0.4025 data_time: 0.0144 memory: 6717 grad_norm: 2.9729 loss: 1.2921 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2921 2023/04/14 07:03:29 - mmengine - INFO - Epoch(train) [53][1540/1879] lr: 2.0000e-03 eta: 9:06:58 time: 0.3311 data_time: 0.0134 memory: 6717 grad_norm: 2.8712 loss: 1.2348 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2348 2023/04/14 07:03:37 - mmengine - INFO - Epoch(train) [53][1560/1879] lr: 2.0000e-03 eta: 9:06:51 time: 0.3735 data_time: 0.0159 memory: 6717 grad_norm: 2.9638 loss: 1.1490 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1490 2023/04/14 07:03:44 - mmengine - INFO - Epoch(train) [53][1580/1879] lr: 2.0000e-03 eta: 9:06:44 time: 0.3621 data_time: 0.0133 memory: 6717 grad_norm: 2.9715 loss: 1.1668 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1668 2023/04/14 07:03:52 - mmengine - INFO - Epoch(train) [53][1600/1879] lr: 2.0000e-03 eta: 9:06:36 time: 0.3842 data_time: 0.0137 memory: 6717 grad_norm: 3.0085 loss: 1.2826 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.2826 2023/04/14 07:03:58 - mmengine - INFO - Epoch(train) [53][1620/1879] lr: 2.0000e-03 eta: 9:06:28 time: 0.3154 data_time: 0.0145 memory: 6717 grad_norm: 2.9770 loss: 1.2317 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2317 2023/04/14 07:04:07 - mmengine - INFO - Epoch(train) [53][1640/1879] lr: 2.0000e-03 eta: 9:06:22 time: 0.4583 data_time: 0.0171 memory: 6717 grad_norm: 3.0060 loss: 1.4144 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4144 2023/04/14 07:04:13 - mmengine - INFO - Epoch(train) [53][1660/1879] lr: 2.0000e-03 eta: 9:06:13 time: 0.2999 data_time: 0.0134 memory: 6717 grad_norm: 2.9956 loss: 1.2578 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2578 2023/04/14 07:04:21 - mmengine - INFO - Epoch(train) [53][1680/1879] lr: 2.0000e-03 eta: 9:06:06 time: 0.3753 data_time: 0.0157 memory: 6717 grad_norm: 3.0007 loss: 1.2572 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2572 2023/04/14 07:04:28 - mmengine - INFO - Epoch(train) [53][1700/1879] lr: 2.0000e-03 eta: 9:05:58 time: 0.3408 data_time: 0.0352 memory: 6717 grad_norm: 3.0106 loss: 1.2819 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2819 2023/04/14 07:04:36 - mmengine - INFO - Epoch(train) [53][1720/1879] lr: 2.0000e-03 eta: 9:05:52 time: 0.4071 data_time: 0.0240 memory: 6717 grad_norm: 3.0101 loss: 1.1579 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1579 2023/04/14 07:04:43 - mmengine - INFO - Epoch(train) [53][1740/1879] lr: 2.0000e-03 eta: 9:05:44 time: 0.3449 data_time: 0.0150 memory: 6717 grad_norm: 3.0973 loss: 1.3149 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3149 2023/04/14 07:04:51 - mmengine - INFO - Epoch(train) [53][1760/1879] lr: 2.0000e-03 eta: 9:05:37 time: 0.4279 data_time: 0.0137 memory: 6717 grad_norm: 3.0356 loss: 1.3122 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3122 2023/04/14 07:04:58 - mmengine - INFO - Epoch(train) [53][1780/1879] lr: 2.0000e-03 eta: 9:05:29 time: 0.3216 data_time: 0.0643 memory: 6717 grad_norm: 3.0265 loss: 1.1414 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1414 2023/04/14 07:05:04 - mmengine - INFO - Epoch(train) [53][1800/1879] lr: 2.0000e-03 eta: 9:05:21 time: 0.3457 data_time: 0.1307 memory: 6717 grad_norm: 3.0700 loss: 1.2689 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2689 2023/04/14 07:05:13 - mmengine - INFO - Epoch(train) [53][1820/1879] lr: 2.0000e-03 eta: 9:05:14 time: 0.4061 data_time: 0.2614 memory: 6717 grad_norm: 3.0740 loss: 1.1934 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1934 2023/04/14 07:05:19 - mmengine - INFO - Epoch(train) [53][1840/1879] lr: 2.0000e-03 eta: 9:05:07 time: 0.3420 data_time: 0.1868 memory: 6717 grad_norm: 2.9295 loss: 1.1145 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1145 2023/04/14 07:05:27 - mmengine - INFO - Epoch(train) [53][1860/1879] lr: 2.0000e-03 eta: 9:04:59 time: 0.3819 data_time: 0.1875 memory: 6717 grad_norm: 3.0600 loss: 1.1287 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1287 2023/04/14 07:05:33 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 07:05:33 - mmengine - INFO - Epoch(train) [53][1879/1879] lr: 2.0000e-03 eta: 9:04:52 time: 0.3175 data_time: 0.1596 memory: 6717 grad_norm: 3.1035 loss: 1.2910 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.2910 2023/04/14 07:05:42 - mmengine - INFO - Epoch(val) [53][ 20/155] eta: 0:00:58 time: 0.4325 data_time: 0.3988 memory: 1391 2023/04/14 07:05:49 - mmengine - INFO - Epoch(val) [53][ 40/155] eta: 0:00:44 time: 0.3453 data_time: 0.3117 memory: 1391 2023/04/14 07:05:56 - mmengine - INFO - Epoch(val) [53][ 60/155] eta: 0:00:36 time: 0.3716 data_time: 0.3374 memory: 1391 2023/04/14 07:06:04 - mmengine - INFO - Epoch(val) [53][ 80/155] eta: 0:00:28 time: 0.3748 data_time: 0.3409 memory: 1391 2023/04/14 07:06:12 - mmengine - INFO - Epoch(val) [53][100/155] eta: 0:00:21 time: 0.4324 data_time: 0.3991 memory: 1391 2023/04/14 07:06:19 - mmengine - INFO - Epoch(val) [53][120/155] eta: 0:00:13 time: 0.3047 data_time: 0.2716 memory: 1391 2023/04/14 07:06:26 - mmengine - INFO - Epoch(val) [53][140/155] eta: 0:00:05 time: 0.3845 data_time: 0.3503 memory: 1391 2023/04/14 07:06:35 - mmengine - INFO - Epoch(val) [53][155/155] acc/top1: 0.6636 acc/top5: 0.8711 acc/mean1: 0.6635 data_time: 0.3077 time: 0.3410 2023/04/14 07:06:35 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_52.pth is removed 2023/04/14 07:06:36 - mmengine - INFO - The best checkpoint with 0.6636 acc/top1 at 53 epoch is saved to best_acc_top1_epoch_53.pth. 2023/04/14 07:06:45 - mmengine - INFO - Epoch(train) [54][ 20/1879] lr: 2.0000e-03 eta: 9:04:46 time: 0.4814 data_time: 0.3154 memory: 6717 grad_norm: 3.0277 loss: 1.3550 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3550 2023/04/14 07:06:52 - mmengine - INFO - Epoch(train) [54][ 40/1879] lr: 2.0000e-03 eta: 9:04:38 time: 0.3288 data_time: 0.1677 memory: 6717 grad_norm: 2.8757 loss: 1.2461 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2461 2023/04/14 07:07:01 - mmengine - INFO - Epoch(train) [54][ 60/1879] lr: 2.0000e-03 eta: 9:04:32 time: 0.4678 data_time: 0.0584 memory: 6717 grad_norm: 2.9743 loss: 1.0687 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0687 2023/04/14 07:07:07 - mmengine - INFO - Epoch(train) [54][ 80/1879] lr: 2.0000e-03 eta: 9:04:24 time: 0.3069 data_time: 0.0131 memory: 6717 grad_norm: 3.0467 loss: 1.1609 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1609 2023/04/14 07:07:16 - mmengine - INFO - Epoch(train) [54][ 100/1879] lr: 2.0000e-03 eta: 9:04:17 time: 0.4154 data_time: 0.0360 memory: 6717 grad_norm: 2.9806 loss: 1.1832 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1832 2023/04/14 07:07:22 - mmengine - INFO - Epoch(train) [54][ 120/1879] lr: 2.0000e-03 eta: 9:04:09 time: 0.3059 data_time: 0.0233 memory: 6717 grad_norm: 3.0916 loss: 1.2945 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2945 2023/04/14 07:07:30 - mmengine - INFO - Epoch(train) [54][ 140/1879] lr: 2.0000e-03 eta: 9:04:02 time: 0.4211 data_time: 0.0285 memory: 6717 grad_norm: 2.9099 loss: 1.1045 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1045 2023/04/14 07:07:37 - mmengine - INFO - Epoch(train) [54][ 160/1879] lr: 2.0000e-03 eta: 9:03:54 time: 0.3248 data_time: 0.0369 memory: 6717 grad_norm: 2.9941 loss: 1.2346 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2346 2023/04/14 07:07:44 - mmengine - INFO - Epoch(train) [54][ 180/1879] lr: 2.0000e-03 eta: 9:03:47 time: 0.3855 data_time: 0.0766 memory: 6717 grad_norm: 3.0440 loss: 1.3229 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3229 2023/04/14 07:07:51 - mmengine - INFO - Epoch(train) [54][ 200/1879] lr: 2.0000e-03 eta: 9:03:39 time: 0.3353 data_time: 0.0985 memory: 6717 grad_norm: 2.9066 loss: 1.1373 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.1373 2023/04/14 07:07:59 - mmengine - INFO - Epoch(train) [54][ 220/1879] lr: 2.0000e-03 eta: 9:03:32 time: 0.4035 data_time: 0.0571 memory: 6717 grad_norm: 2.9288 loss: 1.2452 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2452 2023/04/14 07:08:06 - mmengine - INFO - Epoch(train) [54][ 240/1879] lr: 2.0000e-03 eta: 9:03:24 time: 0.3308 data_time: 0.0600 memory: 6717 grad_norm: 3.0531 loss: 1.3181 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3181 2023/04/14 07:08:14 - mmengine - INFO - Epoch(train) [54][ 260/1879] lr: 2.0000e-03 eta: 9:03:17 time: 0.3863 data_time: 0.0468 memory: 6717 grad_norm: 2.9956 loss: 1.1792 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1792 2023/04/14 07:08:21 - mmengine - INFO - Epoch(train) [54][ 280/1879] lr: 2.0000e-03 eta: 9:03:09 time: 0.3533 data_time: 0.0817 memory: 6717 grad_norm: 2.9806 loss: 1.1379 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1379 2023/04/14 07:08:29 - mmengine - INFO - Epoch(train) [54][ 300/1879] lr: 2.0000e-03 eta: 9:03:02 time: 0.4147 data_time: 0.0263 memory: 6717 grad_norm: 3.0242 loss: 1.4833 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4833 2023/04/14 07:08:35 - mmengine - INFO - Epoch(train) [54][ 320/1879] lr: 2.0000e-03 eta: 9:02:54 time: 0.3164 data_time: 0.0138 memory: 6717 grad_norm: 3.0100 loss: 1.1614 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1614 2023/04/14 07:08:43 - mmengine - INFO - Epoch(train) [54][ 340/1879] lr: 2.0000e-03 eta: 9:02:47 time: 0.3908 data_time: 0.0193 memory: 6717 grad_norm: 3.0512 loss: 1.3103 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.3103 2023/04/14 07:08:50 - mmengine - INFO - Epoch(train) [54][ 360/1879] lr: 2.0000e-03 eta: 9:02:39 time: 0.3502 data_time: 0.0947 memory: 6717 grad_norm: 3.0611 loss: 1.1913 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1913 2023/04/14 07:08:58 - mmengine - INFO - Epoch(train) [54][ 380/1879] lr: 2.0000e-03 eta: 9:02:32 time: 0.3781 data_time: 0.0381 memory: 6717 grad_norm: 3.0396 loss: 1.1384 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1384 2023/04/14 07:09:05 - mmengine - INFO - Epoch(train) [54][ 400/1879] lr: 2.0000e-03 eta: 9:02:25 time: 0.3880 data_time: 0.1850 memory: 6717 grad_norm: 2.9695 loss: 1.0550 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.0550 2023/04/14 07:09:10 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 07:09:13 - mmengine - INFO - Epoch(train) [54][ 420/1879] lr: 2.0000e-03 eta: 9:02:17 time: 0.3619 data_time: 0.0963 memory: 6717 grad_norm: 2.9571 loss: 1.2257 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.2257 2023/04/14 07:09:21 - mmengine - INFO - Epoch(train) [54][ 440/1879] lr: 2.0000e-03 eta: 9:02:11 time: 0.4259 data_time: 0.2798 memory: 6717 grad_norm: 3.0109 loss: 1.2112 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2112 2023/04/14 07:09:28 - mmengine - INFO - Epoch(train) [54][ 460/1879] lr: 2.0000e-03 eta: 9:02:03 time: 0.3337 data_time: 0.1878 memory: 6717 grad_norm: 3.0017 loss: 1.3225 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.3225 2023/04/14 07:09:35 - mmengine - INFO - Epoch(train) [54][ 480/1879] lr: 2.0000e-03 eta: 9:01:56 time: 0.3845 data_time: 0.2430 memory: 6717 grad_norm: 2.9117 loss: 1.1088 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1088 2023/04/14 07:09:43 - mmengine - INFO - Epoch(train) [54][ 500/1879] lr: 2.0000e-03 eta: 9:01:48 time: 0.3517 data_time: 0.2083 memory: 6717 grad_norm: 2.9676 loss: 1.0961 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0961 2023/04/14 07:09:50 - mmengine - INFO - Epoch(train) [54][ 520/1879] lr: 2.0000e-03 eta: 9:01:41 time: 0.3875 data_time: 0.2480 memory: 6717 grad_norm: 3.0595 loss: 1.2971 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2971 2023/04/14 07:09:57 - mmengine - INFO - Epoch(train) [54][ 540/1879] lr: 2.0000e-03 eta: 9:01:33 time: 0.3209 data_time: 0.1744 memory: 6717 grad_norm: 3.0384 loss: 1.1170 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1170 2023/04/14 07:10:05 - mmengine - INFO - Epoch(train) [54][ 560/1879] lr: 2.0000e-03 eta: 9:01:26 time: 0.4362 data_time: 0.2967 memory: 6717 grad_norm: 3.0257 loss: 1.3721 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3721 2023/04/14 07:10:12 - mmengine - INFO - Epoch(train) [54][ 580/1879] lr: 2.0000e-03 eta: 9:01:18 time: 0.3210 data_time: 0.1805 memory: 6717 grad_norm: 3.0410 loss: 1.3528 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3528 2023/04/14 07:10:20 - mmengine - INFO - Epoch(train) [54][ 600/1879] lr: 2.0000e-03 eta: 9:01:11 time: 0.3842 data_time: 0.2417 memory: 6717 grad_norm: 3.0568 loss: 1.2141 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2141 2023/04/14 07:10:26 - mmengine - INFO - Epoch(train) [54][ 620/1879] lr: 2.0000e-03 eta: 9:01:03 time: 0.3251 data_time: 0.1723 memory: 6717 grad_norm: 2.9518 loss: 1.1056 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1056 2023/04/14 07:10:34 - mmengine - INFO - Epoch(train) [54][ 640/1879] lr: 2.0000e-03 eta: 9:00:56 time: 0.4187 data_time: 0.2783 memory: 6717 grad_norm: 2.9674 loss: 1.1974 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1974 2023/04/14 07:10:41 - mmengine - INFO - Epoch(train) [54][ 660/1879] lr: 2.0000e-03 eta: 9:00:48 time: 0.3205 data_time: 0.1662 memory: 6717 grad_norm: 3.0324 loss: 1.2953 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2953 2023/04/14 07:10:49 - mmengine - INFO - Epoch(train) [54][ 680/1879] lr: 2.0000e-03 eta: 9:00:41 time: 0.4056 data_time: 0.2539 memory: 6717 grad_norm: 3.0086 loss: 1.1373 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1373 2023/04/14 07:10:56 - mmengine - INFO - Epoch(train) [54][ 700/1879] lr: 2.0000e-03 eta: 9:00:33 time: 0.3564 data_time: 0.2108 memory: 6717 grad_norm: 3.0031 loss: 1.3108 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.3108 2023/04/14 07:11:04 - mmengine - INFO - Epoch(train) [54][ 720/1879] lr: 2.0000e-03 eta: 9:00:27 time: 0.4026 data_time: 0.2635 memory: 6717 grad_norm: 2.9836 loss: 1.2220 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2220 2023/04/14 07:11:11 - mmengine - INFO - Epoch(train) [54][ 740/1879] lr: 2.0000e-03 eta: 9:00:19 time: 0.3506 data_time: 0.2059 memory: 6717 grad_norm: 2.9753 loss: 1.2088 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2088 2023/04/14 07:11:19 - mmengine - INFO - Epoch(train) [54][ 760/1879] lr: 2.0000e-03 eta: 9:00:12 time: 0.3765 data_time: 0.2380 memory: 6717 grad_norm: 3.0229 loss: 1.1608 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1608 2023/04/14 07:11:25 - mmengine - INFO - Epoch(train) [54][ 780/1879] lr: 2.0000e-03 eta: 9:00:04 time: 0.3385 data_time: 0.1745 memory: 6717 grad_norm: 3.0428 loss: 1.2791 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.2791 2023/04/14 07:11:33 - mmengine - INFO - Epoch(train) [54][ 800/1879] lr: 2.0000e-03 eta: 8:59:56 time: 0.3738 data_time: 0.1757 memory: 6717 grad_norm: 2.9805 loss: 1.2171 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2171 2023/04/14 07:11:40 - mmengine - INFO - Epoch(train) [54][ 820/1879] lr: 2.0000e-03 eta: 8:59:48 time: 0.3425 data_time: 0.1223 memory: 6717 grad_norm: 3.0248 loss: 1.1359 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1359 2023/04/14 07:11:47 - mmengine - INFO - Epoch(train) [54][ 840/1879] lr: 2.0000e-03 eta: 8:59:41 time: 0.3719 data_time: 0.1402 memory: 6717 grad_norm: 3.0049 loss: 1.1890 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1890 2023/04/14 07:11:55 - mmengine - INFO - Epoch(train) [54][ 860/1879] lr: 2.0000e-03 eta: 8:59:34 time: 0.3836 data_time: 0.0857 memory: 6717 grad_norm: 3.0239 loss: 1.1019 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1019 2023/04/14 07:12:02 - mmengine - INFO - Epoch(train) [54][ 880/1879] lr: 2.0000e-03 eta: 8:59:26 time: 0.3536 data_time: 0.1409 memory: 6717 grad_norm: 3.0328 loss: 1.1973 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1973 2023/04/14 07:12:10 - mmengine - INFO - Epoch(train) [54][ 900/1879] lr: 2.0000e-03 eta: 8:59:19 time: 0.3808 data_time: 0.2108 memory: 6717 grad_norm: 2.9880 loss: 1.3027 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.3027 2023/04/14 07:12:16 - mmengine - INFO - Epoch(train) [54][ 920/1879] lr: 2.0000e-03 eta: 8:59:11 time: 0.3381 data_time: 0.1752 memory: 6717 grad_norm: 2.9965 loss: 1.3555 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3555 2023/04/14 07:12:24 - mmengine - INFO - Epoch(train) [54][ 940/1879] lr: 2.0000e-03 eta: 8:59:04 time: 0.4000 data_time: 0.2550 memory: 6717 grad_norm: 2.9680 loss: 1.1256 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1256 2023/04/14 07:12:31 - mmengine - INFO - Epoch(train) [54][ 960/1879] lr: 2.0000e-03 eta: 8:58:56 time: 0.3432 data_time: 0.2033 memory: 6717 grad_norm: 3.0388 loss: 1.1653 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1653 2023/04/14 07:12:39 - mmengine - INFO - Epoch(train) [54][ 980/1879] lr: 2.0000e-03 eta: 8:58:49 time: 0.4018 data_time: 0.2528 memory: 6717 grad_norm: 3.0138 loss: 1.1721 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1721 2023/04/14 07:12:46 - mmengine - INFO - Epoch(train) [54][1000/1879] lr: 2.0000e-03 eta: 8:58:41 time: 0.3382 data_time: 0.1947 memory: 6717 grad_norm: 2.9577 loss: 1.3728 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3728 2023/04/14 07:12:54 - mmengine - INFO - Epoch(train) [54][1020/1879] lr: 2.0000e-03 eta: 8:58:34 time: 0.3924 data_time: 0.2512 memory: 6717 grad_norm: 3.1073 loss: 1.2316 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2316 2023/04/14 07:13:00 - mmengine - INFO - Epoch(train) [54][1040/1879] lr: 2.0000e-03 eta: 8:58:26 time: 0.3155 data_time: 0.1734 memory: 6717 grad_norm: 3.0709 loss: 1.1741 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1741 2023/04/14 07:13:09 - mmengine - INFO - Epoch(train) [54][1060/1879] lr: 2.0000e-03 eta: 8:58:20 time: 0.4289 data_time: 0.2598 memory: 6717 grad_norm: 2.9942 loss: 1.2239 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2239 2023/04/14 07:13:15 - mmengine - INFO - Epoch(train) [54][1080/1879] lr: 2.0000e-03 eta: 8:58:11 time: 0.3126 data_time: 0.1511 memory: 6717 grad_norm: 3.0102 loss: 1.1633 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1633 2023/04/14 07:13:23 - mmengine - INFO - Epoch(train) [54][1100/1879] lr: 2.0000e-03 eta: 8:58:04 time: 0.3862 data_time: 0.2003 memory: 6717 grad_norm: 3.0932 loss: 1.3026 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3026 2023/04/14 07:13:30 - mmengine - INFO - Epoch(train) [54][1120/1879] lr: 2.0000e-03 eta: 8:57:56 time: 0.3447 data_time: 0.0965 memory: 6717 grad_norm: 2.9933 loss: 1.2450 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.2450 2023/04/14 07:13:38 - mmengine - INFO - Epoch(train) [54][1140/1879] lr: 2.0000e-03 eta: 8:57:49 time: 0.4000 data_time: 0.1661 memory: 6717 grad_norm: 3.0289 loss: 1.1822 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.1822 2023/04/14 07:13:45 - mmengine - INFO - Epoch(train) [54][1160/1879] lr: 2.0000e-03 eta: 8:57:42 time: 0.3692 data_time: 0.0758 memory: 6717 grad_norm: 3.0303 loss: 1.3490 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3490 2023/04/14 07:13:53 - mmengine - INFO - Epoch(train) [54][1180/1879] lr: 2.0000e-03 eta: 8:57:35 time: 0.3949 data_time: 0.0243 memory: 6717 grad_norm: 2.9896 loss: 1.1897 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1897 2023/04/14 07:14:00 - mmengine - INFO - Epoch(train) [54][1200/1879] lr: 2.0000e-03 eta: 8:57:27 time: 0.3597 data_time: 0.0142 memory: 6717 grad_norm: 3.0832 loss: 1.2269 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2269 2023/04/14 07:14:08 - mmengine - INFO - Epoch(train) [54][1220/1879] lr: 2.0000e-03 eta: 8:57:21 time: 0.3995 data_time: 0.0142 memory: 6717 grad_norm: 3.1061 loss: 1.2179 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2179 2023/04/14 07:14:15 - mmengine - INFO - Epoch(train) [54][1240/1879] lr: 2.0000e-03 eta: 8:57:13 time: 0.3385 data_time: 0.0151 memory: 6717 grad_norm: 3.0821 loss: 1.2669 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.2669 2023/04/14 07:14:22 - mmengine - INFO - Epoch(train) [54][1260/1879] lr: 2.0000e-03 eta: 8:57:05 time: 0.3554 data_time: 0.0666 memory: 6717 grad_norm: 3.0180 loss: 1.2264 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2264 2023/04/14 07:14:29 - mmengine - INFO - Epoch(train) [54][1280/1879] lr: 2.0000e-03 eta: 8:56:57 time: 0.3692 data_time: 0.0863 memory: 6717 grad_norm: 3.0567 loss: 1.3423 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3423 2023/04/14 07:14:37 - mmengine - INFO - Epoch(train) [54][1300/1879] lr: 2.0000e-03 eta: 8:56:50 time: 0.3796 data_time: 0.0364 memory: 6717 grad_norm: 2.9405 loss: 1.0264 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0264 2023/04/14 07:14:44 - mmengine - INFO - Epoch(train) [54][1320/1879] lr: 2.0000e-03 eta: 8:56:43 time: 0.3604 data_time: 0.0139 memory: 6717 grad_norm: 2.9820 loss: 1.1709 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1709 2023/04/14 07:14:53 - mmengine - INFO - Epoch(train) [54][1340/1879] lr: 2.0000e-03 eta: 8:56:36 time: 0.4136 data_time: 0.0137 memory: 6717 grad_norm: 2.9725 loss: 1.1795 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1795 2023/04/14 07:14:59 - mmengine - INFO - Epoch(train) [54][1360/1879] lr: 2.0000e-03 eta: 8:56:28 time: 0.3292 data_time: 0.0138 memory: 6717 grad_norm: 3.0112 loss: 1.1474 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.1474 2023/04/14 07:15:07 - mmengine - INFO - Epoch(train) [54][1380/1879] lr: 2.0000e-03 eta: 8:56:21 time: 0.4142 data_time: 0.0136 memory: 6717 grad_norm: 3.0670 loss: 1.2747 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2747 2023/04/14 07:15:14 - mmengine - INFO - Epoch(train) [54][1400/1879] lr: 2.0000e-03 eta: 8:56:13 time: 0.3081 data_time: 0.0143 memory: 6717 grad_norm: 3.0791 loss: 1.2312 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2312 2023/04/14 07:15:19 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 07:15:22 - mmengine - INFO - Epoch(train) [54][1420/1879] lr: 2.0000e-03 eta: 8:56:06 time: 0.4163 data_time: 0.0148 memory: 6717 grad_norm: 3.0406 loss: 1.1731 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.1731 2023/04/14 07:15:29 - mmengine - INFO - Epoch(train) [54][1440/1879] lr: 2.0000e-03 eta: 8:55:58 time: 0.3431 data_time: 0.0129 memory: 6717 grad_norm: 2.9643 loss: 1.1429 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1429 2023/04/14 07:15:37 - mmengine - INFO - Epoch(train) [54][1460/1879] lr: 2.0000e-03 eta: 8:55:52 time: 0.4143 data_time: 0.0152 memory: 6717 grad_norm: 2.9889 loss: 1.1346 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1346 2023/04/14 07:15:44 - mmengine - INFO - Epoch(train) [54][1480/1879] lr: 2.0000e-03 eta: 8:55:44 time: 0.3685 data_time: 0.0142 memory: 6717 grad_norm: 3.0826 loss: 1.1442 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1442 2023/04/14 07:15:53 - mmengine - INFO - Epoch(train) [54][1500/1879] lr: 2.0000e-03 eta: 8:55:38 time: 0.4237 data_time: 0.0146 memory: 6717 grad_norm: 3.0596 loss: 1.2036 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2036 2023/04/14 07:16:00 - mmengine - INFO - Epoch(train) [54][1520/1879] lr: 2.0000e-03 eta: 8:55:30 time: 0.3389 data_time: 0.0151 memory: 6717 grad_norm: 3.0092 loss: 1.2058 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2058 2023/04/14 07:16:09 - mmengine - INFO - Epoch(train) [54][1540/1879] lr: 2.0000e-03 eta: 8:55:24 time: 0.4451 data_time: 0.0135 memory: 6717 grad_norm: 3.0719 loss: 1.3577 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3577 2023/04/14 07:16:15 - mmengine - INFO - Epoch(train) [54][1560/1879] lr: 2.0000e-03 eta: 8:55:16 time: 0.3325 data_time: 0.0144 memory: 6717 grad_norm: 2.9918 loss: 1.2378 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2378 2023/04/14 07:16:23 - mmengine - INFO - Epoch(train) [54][1580/1879] lr: 2.0000e-03 eta: 8:55:09 time: 0.4117 data_time: 0.0137 memory: 6717 grad_norm: 3.0043 loss: 1.1423 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1423 2023/04/14 07:16:30 - mmengine - INFO - Epoch(train) [54][1600/1879] lr: 2.0000e-03 eta: 8:55:01 time: 0.3212 data_time: 0.0159 memory: 6717 grad_norm: 3.0578 loss: 1.1182 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1182 2023/04/14 07:16:38 - mmengine - INFO - Epoch(train) [54][1620/1879] lr: 2.0000e-03 eta: 8:54:54 time: 0.4124 data_time: 0.0148 memory: 6717 grad_norm: 3.0142 loss: 1.1283 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1283 2023/04/14 07:16:44 - mmengine - INFO - Epoch(train) [54][1640/1879] lr: 2.0000e-03 eta: 8:54:45 time: 0.3007 data_time: 0.0131 memory: 6717 grad_norm: 3.0672 loss: 1.2278 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2278 2023/04/14 07:16:53 - mmengine - INFO - Epoch(train) [54][1660/1879] lr: 2.0000e-03 eta: 8:54:39 time: 0.4201 data_time: 0.0138 memory: 6717 grad_norm: 3.0351 loss: 1.3603 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3603 2023/04/14 07:16:59 - mmengine - INFO - Epoch(train) [54][1680/1879] lr: 2.0000e-03 eta: 8:54:31 time: 0.3205 data_time: 0.0142 memory: 6717 grad_norm: 2.9849 loss: 1.2226 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2226 2023/04/14 07:17:07 - mmengine - INFO - Epoch(train) [54][1700/1879] lr: 2.0000e-03 eta: 8:54:24 time: 0.3952 data_time: 0.0152 memory: 6717 grad_norm: 3.0070 loss: 1.1868 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1868 2023/04/14 07:17:13 - mmengine - INFO - Epoch(train) [54][1720/1879] lr: 2.0000e-03 eta: 8:54:16 time: 0.3284 data_time: 0.0124 memory: 6717 grad_norm: 2.9862 loss: 1.1623 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1623 2023/04/14 07:17:22 - mmengine - INFO - Epoch(train) [54][1740/1879] lr: 2.0000e-03 eta: 8:54:09 time: 0.4197 data_time: 0.0156 memory: 6717 grad_norm: 3.0975 loss: 1.2061 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2061 2023/04/14 07:17:28 - mmengine - INFO - Epoch(train) [54][1760/1879] lr: 2.0000e-03 eta: 8:54:00 time: 0.3050 data_time: 0.0138 memory: 6717 grad_norm: 2.9651 loss: 1.1324 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1324 2023/04/14 07:17:36 - mmengine - INFO - Epoch(train) [54][1780/1879] lr: 2.0000e-03 eta: 8:53:54 time: 0.4045 data_time: 0.0152 memory: 6717 grad_norm: 3.0380 loss: 1.1991 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1991 2023/04/14 07:17:43 - mmengine - INFO - Epoch(train) [54][1800/1879] lr: 2.0000e-03 eta: 8:53:46 time: 0.3414 data_time: 0.0146 memory: 6717 grad_norm: 3.0780 loss: 1.3931 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3931 2023/04/14 07:17:51 - mmengine - INFO - Epoch(train) [54][1820/1879] lr: 2.0000e-03 eta: 8:53:39 time: 0.3917 data_time: 0.0156 memory: 6717 grad_norm: 3.0878 loss: 1.3378 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3378 2023/04/14 07:17:58 - mmengine - INFO - Epoch(train) [54][1840/1879] lr: 2.0000e-03 eta: 8:53:31 time: 0.3568 data_time: 0.0135 memory: 6717 grad_norm: 3.0058 loss: 1.1276 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1276 2023/04/14 07:18:05 - mmengine - INFO - Epoch(train) [54][1860/1879] lr: 2.0000e-03 eta: 8:53:24 time: 0.3796 data_time: 0.0157 memory: 6717 grad_norm: 2.9910 loss: 1.1980 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1980 2023/04/14 07:18:11 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 07:18:11 - mmengine - INFO - Epoch(train) [54][1879/1879] lr: 2.0000e-03 eta: 8:53:16 time: 0.2922 data_time: 0.0136 memory: 6717 grad_norm: 3.1107 loss: 1.3389 top1_acc: 0.4286 top5_acc: 0.7143 loss_cls: 1.3389 2023/04/14 07:18:11 - mmengine - INFO - Saving checkpoint at 54 epochs 2023/04/14 07:18:21 - mmengine - INFO - Epoch(val) [54][ 20/155] eta: 0:01:00 time: 0.4496 data_time: 0.4163 memory: 1391 2023/04/14 07:18:27 - mmengine - INFO - Epoch(val) [54][ 40/155] eta: 0:00:44 time: 0.3167 data_time: 0.2837 memory: 1391 2023/04/14 07:18:36 - mmengine - INFO - Epoch(val) [54][ 60/155] eta: 0:00:38 time: 0.4397 data_time: 0.4066 memory: 1391 2023/04/14 07:18:42 - mmengine - INFO - Epoch(val) [54][ 80/155] eta: 0:00:28 time: 0.3160 data_time: 0.2837 memory: 1391 2023/04/14 07:18:51 - mmengine - INFO - Epoch(val) [54][100/155] eta: 0:00:21 time: 0.4562 data_time: 0.4231 memory: 1391 2023/04/14 07:18:57 - mmengine - INFO - Epoch(val) [54][120/155] eta: 0:00:13 time: 0.3101 data_time: 0.2770 memory: 1391 2023/04/14 07:19:07 - mmengine - INFO - Epoch(val) [54][140/155] eta: 0:00:05 time: 0.4908 data_time: 0.4578 memory: 1391 2023/04/14 07:19:14 - mmengine - INFO - Epoch(val) [54][155/155] acc/top1: 0.6627 acc/top5: 0.8720 acc/mean1: 0.6627 data_time: 0.4182 time: 0.4500 2023/04/14 07:19:24 - mmengine - INFO - Epoch(train) [55][ 20/1879] lr: 2.0000e-03 eta: 8:53:11 time: 0.5073 data_time: 0.2301 memory: 6717 grad_norm: 3.0693 loss: 1.1267 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1267 2023/04/14 07:19:31 - mmengine - INFO - Epoch(train) [55][ 40/1879] lr: 2.0000e-03 eta: 8:53:02 time: 0.3204 data_time: 0.0452 memory: 6717 grad_norm: 2.9863 loss: 0.9986 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9986 2023/04/14 07:19:39 - mmengine - INFO - Epoch(train) [55][ 60/1879] lr: 2.0000e-03 eta: 8:52:55 time: 0.4008 data_time: 0.0151 memory: 6717 grad_norm: 3.0215 loss: 1.2919 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2919 2023/04/14 07:19:45 - mmengine - INFO - Epoch(train) [55][ 80/1879] lr: 2.0000e-03 eta: 8:52:47 time: 0.3177 data_time: 0.0131 memory: 6717 grad_norm: 3.1031 loss: 1.3125 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3125 2023/04/14 07:19:53 - mmengine - INFO - Epoch(train) [55][ 100/1879] lr: 2.0000e-03 eta: 8:52:40 time: 0.4040 data_time: 0.0387 memory: 6717 grad_norm: 2.9824 loss: 1.1765 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1765 2023/04/14 07:20:00 - mmengine - INFO - Epoch(train) [55][ 120/1879] lr: 2.0000e-03 eta: 8:52:32 time: 0.3230 data_time: 0.0231 memory: 6717 grad_norm: 2.9658 loss: 1.1849 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1849 2023/04/14 07:20:08 - mmengine - INFO - Epoch(train) [55][ 140/1879] lr: 2.0000e-03 eta: 8:52:25 time: 0.4125 data_time: 0.0155 memory: 6717 grad_norm: 3.0677 loss: 1.5051 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.5051 2023/04/14 07:20:14 - mmengine - INFO - Epoch(train) [55][ 160/1879] lr: 2.0000e-03 eta: 8:52:17 time: 0.3226 data_time: 0.0317 memory: 6717 grad_norm: 2.9565 loss: 1.2778 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2778 2023/04/14 07:20:23 - mmengine - INFO - Epoch(train) [55][ 180/1879] lr: 2.0000e-03 eta: 8:52:11 time: 0.4238 data_time: 0.1289 memory: 6717 grad_norm: 2.9686 loss: 1.2715 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2715 2023/04/14 07:20:29 - mmengine - INFO - Epoch(train) [55][ 200/1879] lr: 2.0000e-03 eta: 8:52:03 time: 0.3262 data_time: 0.0144 memory: 6717 grad_norm: 3.0022 loss: 1.1848 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1848 2023/04/14 07:20:38 - mmengine - INFO - Epoch(train) [55][ 220/1879] lr: 2.0000e-03 eta: 8:51:56 time: 0.4407 data_time: 0.0141 memory: 6717 grad_norm: 3.0459 loss: 1.1476 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1476 2023/04/14 07:20:45 - mmengine - INFO - Epoch(train) [55][ 240/1879] lr: 2.0000e-03 eta: 8:51:48 time: 0.3179 data_time: 0.0144 memory: 6717 grad_norm: 3.0928 loss: 1.2290 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2290 2023/04/14 07:20:53 - mmengine - INFO - Epoch(train) [55][ 260/1879] lr: 2.0000e-03 eta: 8:51:42 time: 0.4251 data_time: 0.0162 memory: 6717 grad_norm: 3.0018 loss: 1.1562 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1562 2023/04/14 07:21:00 - mmengine - INFO - Epoch(train) [55][ 280/1879] lr: 2.0000e-03 eta: 8:51:34 time: 0.3319 data_time: 0.0146 memory: 6717 grad_norm: 3.0583 loss: 1.3975 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3975 2023/04/14 07:21:09 - mmengine - INFO - Epoch(train) [55][ 300/1879] lr: 2.0000e-03 eta: 8:51:27 time: 0.4434 data_time: 0.0130 memory: 6717 grad_norm: 3.0179 loss: 1.1553 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.1553 2023/04/14 07:21:15 - mmengine - INFO - Epoch(train) [55][ 320/1879] lr: 2.0000e-03 eta: 8:51:19 time: 0.3295 data_time: 0.0140 memory: 6717 grad_norm: 3.0390 loss: 1.0603 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0603 2023/04/14 07:21:23 - mmengine - INFO - Epoch(train) [55][ 340/1879] lr: 2.0000e-03 eta: 8:51:13 time: 0.4098 data_time: 0.0144 memory: 6717 grad_norm: 3.0547 loss: 1.4379 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4379 2023/04/14 07:21:30 - mmengine - INFO - Epoch(train) [55][ 360/1879] lr: 2.0000e-03 eta: 8:51:04 time: 0.3232 data_time: 0.0142 memory: 6717 grad_norm: 2.9587 loss: 1.3277 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3277 2023/04/14 07:21:38 - mmengine - INFO - Epoch(train) [55][ 380/1879] lr: 2.0000e-03 eta: 8:50:58 time: 0.4229 data_time: 0.0132 memory: 6717 grad_norm: 3.1488 loss: 1.3748 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3748 2023/04/14 07:21:44 - mmengine - INFO - Epoch(train) [55][ 400/1879] lr: 2.0000e-03 eta: 8:50:49 time: 0.3043 data_time: 0.0146 memory: 6717 grad_norm: 3.0037 loss: 1.1606 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1606 2023/04/14 07:21:53 - mmengine - INFO - Epoch(train) [55][ 420/1879] lr: 2.0000e-03 eta: 8:50:43 time: 0.4178 data_time: 0.0135 memory: 6717 grad_norm: 3.0129 loss: 1.2498 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2498 2023/04/14 07:21:59 - mmengine - INFO - Epoch(train) [55][ 440/1879] lr: 2.0000e-03 eta: 8:50:34 time: 0.3032 data_time: 0.0151 memory: 6717 grad_norm: 2.9919 loss: 1.0186 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0186 2023/04/14 07:22:07 - mmengine - INFO - Epoch(train) [55][ 460/1879] lr: 2.0000e-03 eta: 8:50:27 time: 0.4088 data_time: 0.0147 memory: 6717 grad_norm: 3.0460 loss: 1.1874 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1874 2023/04/14 07:22:14 - mmengine - INFO - Epoch(train) [55][ 480/1879] lr: 2.0000e-03 eta: 8:50:19 time: 0.3315 data_time: 0.0142 memory: 6717 grad_norm: 3.0230 loss: 1.1421 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1421 2023/04/14 07:22:22 - mmengine - INFO - Epoch(train) [55][ 500/1879] lr: 2.0000e-03 eta: 8:50:13 time: 0.4098 data_time: 0.0150 memory: 6717 grad_norm: 3.0229 loss: 1.2055 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2055 2023/04/14 07:22:29 - mmengine - INFO - Epoch(train) [55][ 520/1879] lr: 2.0000e-03 eta: 8:50:05 time: 0.3446 data_time: 0.0289 memory: 6717 grad_norm: 3.1217 loss: 1.4468 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4468 2023/04/14 07:22:34 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 07:22:37 - mmengine - INFO - Epoch(train) [55][ 540/1879] lr: 2.0000e-03 eta: 8:49:58 time: 0.3990 data_time: 0.0763 memory: 6717 grad_norm: 2.9100 loss: 1.3007 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3007 2023/04/14 07:22:44 - mmengine - INFO - Epoch(train) [55][ 560/1879] lr: 2.0000e-03 eta: 8:49:50 time: 0.3343 data_time: 0.0603 memory: 6717 grad_norm: 3.0194 loss: 1.1637 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1637 2023/04/14 07:22:51 - mmengine - INFO - Epoch(train) [55][ 580/1879] lr: 2.0000e-03 eta: 8:49:42 time: 0.3710 data_time: 0.0652 memory: 6717 grad_norm: 3.0916 loss: 1.3099 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3099 2023/04/14 07:22:58 - mmengine - INFO - Epoch(train) [55][ 600/1879] lr: 2.0000e-03 eta: 8:49:35 time: 0.3397 data_time: 0.1336 memory: 6717 grad_norm: 2.9524 loss: 1.2784 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.2784 2023/04/14 07:23:05 - mmengine - INFO - Epoch(train) [55][ 620/1879] lr: 2.0000e-03 eta: 8:49:27 time: 0.3834 data_time: 0.1127 memory: 6717 grad_norm: 3.0225 loss: 1.1887 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1887 2023/04/14 07:23:13 - mmengine - INFO - Epoch(train) [55][ 640/1879] lr: 2.0000e-03 eta: 8:49:20 time: 0.3871 data_time: 0.1889 memory: 6717 grad_norm: 3.0297 loss: 1.2389 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2389 2023/04/14 07:23:20 - mmengine - INFO - Epoch(train) [55][ 660/1879] lr: 2.0000e-03 eta: 8:49:12 time: 0.3483 data_time: 0.1763 memory: 6717 grad_norm: 3.1048 loss: 1.4284 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4284 2023/04/14 07:23:28 - mmengine - INFO - Epoch(train) [55][ 680/1879] lr: 2.0000e-03 eta: 8:49:06 time: 0.4175 data_time: 0.2689 memory: 6717 grad_norm: 3.0041 loss: 1.2087 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2087 2023/04/14 07:23:35 - mmengine - INFO - Epoch(train) [55][ 700/1879] lr: 2.0000e-03 eta: 8:48:58 time: 0.3486 data_time: 0.1687 memory: 6717 grad_norm: 3.0260 loss: 1.3582 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3582 2023/04/14 07:23:43 - mmengine - INFO - Epoch(train) [55][ 720/1879] lr: 2.0000e-03 eta: 8:48:51 time: 0.3774 data_time: 0.1298 memory: 6717 grad_norm: 2.9968 loss: 1.3020 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3020 2023/04/14 07:23:51 - mmengine - INFO - Epoch(train) [55][ 740/1879] lr: 2.0000e-03 eta: 8:48:44 time: 0.3934 data_time: 0.0183 memory: 6717 grad_norm: 3.0243 loss: 1.1840 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1840 2023/04/14 07:23:57 - mmengine - INFO - Epoch(train) [55][ 760/1879] lr: 2.0000e-03 eta: 8:48:36 time: 0.3279 data_time: 0.0268 memory: 6717 grad_norm: 3.0343 loss: 1.1135 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1135 2023/04/14 07:24:04 - mmengine - INFO - Epoch(train) [55][ 780/1879] lr: 2.0000e-03 eta: 8:48:28 time: 0.3375 data_time: 0.0329 memory: 6717 grad_norm: 2.9773 loss: 1.2713 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2713 2023/04/14 07:24:12 - mmengine - INFO - Epoch(train) [55][ 800/1879] lr: 2.0000e-03 eta: 8:48:21 time: 0.4037 data_time: 0.2489 memory: 6717 grad_norm: 2.9997 loss: 1.3240 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3240 2023/04/14 07:24:19 - mmengine - INFO - Epoch(train) [55][ 820/1879] lr: 2.0000e-03 eta: 8:48:13 time: 0.3146 data_time: 0.1667 memory: 6717 grad_norm: 3.0415 loss: 1.2807 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2807 2023/04/14 07:24:26 - mmengine - INFO - Epoch(train) [55][ 840/1879] lr: 2.0000e-03 eta: 8:48:05 time: 0.3851 data_time: 0.1476 memory: 6717 grad_norm: 3.0852 loss: 1.1507 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1507 2023/04/14 07:24:34 - mmengine - INFO - Epoch(train) [55][ 860/1879] lr: 2.0000e-03 eta: 8:47:58 time: 0.3963 data_time: 0.0159 memory: 6717 grad_norm: 3.0507 loss: 1.2876 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2876 2023/04/14 07:24:41 - mmengine - INFO - Epoch(train) [55][ 880/1879] lr: 2.0000e-03 eta: 8:47:50 time: 0.3287 data_time: 0.0158 memory: 6717 grad_norm: 3.0912 loss: 1.1627 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1627 2023/04/14 07:24:49 - mmengine - INFO - Epoch(train) [55][ 900/1879] lr: 2.0000e-03 eta: 8:47:44 time: 0.4142 data_time: 0.0135 memory: 6717 grad_norm: 3.0924 loss: 1.1681 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1681 2023/04/14 07:24:56 - mmengine - INFO - Epoch(train) [55][ 920/1879] lr: 2.0000e-03 eta: 8:47:36 time: 0.3495 data_time: 0.0598 memory: 6717 grad_norm: 3.0578 loss: 1.2272 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2272 2023/04/14 07:25:04 - mmengine - INFO - Epoch(train) [55][ 940/1879] lr: 2.0000e-03 eta: 8:47:29 time: 0.3859 data_time: 0.1593 memory: 6717 grad_norm: 3.0209 loss: 1.1528 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1528 2023/04/14 07:25:11 - mmengine - INFO - Epoch(train) [55][ 960/1879] lr: 2.0000e-03 eta: 8:47:21 time: 0.3492 data_time: 0.1288 memory: 6717 grad_norm: 3.0007 loss: 1.2885 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2885 2023/04/14 07:25:19 - mmengine - INFO - Epoch(train) [55][ 980/1879] lr: 2.0000e-03 eta: 8:47:14 time: 0.4088 data_time: 0.1914 memory: 6717 grad_norm: 3.1243 loss: 1.2916 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2916 2023/04/14 07:25:25 - mmengine - INFO - Epoch(train) [55][1000/1879] lr: 2.0000e-03 eta: 8:47:06 time: 0.2989 data_time: 0.1595 memory: 6717 grad_norm: 3.0632 loss: 1.2157 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2157 2023/04/14 07:25:33 - mmengine - INFO - Epoch(train) [55][1020/1879] lr: 2.0000e-03 eta: 8:46:59 time: 0.4027 data_time: 0.1038 memory: 6717 grad_norm: 3.0540 loss: 1.2038 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2038 2023/04/14 07:25:40 - mmengine - INFO - Epoch(train) [55][1040/1879] lr: 2.0000e-03 eta: 8:46:51 time: 0.3411 data_time: 0.0571 memory: 6717 grad_norm: 3.0295 loss: 1.3846 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3846 2023/04/14 07:25:48 - mmengine - INFO - Epoch(train) [55][1060/1879] lr: 2.0000e-03 eta: 8:46:45 time: 0.4338 data_time: 0.1398 memory: 6717 grad_norm: 3.0717 loss: 1.1650 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1650 2023/04/14 07:25:55 - mmengine - INFO - Epoch(train) [55][1080/1879] lr: 2.0000e-03 eta: 8:46:37 time: 0.3363 data_time: 0.1651 memory: 6717 grad_norm: 2.9923 loss: 1.1489 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1489 2023/04/14 07:26:04 - mmengine - INFO - Epoch(train) [55][1100/1879] lr: 2.0000e-03 eta: 8:46:30 time: 0.4312 data_time: 0.2699 memory: 6717 grad_norm: 2.9814 loss: 1.2212 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2212 2023/04/14 07:26:10 - mmengine - INFO - Epoch(train) [55][1120/1879] lr: 2.0000e-03 eta: 8:46:22 time: 0.3060 data_time: 0.1464 memory: 6717 grad_norm: 2.9291 loss: 1.1284 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1284 2023/04/14 07:26:18 - mmengine - INFO - Epoch(train) [55][1140/1879] lr: 2.0000e-03 eta: 8:46:15 time: 0.4009 data_time: 0.2502 memory: 6717 grad_norm: 3.0021 loss: 1.0479 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0479 2023/04/14 07:26:25 - mmengine - INFO - Epoch(train) [55][1160/1879] lr: 2.0000e-03 eta: 8:46:07 time: 0.3434 data_time: 0.1339 memory: 6717 grad_norm: 3.0517 loss: 1.1921 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1921 2023/04/14 07:26:33 - mmengine - INFO - Epoch(train) [55][1180/1879] lr: 2.0000e-03 eta: 8:46:01 time: 0.4264 data_time: 0.2749 memory: 6717 grad_norm: 3.1205 loss: 0.9389 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9389 2023/04/14 07:26:40 - mmengine - INFO - Epoch(train) [55][1200/1879] lr: 2.0000e-03 eta: 8:45:52 time: 0.3253 data_time: 0.1374 memory: 6717 grad_norm: 3.1358 loss: 1.2612 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2612 2023/04/14 07:26:48 - mmengine - INFO - Epoch(train) [55][1220/1879] lr: 2.0000e-03 eta: 8:45:46 time: 0.4100 data_time: 0.0543 memory: 6717 grad_norm: 3.0547 loss: 1.1668 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1668 2023/04/14 07:26:55 - mmengine - INFO - Epoch(train) [55][1240/1879] lr: 2.0000e-03 eta: 8:45:38 time: 0.3316 data_time: 0.0726 memory: 6717 grad_norm: 3.0319 loss: 1.2640 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2640 2023/04/14 07:27:03 - mmengine - INFO - Epoch(train) [55][1260/1879] lr: 2.0000e-03 eta: 8:45:31 time: 0.4169 data_time: 0.2319 memory: 6717 grad_norm: 3.0307 loss: 1.1885 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1885 2023/04/14 07:27:10 - mmengine - INFO - Epoch(train) [55][1280/1879] lr: 2.0000e-03 eta: 8:45:23 time: 0.3262 data_time: 0.1694 memory: 6717 grad_norm: 3.0680 loss: 1.1833 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1833 2023/04/14 07:27:18 - mmengine - INFO - Epoch(train) [55][1300/1879] lr: 2.0000e-03 eta: 8:45:16 time: 0.3983 data_time: 0.2582 memory: 6717 grad_norm: 2.9144 loss: 1.2536 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2536 2023/04/14 07:27:24 - mmengine - INFO - Epoch(train) [55][1320/1879] lr: 2.0000e-03 eta: 8:45:08 time: 0.3191 data_time: 0.1653 memory: 6717 grad_norm: 3.0662 loss: 1.2210 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2210 2023/04/14 07:27:32 - mmengine - INFO - Epoch(train) [55][1340/1879] lr: 2.0000e-03 eta: 8:45:01 time: 0.4064 data_time: 0.2234 memory: 6717 grad_norm: 3.0559 loss: 1.4504 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4504 2023/04/14 07:27:38 - mmengine - INFO - Epoch(train) [55][1360/1879] lr: 2.0000e-03 eta: 8:44:52 time: 0.3109 data_time: 0.1192 memory: 6717 grad_norm: 3.0609 loss: 1.2712 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2712 2023/04/14 07:27:47 - mmengine - INFO - Epoch(train) [55][1380/1879] lr: 2.0000e-03 eta: 8:44:46 time: 0.4373 data_time: 0.2161 memory: 6717 grad_norm: 2.9779 loss: 1.1899 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1899 2023/04/14 07:27:54 - mmengine - INFO - Epoch(train) [55][1400/1879] lr: 2.0000e-03 eta: 8:44:38 time: 0.3298 data_time: 0.1469 memory: 6717 grad_norm: 3.0551 loss: 1.3558 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3558 2023/04/14 07:28:02 - mmengine - INFO - Epoch(train) [55][1420/1879] lr: 2.0000e-03 eta: 8:44:31 time: 0.4056 data_time: 0.1106 memory: 6717 grad_norm: 3.0484 loss: 1.1442 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1442 2023/04/14 07:28:08 - mmengine - INFO - Epoch(train) [55][1440/1879] lr: 2.0000e-03 eta: 8:44:23 time: 0.3023 data_time: 0.0689 memory: 6717 grad_norm: 3.1290 loss: 1.2776 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2776 2023/04/14 07:28:16 - mmengine - INFO - Epoch(train) [55][1460/1879] lr: 2.0000e-03 eta: 8:44:16 time: 0.4106 data_time: 0.1058 memory: 6717 grad_norm: 3.0961 loss: 1.3172 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3172 2023/04/14 07:28:22 - mmengine - INFO - Epoch(train) [55][1480/1879] lr: 2.0000e-03 eta: 8:44:07 time: 0.2842 data_time: 0.0647 memory: 6717 grad_norm: 2.9839 loss: 1.2674 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2674 2023/04/14 07:28:30 - mmengine - INFO - Epoch(train) [55][1500/1879] lr: 2.0000e-03 eta: 8:44:01 time: 0.4241 data_time: 0.0651 memory: 6717 grad_norm: 2.9886 loss: 1.2569 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2569 2023/04/14 07:28:37 - mmengine - INFO - Epoch(train) [55][1520/1879] lr: 2.0000e-03 eta: 8:43:53 time: 0.3405 data_time: 0.0146 memory: 6717 grad_norm: 3.1003 loss: 1.3012 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3012 2023/04/14 07:28:43 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 07:28:45 - mmengine - INFO - Epoch(train) [55][1540/1879] lr: 2.0000e-03 eta: 8:43:46 time: 0.4033 data_time: 0.0149 memory: 6717 grad_norm: 3.0536 loss: 1.2864 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2864 2023/04/14 07:28:52 - mmengine - INFO - Epoch(train) [55][1560/1879] lr: 2.0000e-03 eta: 8:43:38 time: 0.3515 data_time: 0.0122 memory: 6717 grad_norm: 3.0576 loss: 1.3586 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3586 2023/04/14 07:29:01 - mmengine - INFO - Epoch(train) [55][1580/1879] lr: 2.0000e-03 eta: 8:43:32 time: 0.4259 data_time: 0.0154 memory: 6717 grad_norm: 2.9519 loss: 1.2120 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.2120 2023/04/14 07:29:07 - mmengine - INFO - Epoch(train) [55][1600/1879] lr: 2.0000e-03 eta: 8:43:24 time: 0.3274 data_time: 0.0130 memory: 6717 grad_norm: 3.0525 loss: 1.4792 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4792 2023/04/14 07:29:15 - mmengine - INFO - Epoch(train) [55][1620/1879] lr: 2.0000e-03 eta: 8:43:17 time: 0.4035 data_time: 0.0142 memory: 6717 grad_norm: 3.0271 loss: 1.2653 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2653 2023/04/14 07:29:22 - mmengine - INFO - Epoch(train) [55][1640/1879] lr: 2.0000e-03 eta: 8:43:09 time: 0.3179 data_time: 0.0136 memory: 6717 grad_norm: 3.0663 loss: 1.4543 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4543 2023/04/14 07:29:30 - mmengine - INFO - Epoch(train) [55][1660/1879] lr: 2.0000e-03 eta: 8:43:02 time: 0.4046 data_time: 0.0143 memory: 6717 grad_norm: 3.0141 loss: 1.0975 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0975 2023/04/14 07:29:36 - mmengine - INFO - Epoch(train) [55][1680/1879] lr: 2.0000e-03 eta: 8:42:54 time: 0.3308 data_time: 0.0148 memory: 6717 grad_norm: 3.0031 loss: 1.2347 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2347 2023/04/14 07:29:45 - mmengine - INFO - Epoch(train) [55][1700/1879] lr: 2.0000e-03 eta: 8:42:47 time: 0.4177 data_time: 0.0259 memory: 6717 grad_norm: 3.0994 loss: 1.4721 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4721 2023/04/14 07:29:51 - mmengine - INFO - Epoch(train) [55][1720/1879] lr: 2.0000e-03 eta: 8:42:39 time: 0.3155 data_time: 0.0130 memory: 6717 grad_norm: 3.1160 loss: 1.3355 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3355 2023/04/14 07:29:59 - mmengine - INFO - Epoch(train) [55][1740/1879] lr: 2.0000e-03 eta: 8:42:32 time: 0.4050 data_time: 0.0137 memory: 6717 grad_norm: 3.1212 loss: 1.2988 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2988 2023/04/14 07:30:06 - mmengine - INFO - Epoch(train) [55][1760/1879] lr: 2.0000e-03 eta: 8:42:24 time: 0.3288 data_time: 0.0151 memory: 6717 grad_norm: 3.0453 loss: 1.2350 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2350 2023/04/14 07:30:14 - mmengine - INFO - Epoch(train) [55][1780/1879] lr: 2.0000e-03 eta: 8:42:17 time: 0.4042 data_time: 0.0148 memory: 6717 grad_norm: 3.1087 loss: 1.2155 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2155 2023/04/14 07:30:20 - mmengine - INFO - Epoch(train) [55][1800/1879] lr: 2.0000e-03 eta: 8:42:09 time: 0.3226 data_time: 0.0149 memory: 6717 grad_norm: 3.0204 loss: 1.2953 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2953 2023/04/14 07:30:28 - mmengine - INFO - Epoch(train) [55][1820/1879] lr: 2.0000e-03 eta: 8:42:02 time: 0.4111 data_time: 0.0139 memory: 6717 grad_norm: 3.0470 loss: 1.0764 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0764 2023/04/14 07:30:35 - mmengine - INFO - Epoch(train) [55][1840/1879] lr: 2.0000e-03 eta: 8:41:54 time: 0.3285 data_time: 0.0146 memory: 6717 grad_norm: 2.9988 loss: 1.2691 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2691 2023/04/14 07:30:43 - mmengine - INFO - Epoch(train) [55][1860/1879] lr: 2.0000e-03 eta: 8:41:47 time: 0.4036 data_time: 0.0146 memory: 6717 grad_norm: 3.0765 loss: 1.3585 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3585 2023/04/14 07:30:49 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 07:30:49 - mmengine - INFO - Epoch(train) [55][1879/1879] lr: 2.0000e-03 eta: 8:41:39 time: 0.2926 data_time: 0.0125 memory: 6717 grad_norm: 3.0797 loss: 1.0844 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.0844 2023/04/14 07:30:58 - mmengine - INFO - Epoch(val) [55][ 20/155] eta: 0:01:01 time: 0.4578 data_time: 0.4248 memory: 1391 2023/04/14 07:31:04 - mmengine - INFO - Epoch(val) [55][ 40/155] eta: 0:00:44 time: 0.3101 data_time: 0.2777 memory: 1391 2023/04/14 07:31:13 - mmengine - INFO - Epoch(val) [55][ 60/155] eta: 0:00:38 time: 0.4352 data_time: 0.4018 memory: 1391 2023/04/14 07:31:19 - mmengine - INFO - Epoch(val) [55][ 80/155] eta: 0:00:28 time: 0.3123 data_time: 0.2796 memory: 1391 2023/04/14 07:31:28 - mmengine - INFO - Epoch(val) [55][100/155] eta: 0:00:21 time: 0.4238 data_time: 0.3907 memory: 1391 2023/04/14 07:31:34 - mmengine - INFO - Epoch(val) [55][120/155] eta: 0:00:13 time: 0.3362 data_time: 0.3030 memory: 1391 2023/04/14 07:31:44 - mmengine - INFO - Epoch(val) [55][140/155] eta: 0:00:05 time: 0.4844 data_time: 0.4510 memory: 1391 2023/04/14 07:31:51 - mmengine - INFO - Epoch(val) [55][155/155] acc/top1: 0.6620 acc/top5: 0.8705 acc/mean1: 0.6620 data_time: 0.4188 time: 0.4505 2023/04/14 07:32:02 - mmengine - INFO - Epoch(train) [56][ 20/1879] lr: 2.0000e-03 eta: 8:41:34 time: 0.5153 data_time: 0.1602 memory: 6717 grad_norm: 2.9642 loss: 1.2934 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2934 2023/04/14 07:32:08 - mmengine - INFO - Epoch(train) [56][ 40/1879] lr: 2.0000e-03 eta: 8:41:26 time: 0.3116 data_time: 0.0256 memory: 6717 grad_norm: 3.0323 loss: 1.1781 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1781 2023/04/14 07:32:16 - mmengine - INFO - Epoch(train) [56][ 60/1879] lr: 2.0000e-03 eta: 8:41:19 time: 0.4254 data_time: 0.0302 memory: 6717 grad_norm: 3.0777 loss: 1.1708 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1708 2023/04/14 07:32:23 - mmengine - INFO - Epoch(train) [56][ 80/1879] lr: 2.0000e-03 eta: 8:41:11 time: 0.3336 data_time: 0.0239 memory: 6717 grad_norm: 3.0703 loss: 1.3122 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3122 2023/04/14 07:32:31 - mmengine - INFO - Epoch(train) [56][ 100/1879] lr: 2.0000e-03 eta: 8:41:05 time: 0.4271 data_time: 0.0278 memory: 6717 grad_norm: 3.0244 loss: 1.3112 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3112 2023/04/14 07:32:38 - mmengine - INFO - Epoch(train) [56][ 120/1879] lr: 2.0000e-03 eta: 8:40:56 time: 0.3022 data_time: 0.0123 memory: 6717 grad_norm: 2.9940 loss: 1.1342 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1342 2023/04/14 07:32:46 - mmengine - INFO - Epoch(train) [56][ 140/1879] lr: 2.0000e-03 eta: 8:40:49 time: 0.4118 data_time: 0.0162 memory: 6717 grad_norm: 3.0086 loss: 1.1794 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1794 2023/04/14 07:32:53 - mmengine - INFO - Epoch(train) [56][ 160/1879] lr: 2.0000e-03 eta: 8:40:41 time: 0.3378 data_time: 0.0724 memory: 6717 grad_norm: 3.0218 loss: 1.3227 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3227 2023/04/14 07:33:00 - mmengine - INFO - Epoch(train) [56][ 180/1879] lr: 2.0000e-03 eta: 8:40:34 time: 0.3810 data_time: 0.0547 memory: 6717 grad_norm: 3.0468 loss: 1.2237 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2237 2023/04/14 07:33:07 - mmengine - INFO - Epoch(train) [56][ 200/1879] lr: 2.0000e-03 eta: 8:40:26 time: 0.3385 data_time: 0.0237 memory: 6717 grad_norm: 3.0535 loss: 1.4129 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.4129 2023/04/14 07:33:15 - mmengine - INFO - Epoch(train) [56][ 220/1879] lr: 2.0000e-03 eta: 8:40:20 time: 0.4254 data_time: 0.0138 memory: 6717 grad_norm: 2.9747 loss: 1.2683 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2683 2023/04/14 07:33:21 - mmengine - INFO - Epoch(train) [56][ 240/1879] lr: 2.0000e-03 eta: 8:40:11 time: 0.2955 data_time: 0.0131 memory: 6717 grad_norm: 3.1127 loss: 1.3146 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3146 2023/04/14 07:33:30 - mmengine - INFO - Epoch(train) [56][ 260/1879] lr: 2.0000e-03 eta: 8:40:05 time: 0.4472 data_time: 0.0147 memory: 6717 grad_norm: 3.0455 loss: 1.3330 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3330 2023/04/14 07:33:37 - mmengine - INFO - Epoch(train) [56][ 280/1879] lr: 2.0000e-03 eta: 8:39:57 time: 0.3135 data_time: 0.0126 memory: 6717 grad_norm: 3.0486 loss: 1.1730 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1730 2023/04/14 07:33:45 - mmengine - INFO - Epoch(train) [56][ 300/1879] lr: 2.0000e-03 eta: 8:39:50 time: 0.4170 data_time: 0.0160 memory: 6717 grad_norm: 2.9928 loss: 1.1521 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1521 2023/04/14 07:33:51 - mmengine - INFO - Epoch(train) [56][ 320/1879] lr: 2.0000e-03 eta: 8:39:42 time: 0.3247 data_time: 0.0152 memory: 6717 grad_norm: 3.0007 loss: 1.1447 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1447 2023/04/14 07:33:59 - mmengine - INFO - Epoch(train) [56][ 340/1879] lr: 2.0000e-03 eta: 8:39:35 time: 0.3934 data_time: 0.0252 memory: 6717 grad_norm: 3.0332 loss: 1.2691 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2691 2023/04/14 07:34:06 - mmengine - INFO - Epoch(train) [56][ 360/1879] lr: 2.0000e-03 eta: 8:39:27 time: 0.3286 data_time: 0.0425 memory: 6717 grad_norm: 3.0419 loss: 1.1869 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1869 2023/04/14 07:34:14 - mmengine - INFO - Epoch(train) [56][ 380/1879] lr: 2.0000e-03 eta: 8:39:20 time: 0.4218 data_time: 0.0332 memory: 6717 grad_norm: 3.0623 loss: 1.3764 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3764 2023/04/14 07:34:21 - mmengine - INFO - Epoch(train) [56][ 400/1879] lr: 2.0000e-03 eta: 8:39:12 time: 0.3380 data_time: 0.0144 memory: 6717 grad_norm: 3.1153 loss: 1.3238 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3238 2023/04/14 07:34:29 - mmengine - INFO - Epoch(train) [56][ 420/1879] lr: 2.0000e-03 eta: 8:39:06 time: 0.4187 data_time: 0.0129 memory: 6717 grad_norm: 3.0320 loss: 0.9503 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9503 2023/04/14 07:34:36 - mmengine - INFO - Epoch(train) [56][ 440/1879] lr: 2.0000e-03 eta: 8:38:58 time: 0.3496 data_time: 0.0140 memory: 6717 grad_norm: 2.9677 loss: 1.1014 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1014 2023/04/14 07:34:44 - mmengine - INFO - Epoch(train) [56][ 460/1879] lr: 2.0000e-03 eta: 8:38:51 time: 0.3977 data_time: 0.0515 memory: 6717 grad_norm: 3.0282 loss: 1.2932 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2932 2023/04/14 07:34:51 - mmengine - INFO - Epoch(train) [56][ 480/1879] lr: 2.0000e-03 eta: 8:38:43 time: 0.3260 data_time: 0.0393 memory: 6717 grad_norm: 3.0536 loss: 1.2564 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2564 2023/04/14 07:34:59 - mmengine - INFO - Epoch(train) [56][ 500/1879] lr: 2.0000e-03 eta: 8:38:36 time: 0.3956 data_time: 0.0566 memory: 6717 grad_norm: 3.0620 loss: 1.0970 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0970 2023/04/14 07:35:05 - mmengine - INFO - Epoch(train) [56][ 520/1879] lr: 2.0000e-03 eta: 8:38:28 time: 0.3124 data_time: 0.1416 memory: 6717 grad_norm: 3.1498 loss: 1.1332 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1332 2023/04/14 07:35:14 - mmengine - INFO - Epoch(train) [56][ 540/1879] lr: 2.0000e-03 eta: 8:38:21 time: 0.4354 data_time: 0.2810 memory: 6717 grad_norm: 3.1399 loss: 1.4589 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4589 2023/04/14 07:35:20 - mmengine - INFO - Epoch(train) [56][ 560/1879] lr: 2.0000e-03 eta: 8:38:13 time: 0.3007 data_time: 0.1043 memory: 6717 grad_norm: 2.9992 loss: 1.1705 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1705 2023/04/14 07:35:28 - mmengine - INFO - Epoch(train) [56][ 580/1879] lr: 2.0000e-03 eta: 8:38:06 time: 0.4180 data_time: 0.1849 memory: 6717 grad_norm: 3.0405 loss: 1.1706 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1706 2023/04/14 07:35:34 - mmengine - INFO - Epoch(train) [56][ 600/1879] lr: 2.0000e-03 eta: 8:37:58 time: 0.3056 data_time: 0.1414 memory: 6717 grad_norm: 3.1619 loss: 1.2752 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2752 2023/04/14 07:35:42 - mmengine - INFO - Epoch(train) [56][ 620/1879] lr: 2.0000e-03 eta: 8:37:50 time: 0.3894 data_time: 0.2126 memory: 6717 grad_norm: 3.0709 loss: 1.4019 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4019 2023/04/14 07:35:50 - mmengine - INFO - Epoch(train) [56][ 640/1879] lr: 2.0000e-03 eta: 8:37:43 time: 0.3840 data_time: 0.0821 memory: 6717 grad_norm: 2.9553 loss: 1.2820 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2820 2023/04/14 07:35:55 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 07:35:57 - mmengine - INFO - Epoch(train) [56][ 660/1879] lr: 2.0000e-03 eta: 8:37:35 time: 0.3389 data_time: 0.1330 memory: 6717 grad_norm: 3.0234 loss: 1.3267 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.3267 2023/04/14 07:36:04 - mmengine - INFO - Epoch(train) [56][ 680/1879] lr: 2.0000e-03 eta: 8:37:28 time: 0.3704 data_time: 0.1207 memory: 6717 grad_norm: 3.0956 loss: 1.0989 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0989 2023/04/14 07:36:11 - mmengine - INFO - Epoch(train) [56][ 700/1879] lr: 2.0000e-03 eta: 8:37:20 time: 0.3623 data_time: 0.1858 memory: 6717 grad_norm: 3.0749 loss: 1.3506 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3506 2023/04/14 07:36:19 - mmengine - INFO - Epoch(train) [56][ 720/1879] lr: 2.0000e-03 eta: 8:37:13 time: 0.3773 data_time: 0.1791 memory: 6717 grad_norm: 3.0887 loss: 1.3491 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3491 2023/04/14 07:36:26 - mmengine - INFO - Epoch(train) [56][ 740/1879] lr: 2.0000e-03 eta: 8:37:05 time: 0.3475 data_time: 0.1429 memory: 6717 grad_norm: 3.0163 loss: 1.0926 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0926 2023/04/14 07:36:34 - mmengine - INFO - Epoch(train) [56][ 760/1879] lr: 2.0000e-03 eta: 8:36:59 time: 0.4141 data_time: 0.0610 memory: 6717 grad_norm: 3.1528 loss: 1.3612 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3612 2023/04/14 07:36:41 - mmengine - INFO - Epoch(train) [56][ 780/1879] lr: 2.0000e-03 eta: 8:36:51 time: 0.3386 data_time: 0.0648 memory: 6717 grad_norm: 3.0972 loss: 1.3385 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.3385 2023/04/14 07:36:49 - mmengine - INFO - Epoch(train) [56][ 800/1879] lr: 2.0000e-03 eta: 8:36:44 time: 0.3966 data_time: 0.0226 memory: 6717 grad_norm: 3.0389 loss: 1.3361 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3361 2023/04/14 07:36:56 - mmengine - INFO - Epoch(train) [56][ 820/1879] lr: 2.0000e-03 eta: 8:36:36 time: 0.3414 data_time: 0.0209 memory: 6717 grad_norm: 3.0264 loss: 1.2642 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2642 2023/04/14 07:37:04 - mmengine - INFO - Epoch(train) [56][ 840/1879] lr: 2.0000e-03 eta: 8:36:29 time: 0.4055 data_time: 0.0137 memory: 6717 grad_norm: 3.0849 loss: 1.1405 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1405 2023/04/14 07:37:10 - mmengine - INFO - Epoch(train) [56][ 860/1879] lr: 2.0000e-03 eta: 8:36:21 time: 0.3256 data_time: 0.0163 memory: 6717 grad_norm: 2.9458 loss: 1.0876 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0876 2023/04/14 07:37:18 - mmengine - INFO - Epoch(train) [56][ 880/1879] lr: 2.0000e-03 eta: 8:36:14 time: 0.3814 data_time: 0.0454 memory: 6717 grad_norm: 3.1074 loss: 1.3774 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3774 2023/04/14 07:37:25 - mmengine - INFO - Epoch(train) [56][ 900/1879] lr: 2.0000e-03 eta: 8:36:06 time: 0.3612 data_time: 0.1588 memory: 6717 grad_norm: 2.9879 loss: 1.1796 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1796 2023/04/14 07:37:32 - mmengine - INFO - Epoch(train) [56][ 920/1879] lr: 2.0000e-03 eta: 8:35:58 time: 0.3502 data_time: 0.0996 memory: 6717 grad_norm: 3.0736 loss: 1.4119 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4119 2023/04/14 07:37:40 - mmengine - INFO - Epoch(train) [56][ 940/1879] lr: 2.0000e-03 eta: 8:35:51 time: 0.3887 data_time: 0.1880 memory: 6717 grad_norm: 3.0236 loss: 1.2512 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2512 2023/04/14 07:37:47 - mmengine - INFO - Epoch(train) [56][ 960/1879] lr: 2.0000e-03 eta: 8:35:44 time: 0.3643 data_time: 0.1196 memory: 6717 grad_norm: 3.0906 loss: 1.3698 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3698 2023/04/14 07:37:54 - mmengine - INFO - Epoch(train) [56][ 980/1879] lr: 2.0000e-03 eta: 8:35:36 time: 0.3545 data_time: 0.0944 memory: 6717 grad_norm: 3.0874 loss: 1.1809 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1809 2023/04/14 07:38:02 - mmengine - INFO - Epoch(train) [56][1000/1879] lr: 2.0000e-03 eta: 8:35:29 time: 0.3840 data_time: 0.0942 memory: 6717 grad_norm: 3.0164 loss: 1.1935 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1935 2023/04/14 07:38:09 - mmengine - INFO - Epoch(train) [56][1020/1879] lr: 2.0000e-03 eta: 8:35:21 time: 0.3508 data_time: 0.0750 memory: 6717 grad_norm: 2.9595 loss: 1.1690 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1690 2023/04/14 07:38:17 - mmengine - INFO - Epoch(train) [56][1040/1879] lr: 2.0000e-03 eta: 8:35:14 time: 0.3996 data_time: 0.0201 memory: 6717 grad_norm: 3.0995 loss: 1.2264 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2264 2023/04/14 07:38:24 - mmengine - INFO - Epoch(train) [56][1060/1879] lr: 2.0000e-03 eta: 8:35:07 time: 0.3649 data_time: 0.0149 memory: 6717 grad_norm: 3.0612 loss: 1.4080 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4080 2023/04/14 07:38:32 - mmengine - INFO - Epoch(train) [56][1080/1879] lr: 2.0000e-03 eta: 8:34:59 time: 0.3675 data_time: 0.0136 memory: 6717 grad_norm: 3.0553 loss: 1.1264 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1264 2023/04/14 07:38:39 - mmengine - INFO - Epoch(train) [56][1100/1879] lr: 2.0000e-03 eta: 8:34:52 time: 0.3577 data_time: 0.0152 memory: 6717 grad_norm: 3.0363 loss: 1.1572 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1572 2023/04/14 07:38:45 - mmengine - INFO - Epoch(train) [56][1120/1879] lr: 2.0000e-03 eta: 8:34:44 time: 0.3370 data_time: 0.0142 memory: 6717 grad_norm: 2.9978 loss: 1.3225 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3225 2023/04/14 07:38:54 - mmengine - INFO - Epoch(train) [56][1140/1879] lr: 2.0000e-03 eta: 8:34:37 time: 0.4224 data_time: 0.0171 memory: 6717 grad_norm: 2.9746 loss: 1.2460 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2460 2023/04/14 07:39:01 - mmengine - INFO - Epoch(train) [56][1160/1879] lr: 2.0000e-03 eta: 8:34:29 time: 0.3326 data_time: 0.0150 memory: 6717 grad_norm: 3.0379 loss: 1.1341 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1341 2023/04/14 07:39:09 - mmengine - INFO - Epoch(train) [56][1180/1879] lr: 2.0000e-03 eta: 8:34:23 time: 0.4286 data_time: 0.0145 memory: 6717 grad_norm: 3.0404 loss: 1.2558 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2558 2023/04/14 07:39:16 - mmengine - INFO - Epoch(train) [56][1200/1879] lr: 2.0000e-03 eta: 8:34:15 time: 0.3282 data_time: 0.0151 memory: 6717 grad_norm: 3.0336 loss: 1.2644 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2644 2023/04/14 07:39:23 - mmengine - INFO - Epoch(train) [56][1220/1879] lr: 2.0000e-03 eta: 8:34:08 time: 0.3786 data_time: 0.0142 memory: 6717 grad_norm: 3.1126 loss: 1.2037 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2037 2023/04/14 07:39:31 - mmengine - INFO - Epoch(train) [56][1240/1879] lr: 2.0000e-03 eta: 8:34:00 time: 0.3689 data_time: 0.0140 memory: 6717 grad_norm: 3.0733 loss: 1.3608 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3608 2023/04/14 07:39:38 - mmengine - INFO - Epoch(train) [56][1260/1879] lr: 2.0000e-03 eta: 8:33:53 time: 0.3758 data_time: 0.0141 memory: 6717 grad_norm: 3.1021 loss: 1.1924 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1924 2023/04/14 07:39:45 - mmengine - INFO - Epoch(train) [56][1280/1879] lr: 2.0000e-03 eta: 8:33:45 time: 0.3526 data_time: 0.0155 memory: 6717 grad_norm: 3.0082 loss: 1.1626 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.1626 2023/04/14 07:39:53 - mmengine - INFO - Epoch(train) [56][1300/1879] lr: 2.0000e-03 eta: 8:33:38 time: 0.4003 data_time: 0.0142 memory: 6717 grad_norm: 3.0924 loss: 1.2950 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2950 2023/04/14 07:40:00 - mmengine - INFO - Epoch(train) [56][1320/1879] lr: 2.0000e-03 eta: 8:33:30 time: 0.3382 data_time: 0.0151 memory: 6717 grad_norm: 3.0269 loss: 1.2965 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2965 2023/04/14 07:40:08 - mmengine - INFO - Epoch(train) [56][1340/1879] lr: 2.0000e-03 eta: 8:33:23 time: 0.3834 data_time: 0.0132 memory: 6717 grad_norm: 3.0023 loss: 1.1787 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.1787 2023/04/14 07:40:15 - mmengine - INFO - Epoch(train) [56][1360/1879] lr: 2.0000e-03 eta: 8:33:15 time: 0.3449 data_time: 0.0158 memory: 6717 grad_norm: 3.0920 loss: 1.1099 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1099 2023/04/14 07:40:22 - mmengine - INFO - Epoch(train) [56][1380/1879] lr: 2.0000e-03 eta: 8:33:08 time: 0.3787 data_time: 0.0315 memory: 6717 grad_norm: 2.9690 loss: 1.1838 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1838 2023/04/14 07:40:29 - mmengine - INFO - Epoch(train) [56][1400/1879] lr: 2.0000e-03 eta: 8:33:00 time: 0.3196 data_time: 0.0136 memory: 6717 grad_norm: 3.0585 loss: 1.1447 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1447 2023/04/14 07:40:36 - mmengine - INFO - Epoch(train) [56][1420/1879] lr: 2.0000e-03 eta: 8:32:52 time: 0.3751 data_time: 0.0631 memory: 6717 grad_norm: 3.0854 loss: 1.3503 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3503 2023/04/14 07:40:44 - mmengine - INFO - Epoch(train) [56][1440/1879] lr: 2.0000e-03 eta: 8:32:46 time: 0.4118 data_time: 0.0137 memory: 6717 grad_norm: 3.0188 loss: 1.0955 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.0955 2023/04/14 07:40:51 - mmengine - INFO - Epoch(train) [56][1460/1879] lr: 2.0000e-03 eta: 8:32:37 time: 0.3138 data_time: 0.0322 memory: 6717 grad_norm: 2.9857 loss: 1.1766 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1766 2023/04/14 07:40:58 - mmengine - INFO - Epoch(train) [56][1480/1879] lr: 2.0000e-03 eta: 8:32:30 time: 0.3945 data_time: 0.1428 memory: 6717 grad_norm: 3.0236 loss: 1.2320 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.2320 2023/04/14 07:41:06 - mmengine - INFO - Epoch(train) [56][1500/1879] lr: 2.0000e-03 eta: 8:32:23 time: 0.3826 data_time: 0.1885 memory: 6717 grad_norm: 3.0757 loss: 1.1831 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1831 2023/04/14 07:41:13 - mmengine - INFO - Epoch(train) [56][1520/1879] lr: 2.0000e-03 eta: 8:32:15 time: 0.3276 data_time: 0.0869 memory: 6717 grad_norm: 3.0981 loss: 1.2804 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2804 2023/04/14 07:41:21 - mmengine - INFO - Epoch(train) [56][1540/1879] lr: 2.0000e-03 eta: 8:32:08 time: 0.4114 data_time: 0.1906 memory: 6717 grad_norm: 3.1468 loss: 1.4967 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4967 2023/04/14 07:41:28 - mmengine - INFO - Epoch(train) [56][1560/1879] lr: 2.0000e-03 eta: 8:32:01 time: 0.3525 data_time: 0.1702 memory: 6717 grad_norm: 2.9953 loss: 1.2084 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2084 2023/04/14 07:41:36 - mmengine - INFO - Epoch(train) [56][1580/1879] lr: 2.0000e-03 eta: 8:31:54 time: 0.4003 data_time: 0.2582 memory: 6717 grad_norm: 2.9626 loss: 1.2200 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2200 2023/04/14 07:41:43 - mmengine - INFO - Epoch(train) [56][1600/1879] lr: 2.0000e-03 eta: 8:31:46 time: 0.3391 data_time: 0.1704 memory: 6717 grad_norm: 3.0281 loss: 1.1426 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1426 2023/04/14 07:41:51 - mmengine - INFO - Epoch(train) [56][1620/1879] lr: 2.0000e-03 eta: 8:31:39 time: 0.4014 data_time: 0.2355 memory: 6717 grad_norm: 3.0700 loss: 1.3445 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3445 2023/04/14 07:41:58 - mmengine - INFO - Epoch(train) [56][1640/1879] lr: 2.0000e-03 eta: 8:31:31 time: 0.3484 data_time: 0.1300 memory: 6717 grad_norm: 3.1299 loss: 1.1543 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1543 2023/04/14 07:42:04 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 07:42:06 - mmengine - INFO - Epoch(train) [56][1660/1879] lr: 2.0000e-03 eta: 8:31:24 time: 0.3905 data_time: 0.2447 memory: 6717 grad_norm: 3.0127 loss: 1.1059 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1059 2023/04/14 07:42:13 - mmengine - INFO - Epoch(train) [56][1680/1879] lr: 2.0000e-03 eta: 8:31:17 time: 0.3597 data_time: 0.1160 memory: 6717 grad_norm: 3.0231 loss: 1.2003 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2003 2023/04/14 07:42:20 - mmengine - INFO - Epoch(train) [56][1700/1879] lr: 2.0000e-03 eta: 8:31:09 time: 0.3504 data_time: 0.1221 memory: 6717 grad_norm: 3.0912 loss: 1.2698 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2698 2023/04/14 07:42:28 - mmengine - INFO - Epoch(train) [56][1720/1879] lr: 2.0000e-03 eta: 8:31:02 time: 0.3960 data_time: 0.0847 memory: 6717 grad_norm: 3.0942 loss: 1.1582 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1582 2023/04/14 07:42:34 - mmengine - INFO - Epoch(train) [56][1740/1879] lr: 2.0000e-03 eta: 8:30:54 time: 0.3363 data_time: 0.1269 memory: 6717 grad_norm: 3.0178 loss: 1.3248 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3248 2023/04/14 07:42:43 - mmengine - INFO - Epoch(train) [56][1760/1879] lr: 2.0000e-03 eta: 8:30:47 time: 0.4172 data_time: 0.0176 memory: 6717 grad_norm: 3.0287 loss: 1.2218 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2218 2023/04/14 07:42:49 - mmengine - INFO - Epoch(train) [56][1780/1879] lr: 2.0000e-03 eta: 8:30:39 time: 0.3287 data_time: 0.0160 memory: 6717 grad_norm: 3.0461 loss: 1.1310 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1310 2023/04/14 07:42:57 - mmengine - INFO - Epoch(train) [56][1800/1879] lr: 2.0000e-03 eta: 8:30:32 time: 0.3895 data_time: 0.0150 memory: 6717 grad_norm: 3.1146 loss: 1.1966 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1966 2023/04/14 07:43:04 - mmengine - INFO - Epoch(train) [56][1820/1879] lr: 2.0000e-03 eta: 8:30:24 time: 0.3479 data_time: 0.0143 memory: 6717 grad_norm: 3.0850 loss: 1.2668 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2668 2023/04/14 07:43:12 - mmengine - INFO - Epoch(train) [56][1840/1879] lr: 2.0000e-03 eta: 8:30:18 time: 0.4054 data_time: 0.0126 memory: 6717 grad_norm: 3.0854 loss: 1.2326 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2326 2023/04/14 07:43:18 - mmengine - INFO - Epoch(train) [56][1860/1879] lr: 2.0000e-03 eta: 8:30:09 time: 0.3079 data_time: 0.0156 memory: 6717 grad_norm: 3.1274 loss: 1.2619 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2619 2023/04/14 07:43:24 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 07:43:24 - mmengine - INFO - Epoch(train) [56][1879/1879] lr: 2.0000e-03 eta: 8:30:01 time: 0.3038 data_time: 0.0112 memory: 6717 grad_norm: 3.1352 loss: 1.0823 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.0823 2023/04/14 07:43:33 - mmengine - INFO - Epoch(val) [56][ 20/155] eta: 0:01:00 time: 0.4475 data_time: 0.4141 memory: 1391 2023/04/14 07:43:40 - mmengine - INFO - Epoch(val) [56][ 40/155] eta: 0:00:44 time: 0.3274 data_time: 0.2944 memory: 1391 2023/04/14 07:43:49 - mmengine - INFO - Epoch(val) [56][ 60/155] eta: 0:00:38 time: 0.4334 data_time: 0.3993 memory: 1391 2023/04/14 07:43:55 - mmengine - INFO - Epoch(val) [56][ 80/155] eta: 0:00:28 time: 0.3152 data_time: 0.2822 memory: 1391 2023/04/14 07:44:04 - mmengine - INFO - Epoch(val) [56][100/155] eta: 0:00:21 time: 0.4580 data_time: 0.4244 memory: 1391 2023/04/14 07:44:10 - mmengine - INFO - Epoch(val) [56][120/155] eta: 0:00:13 time: 0.3048 data_time: 0.2718 memory: 1391 2023/04/14 07:44:20 - mmengine - INFO - Epoch(val) [56][140/155] eta: 0:00:05 time: 0.4788 data_time: 0.4453 memory: 1391 2023/04/14 07:44:27 - mmengine - INFO - Epoch(val) [56][155/155] acc/top1: 0.6602 acc/top5: 0.8693 acc/mean1: 0.6602 data_time: 0.4139 time: 0.4466 2023/04/14 07:44:37 - mmengine - INFO - Epoch(train) [57][ 20/1879] lr: 2.0000e-03 eta: 8:29:56 time: 0.5031 data_time: 0.2662 memory: 6717 grad_norm: 3.0374 loss: 1.2405 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2405 2023/04/14 07:44:44 - mmengine - INFO - Epoch(train) [57][ 40/1879] lr: 2.0000e-03 eta: 8:29:48 time: 0.3358 data_time: 0.0887 memory: 6717 grad_norm: 3.0342 loss: 1.1705 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1705 2023/04/14 07:44:52 - mmengine - INFO - Epoch(train) [57][ 60/1879] lr: 2.0000e-03 eta: 8:29:41 time: 0.4061 data_time: 0.1208 memory: 6717 grad_norm: 3.0771 loss: 1.3808 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3808 2023/04/14 07:44:59 - mmengine - INFO - Epoch(train) [57][ 80/1879] lr: 2.0000e-03 eta: 8:29:33 time: 0.3531 data_time: 0.1365 memory: 6717 grad_norm: 3.0547 loss: 1.2382 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2382 2023/04/14 07:45:07 - mmengine - INFO - Epoch(train) [57][ 100/1879] lr: 2.0000e-03 eta: 8:29:27 time: 0.4048 data_time: 0.1284 memory: 6717 grad_norm: 3.0558 loss: 1.1827 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1827 2023/04/14 07:45:13 - mmengine - INFO - Epoch(train) [57][ 120/1879] lr: 2.0000e-03 eta: 8:29:18 time: 0.3207 data_time: 0.0877 memory: 6717 grad_norm: 3.0875 loss: 1.2412 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2412 2023/04/14 07:45:22 - mmengine - INFO - Epoch(train) [57][ 140/1879] lr: 2.0000e-03 eta: 8:29:12 time: 0.4225 data_time: 0.0476 memory: 6717 grad_norm: 2.9640 loss: 1.0981 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0981 2023/04/14 07:45:29 - mmengine - INFO - Epoch(train) [57][ 160/1879] lr: 2.0000e-03 eta: 8:29:04 time: 0.3449 data_time: 0.0371 memory: 6717 grad_norm: 3.0508 loss: 1.0062 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0062 2023/04/14 07:45:37 - mmengine - INFO - Epoch(train) [57][ 180/1879] lr: 2.0000e-03 eta: 8:28:58 time: 0.4356 data_time: 0.0162 memory: 6717 grad_norm: 3.1160 loss: 1.1388 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1388 2023/04/14 07:45:44 - mmengine - INFO - Epoch(train) [57][ 200/1879] lr: 2.0000e-03 eta: 8:28:50 time: 0.3330 data_time: 0.0129 memory: 6717 grad_norm: 3.0744 loss: 1.1557 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1557 2023/04/14 07:45:52 - mmengine - INFO - Epoch(train) [57][ 220/1879] lr: 2.0000e-03 eta: 8:28:43 time: 0.4127 data_time: 0.0148 memory: 6717 grad_norm: 2.9788 loss: 1.1259 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1259 2023/04/14 07:45:58 - mmengine - INFO - Epoch(train) [57][ 240/1879] lr: 2.0000e-03 eta: 8:28:35 time: 0.3051 data_time: 0.0149 memory: 6717 grad_norm: 3.1136 loss: 1.2187 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2187 2023/04/14 07:46:06 - mmengine - INFO - Epoch(train) [57][ 260/1879] lr: 2.0000e-03 eta: 8:28:27 time: 0.3928 data_time: 0.0141 memory: 6717 grad_norm: 3.1095 loss: 1.0448 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0448 2023/04/14 07:46:12 - mmengine - INFO - Epoch(train) [57][ 280/1879] lr: 2.0000e-03 eta: 8:28:19 time: 0.3006 data_time: 0.0142 memory: 6717 grad_norm: 3.0344 loss: 1.2777 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2777 2023/04/14 07:46:20 - mmengine - INFO - Epoch(train) [57][ 300/1879] lr: 2.0000e-03 eta: 8:28:12 time: 0.3944 data_time: 0.0142 memory: 6717 grad_norm: 3.0563 loss: 1.2505 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2505 2023/04/14 07:46:27 - mmengine - INFO - Epoch(train) [57][ 320/1879] lr: 2.0000e-03 eta: 8:28:04 time: 0.3210 data_time: 0.0137 memory: 6717 grad_norm: 3.0789 loss: 1.3342 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3342 2023/04/14 07:46:35 - mmengine - INFO - Epoch(train) [57][ 340/1879] lr: 2.0000e-03 eta: 8:27:57 time: 0.4196 data_time: 0.0153 memory: 6717 grad_norm: 3.1011 loss: 1.2339 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2339 2023/04/14 07:46:41 - mmengine - INFO - Epoch(train) [57][ 360/1879] lr: 2.0000e-03 eta: 8:27:49 time: 0.2974 data_time: 0.0132 memory: 6717 grad_norm: 3.0450 loss: 1.2721 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2721 2023/04/14 07:46:49 - mmengine - INFO - Epoch(train) [57][ 380/1879] lr: 2.0000e-03 eta: 8:27:42 time: 0.3989 data_time: 0.0163 memory: 6717 grad_norm: 3.1093 loss: 1.3028 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3028 2023/04/14 07:46:56 - mmengine - INFO - Epoch(train) [57][ 400/1879] lr: 2.0000e-03 eta: 8:27:34 time: 0.3562 data_time: 0.0127 memory: 6717 grad_norm: 3.0188 loss: 1.1765 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1765 2023/04/14 07:47:04 - mmengine - INFO - Epoch(train) [57][ 420/1879] lr: 2.0000e-03 eta: 8:27:27 time: 0.4098 data_time: 0.0160 memory: 6717 grad_norm: 3.1106 loss: 1.0762 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0762 2023/04/14 07:47:11 - mmengine - INFO - Epoch(train) [57][ 440/1879] lr: 2.0000e-03 eta: 8:27:19 time: 0.3199 data_time: 0.0128 memory: 6717 grad_norm: 3.0728 loss: 1.1418 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.1418 2023/04/14 07:47:20 - mmengine - INFO - Epoch(train) [57][ 460/1879] lr: 2.0000e-03 eta: 8:27:13 time: 0.4439 data_time: 0.0159 memory: 6717 grad_norm: 3.1044 loss: 1.2478 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2478 2023/04/14 07:47:26 - mmengine - INFO - Epoch(train) [57][ 480/1879] lr: 2.0000e-03 eta: 8:27:05 time: 0.3410 data_time: 0.0133 memory: 6717 grad_norm: 3.0512 loss: 1.0396 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0396 2023/04/14 07:47:34 - mmengine - INFO - Epoch(train) [57][ 500/1879] lr: 2.0000e-03 eta: 8:26:58 time: 0.3810 data_time: 0.0154 memory: 6717 grad_norm: 3.0746 loss: 1.3025 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3025 2023/04/14 07:47:41 - mmengine - INFO - Epoch(train) [57][ 520/1879] lr: 2.0000e-03 eta: 8:26:50 time: 0.3243 data_time: 0.0152 memory: 6717 grad_norm: 3.1237 loss: 1.1585 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1585 2023/04/14 07:47:49 - mmengine - INFO - Epoch(train) [57][ 540/1879] lr: 2.0000e-03 eta: 8:26:43 time: 0.4042 data_time: 0.0156 memory: 6717 grad_norm: 3.0824 loss: 1.2604 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2604 2023/04/14 07:47:55 - mmengine - INFO - Epoch(train) [57][ 560/1879] lr: 2.0000e-03 eta: 8:26:35 time: 0.3265 data_time: 0.0132 memory: 6717 grad_norm: 2.9621 loss: 1.2255 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2255 2023/04/14 07:48:03 - mmengine - INFO - Epoch(train) [57][ 580/1879] lr: 2.0000e-03 eta: 8:26:28 time: 0.4119 data_time: 0.0156 memory: 6717 grad_norm: 3.0404 loss: 1.1387 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1387 2023/04/14 07:48:10 - mmengine - INFO - Epoch(train) [57][ 600/1879] lr: 2.0000e-03 eta: 8:26:20 time: 0.3202 data_time: 0.0123 memory: 6717 grad_norm: 3.0953 loss: 1.2107 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2107 2023/04/14 07:48:18 - mmengine - INFO - Epoch(train) [57][ 620/1879] lr: 2.0000e-03 eta: 8:26:13 time: 0.4136 data_time: 0.0142 memory: 6717 grad_norm: 3.0863 loss: 1.3495 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3495 2023/04/14 07:48:25 - mmengine - INFO - Epoch(train) [57][ 640/1879] lr: 2.0000e-03 eta: 8:26:05 time: 0.3232 data_time: 0.0131 memory: 6717 grad_norm: 3.1061 loss: 1.2016 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2016 2023/04/14 07:48:32 - mmengine - INFO - Epoch(train) [57][ 660/1879] lr: 2.0000e-03 eta: 8:25:58 time: 0.3912 data_time: 0.0179 memory: 6717 grad_norm: 3.0854 loss: 1.1662 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1662 2023/04/14 07:48:40 - mmengine - INFO - Epoch(train) [57][ 680/1879] lr: 2.0000e-03 eta: 8:25:50 time: 0.3711 data_time: 0.0127 memory: 6717 grad_norm: 3.0679 loss: 1.2103 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2103 2023/04/14 07:48:47 - mmengine - INFO - Epoch(train) [57][ 700/1879] lr: 2.0000e-03 eta: 8:25:43 time: 0.3747 data_time: 0.0151 memory: 6717 grad_norm: 3.1538 loss: 1.3631 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3631 2023/04/14 07:48:54 - mmengine - INFO - Epoch(train) [57][ 720/1879] lr: 2.0000e-03 eta: 8:25:35 time: 0.3490 data_time: 0.0182 memory: 6717 grad_norm: 3.0735 loss: 1.1558 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1558 2023/04/14 07:49:02 - mmengine - INFO - Epoch(train) [57][ 740/1879] lr: 2.0000e-03 eta: 8:25:28 time: 0.3929 data_time: 0.0154 memory: 6717 grad_norm: 3.0750 loss: 1.0547 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0547 2023/04/14 07:49:10 - mmengine - INFO - Epoch(train) [57][ 760/1879] lr: 2.0000e-03 eta: 8:25:21 time: 0.3696 data_time: 0.0157 memory: 6717 grad_norm: 3.0613 loss: 1.0539 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0539 2023/04/14 07:49:16 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 07:49:17 - mmengine - INFO - Epoch(train) [57][ 780/1879] lr: 2.0000e-03 eta: 8:25:13 time: 0.3668 data_time: 0.0143 memory: 6717 grad_norm: 3.0515 loss: 1.1984 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1984 2023/04/14 07:49:25 - mmengine - INFO - Epoch(train) [57][ 800/1879] lr: 2.0000e-03 eta: 8:25:07 time: 0.4114 data_time: 0.0161 memory: 6717 grad_norm: 3.0414 loss: 1.2144 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2144 2023/04/14 07:49:31 - mmengine - INFO - Epoch(train) [57][ 820/1879] lr: 2.0000e-03 eta: 8:24:58 time: 0.3099 data_time: 0.0123 memory: 6717 grad_norm: 3.1190 loss: 1.2171 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.2171 2023/04/14 07:49:39 - mmengine - INFO - Epoch(train) [57][ 840/1879] lr: 2.0000e-03 eta: 8:24:51 time: 0.4052 data_time: 0.0139 memory: 6717 grad_norm: 3.0891 loss: 1.0592 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0592 2023/04/14 07:49:46 - mmengine - INFO - Epoch(train) [57][ 860/1879] lr: 2.0000e-03 eta: 8:24:44 time: 0.3503 data_time: 0.0150 memory: 6717 grad_norm: 3.0585 loss: 1.3490 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3490 2023/04/14 07:49:55 - mmengine - INFO - Epoch(train) [57][ 880/1879] lr: 2.0000e-03 eta: 8:24:37 time: 0.4119 data_time: 0.0142 memory: 6717 grad_norm: 3.0776 loss: 1.2618 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2618 2023/04/14 07:50:01 - mmengine - INFO - Epoch(train) [57][ 900/1879] lr: 2.0000e-03 eta: 8:24:29 time: 0.3056 data_time: 0.0146 memory: 6717 grad_norm: 3.0537 loss: 1.2648 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2648 2023/04/14 07:50:09 - mmengine - INFO - Epoch(train) [57][ 920/1879] lr: 2.0000e-03 eta: 8:24:22 time: 0.3969 data_time: 0.0143 memory: 6717 grad_norm: 3.0592 loss: 1.1405 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1405 2023/04/14 07:50:15 - mmengine - INFO - Epoch(train) [57][ 940/1879] lr: 2.0000e-03 eta: 8:24:14 time: 0.3321 data_time: 0.0151 memory: 6717 grad_norm: 3.0582 loss: 1.2073 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2073 2023/04/14 07:50:24 - mmengine - INFO - Epoch(train) [57][ 960/1879] lr: 2.0000e-03 eta: 8:24:07 time: 0.4112 data_time: 0.0141 memory: 6717 grad_norm: 3.0905 loss: 1.2253 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2253 2023/04/14 07:50:30 - mmengine - INFO - Epoch(train) [57][ 980/1879] lr: 2.0000e-03 eta: 8:23:59 time: 0.3177 data_time: 0.0160 memory: 6717 grad_norm: 3.0926 loss: 1.2917 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2917 2023/04/14 07:50:38 - mmengine - INFO - Epoch(train) [57][1000/1879] lr: 2.0000e-03 eta: 8:23:52 time: 0.4037 data_time: 0.0134 memory: 6717 grad_norm: 3.0424 loss: 1.0464 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0464 2023/04/14 07:50:45 - mmengine - INFO - Epoch(train) [57][1020/1879] lr: 2.0000e-03 eta: 8:23:44 time: 0.3306 data_time: 0.0154 memory: 6717 grad_norm: 3.1935 loss: 1.1442 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1442 2023/04/14 07:50:53 - mmengine - INFO - Epoch(train) [57][1040/1879] lr: 2.0000e-03 eta: 8:23:37 time: 0.4026 data_time: 0.0172 memory: 6717 grad_norm: 3.0604 loss: 1.1518 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1518 2023/04/14 07:51:00 - mmengine - INFO - Epoch(train) [57][1060/1879] lr: 2.0000e-03 eta: 8:23:29 time: 0.3483 data_time: 0.0152 memory: 6717 grad_norm: 3.0757 loss: 1.1794 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1794 2023/04/14 07:51:07 - mmengine - INFO - Epoch(train) [57][1080/1879] lr: 2.0000e-03 eta: 8:23:22 time: 0.3828 data_time: 0.0145 memory: 6717 grad_norm: 3.0763 loss: 1.2083 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2083 2023/04/14 07:51:14 - mmengine - INFO - Epoch(train) [57][1100/1879] lr: 2.0000e-03 eta: 8:23:14 time: 0.3337 data_time: 0.0146 memory: 6717 grad_norm: 3.0136 loss: 1.4494 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4494 2023/04/14 07:51:22 - mmengine - INFO - Epoch(train) [57][1120/1879] lr: 2.0000e-03 eta: 8:23:07 time: 0.4161 data_time: 0.0135 memory: 6717 grad_norm: 3.0624 loss: 1.1256 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1256 2023/04/14 07:51:29 - mmengine - INFO - Epoch(train) [57][1140/1879] lr: 2.0000e-03 eta: 8:22:59 time: 0.3297 data_time: 0.0163 memory: 6717 grad_norm: 3.0129 loss: 1.2075 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2075 2023/04/14 07:51:38 - mmengine - INFO - Epoch(train) [57][1160/1879] lr: 2.0000e-03 eta: 8:22:53 time: 0.4310 data_time: 0.0122 memory: 6717 grad_norm: 3.0872 loss: 1.4156 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4156 2023/04/14 07:51:44 - mmengine - INFO - Epoch(train) [57][1180/1879] lr: 2.0000e-03 eta: 8:22:45 time: 0.3382 data_time: 0.0162 memory: 6717 grad_norm: 3.1702 loss: 1.1545 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1545 2023/04/14 07:51:52 - mmengine - INFO - Epoch(train) [57][1200/1879] lr: 2.0000e-03 eta: 8:22:38 time: 0.4060 data_time: 0.0170 memory: 6717 grad_norm: 3.0959 loss: 1.3185 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.3185 2023/04/14 07:51:59 - mmengine - INFO - Epoch(train) [57][1220/1879] lr: 2.0000e-03 eta: 8:22:30 time: 0.3281 data_time: 0.0148 memory: 6717 grad_norm: 3.0580 loss: 1.4067 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.4067 2023/04/14 07:52:07 - mmengine - INFO - Epoch(train) [57][1240/1879] lr: 2.0000e-03 eta: 8:22:23 time: 0.4129 data_time: 0.0133 memory: 6717 grad_norm: 2.9941 loss: 1.3345 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3345 2023/04/14 07:52:13 - mmengine - INFO - Epoch(train) [57][1260/1879] lr: 2.0000e-03 eta: 8:22:15 time: 0.2897 data_time: 0.0148 memory: 6717 grad_norm: 3.0537 loss: 1.2726 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2726 2023/04/14 07:52:22 - mmengine - INFO - Epoch(train) [57][1280/1879] lr: 2.0000e-03 eta: 8:22:08 time: 0.4441 data_time: 0.0144 memory: 6717 grad_norm: 3.1129 loss: 1.2992 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2992 2023/04/14 07:52:28 - mmengine - INFO - Epoch(train) [57][1300/1879] lr: 2.0000e-03 eta: 8:22:00 time: 0.3011 data_time: 0.0138 memory: 6717 grad_norm: 3.1341 loss: 1.1740 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1740 2023/04/14 07:52:36 - mmengine - INFO - Epoch(train) [57][1320/1879] lr: 2.0000e-03 eta: 8:21:53 time: 0.3812 data_time: 0.0149 memory: 6717 grad_norm: 3.0846 loss: 1.3293 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3293 2023/04/14 07:52:43 - mmengine - INFO - Epoch(train) [57][1340/1879] lr: 2.0000e-03 eta: 8:21:45 time: 0.3596 data_time: 0.0256 memory: 6717 grad_norm: 3.1073 loss: 1.2014 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2014 2023/04/14 07:52:51 - mmengine - INFO - Epoch(train) [57][1360/1879] lr: 2.0000e-03 eta: 8:21:38 time: 0.3886 data_time: 0.0158 memory: 6717 grad_norm: 3.0610 loss: 1.4098 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4098 2023/04/14 07:52:58 - mmengine - INFO - Epoch(train) [57][1380/1879] lr: 2.0000e-03 eta: 8:21:31 time: 0.3696 data_time: 0.0136 memory: 6717 grad_norm: 2.9763 loss: 1.2844 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2844 2023/04/14 07:53:05 - mmengine - INFO - Epoch(train) [57][1400/1879] lr: 2.0000e-03 eta: 8:21:23 time: 0.3433 data_time: 0.0129 memory: 6717 grad_norm: 3.0892 loss: 1.3999 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3999 2023/04/14 07:53:12 - mmengine - INFO - Epoch(train) [57][1420/1879] lr: 2.0000e-03 eta: 8:21:15 time: 0.3780 data_time: 0.0153 memory: 6717 grad_norm: 3.0564 loss: 1.1521 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1521 2023/04/14 07:53:19 - mmengine - INFO - Epoch(train) [57][1440/1879] lr: 2.0000e-03 eta: 8:21:08 time: 0.3537 data_time: 0.0136 memory: 6717 grad_norm: 3.0977 loss: 1.2144 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2144 2023/04/14 07:53:27 - mmengine - INFO - Epoch(train) [57][1460/1879] lr: 2.0000e-03 eta: 8:21:00 time: 0.3766 data_time: 0.0143 memory: 6717 grad_norm: 3.0377 loss: 1.1839 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1839 2023/04/14 07:53:35 - mmengine - INFO - Epoch(train) [57][1480/1879] lr: 2.0000e-03 eta: 8:20:53 time: 0.3890 data_time: 0.0142 memory: 6717 grad_norm: 3.1188 loss: 1.2261 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2261 2023/04/14 07:53:41 - mmengine - INFO - Epoch(train) [57][1500/1879] lr: 2.0000e-03 eta: 8:20:45 time: 0.3316 data_time: 0.0156 memory: 6717 grad_norm: 3.1101 loss: 1.4770 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4770 2023/04/14 07:53:49 - mmengine - INFO - Epoch(train) [57][1520/1879] lr: 2.0000e-03 eta: 8:20:38 time: 0.4003 data_time: 0.0138 memory: 6717 grad_norm: 3.0023 loss: 1.3170 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3170 2023/04/14 07:53:56 - mmengine - INFO - Epoch(train) [57][1540/1879] lr: 2.0000e-03 eta: 8:20:31 time: 0.3428 data_time: 0.0318 memory: 6717 grad_norm: 3.0775 loss: 1.1070 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1070 2023/04/14 07:54:04 - mmengine - INFO - Epoch(train) [57][1560/1879] lr: 2.0000e-03 eta: 8:20:23 time: 0.3782 data_time: 0.0703 memory: 6717 grad_norm: 3.0918 loss: 1.2396 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2396 2023/04/14 07:54:11 - mmengine - INFO - Epoch(train) [57][1580/1879] lr: 2.0000e-03 eta: 8:20:16 time: 0.3504 data_time: 0.0375 memory: 6717 grad_norm: 3.1150 loss: 1.3501 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3501 2023/04/14 07:54:19 - mmengine - INFO - Epoch(train) [57][1600/1879] lr: 2.0000e-03 eta: 8:20:09 time: 0.4138 data_time: 0.0122 memory: 6717 grad_norm: 2.9978 loss: 1.1770 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1770 2023/04/14 07:54:26 - mmengine - INFO - Epoch(train) [57][1620/1879] lr: 2.0000e-03 eta: 8:20:01 time: 0.3228 data_time: 0.0168 memory: 6717 grad_norm: 3.0982 loss: 1.4446 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.4446 2023/04/14 07:54:34 - mmengine - INFO - Epoch(train) [57][1640/1879] lr: 2.0000e-03 eta: 8:19:54 time: 0.4107 data_time: 0.0118 memory: 6717 grad_norm: 3.0761 loss: 1.3071 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3071 2023/04/14 07:54:41 - mmengine - INFO - Epoch(train) [57][1660/1879] lr: 2.0000e-03 eta: 8:19:46 time: 0.3614 data_time: 0.0157 memory: 6717 grad_norm: 3.0296 loss: 1.4039 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.4039 2023/04/14 07:54:49 - mmengine - INFO - Epoch(train) [57][1680/1879] lr: 2.0000e-03 eta: 8:19:39 time: 0.3914 data_time: 0.0135 memory: 6717 grad_norm: 3.0135 loss: 1.1785 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1785 2023/04/14 07:54:56 - mmengine - INFO - Epoch(train) [57][1700/1879] lr: 2.0000e-03 eta: 8:19:32 time: 0.3412 data_time: 0.0146 memory: 6717 grad_norm: 2.9914 loss: 1.1874 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1874 2023/04/14 07:55:04 - mmengine - INFO - Epoch(train) [57][1720/1879] lr: 2.0000e-03 eta: 8:19:25 time: 0.4333 data_time: 0.0139 memory: 6717 grad_norm: 3.0712 loss: 1.2582 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2582 2023/04/14 07:55:11 - mmengine - INFO - Epoch(train) [57][1740/1879] lr: 2.0000e-03 eta: 8:19:17 time: 0.3175 data_time: 0.0150 memory: 6717 grad_norm: 3.0342 loss: 1.1743 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1743 2023/04/14 07:55:19 - mmengine - INFO - Epoch(train) [57][1760/1879] lr: 2.0000e-03 eta: 8:19:10 time: 0.4115 data_time: 0.0165 memory: 6717 grad_norm: 3.1355 loss: 1.3040 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3040 2023/04/14 07:55:25 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 07:55:26 - mmengine - INFO - Epoch(train) [57][1780/1879] lr: 2.0000e-03 eta: 8:19:02 time: 0.3331 data_time: 0.0149 memory: 6717 grad_norm: 3.1465 loss: 1.2949 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2949 2023/04/14 07:55:34 - mmengine - INFO - Epoch(train) [57][1800/1879] lr: 2.0000e-03 eta: 8:18:56 time: 0.4300 data_time: 0.0148 memory: 6717 grad_norm: 2.9537 loss: 1.1181 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1181 2023/04/14 07:55:40 - mmengine - INFO - Epoch(train) [57][1820/1879] lr: 2.0000e-03 eta: 8:18:47 time: 0.3118 data_time: 0.0127 memory: 6717 grad_norm: 3.1252 loss: 1.1962 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1962 2023/04/14 07:55:49 - mmengine - INFO - Epoch(train) [57][1840/1879] lr: 2.0000e-03 eta: 8:18:41 time: 0.4098 data_time: 0.0164 memory: 6717 grad_norm: 3.0462 loss: 1.2422 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2422 2023/04/14 07:55:55 - mmengine - INFO - Epoch(train) [57][1860/1879] lr: 2.0000e-03 eta: 8:18:32 time: 0.3215 data_time: 0.0130 memory: 6717 grad_norm: 3.0502 loss: 1.2425 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2425 2023/04/14 07:56:02 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 07:56:02 - mmengine - INFO - Epoch(train) [57][1879/1879] lr: 2.0000e-03 eta: 8:18:25 time: 0.3506 data_time: 0.0129 memory: 6717 grad_norm: 3.1096 loss: 1.3238 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.3238 2023/04/14 07:56:02 - mmengine - INFO - Saving checkpoint at 57 epochs 2023/04/14 07:56:12 - mmengine - INFO - Epoch(val) [57][ 20/155] eta: 0:01:02 time: 0.4630 data_time: 0.4302 memory: 1391 2023/04/14 07:56:18 - mmengine - INFO - Epoch(val) [57][ 40/155] eta: 0:00:43 time: 0.2944 data_time: 0.2613 memory: 1391 2023/04/14 07:56:27 - mmengine - INFO - Epoch(val) [57][ 60/155] eta: 0:00:38 time: 0.4544 data_time: 0.4209 memory: 1391 2023/04/14 07:56:33 - mmengine - INFO - Epoch(val) [57][ 80/155] eta: 0:00:28 time: 0.3166 data_time: 0.2831 memory: 1391 2023/04/14 07:56:42 - mmengine - INFO - Epoch(val) [57][100/155] eta: 0:00:21 time: 0.4582 data_time: 0.4250 memory: 1391 2023/04/14 07:56:48 - mmengine - INFO - Epoch(val) [57][120/155] eta: 0:00:13 time: 0.3005 data_time: 0.2675 memory: 1391 2023/04/14 07:56:58 - mmengine - INFO - Epoch(val) [57][140/155] eta: 0:00:05 time: 0.4840 data_time: 0.4515 memory: 1391 2023/04/14 07:57:05 - mmengine - INFO - Epoch(val) [57][155/155] acc/top1: 0.6625 acc/top5: 0.8708 acc/mean1: 0.6624 data_time: 0.4182 time: 0.4501 2023/04/14 07:57:15 - mmengine - INFO - Epoch(train) [58][ 20/1879] lr: 2.0000e-03 eta: 8:18:20 time: 0.4859 data_time: 0.2552 memory: 6717 grad_norm: 3.1121 loss: 1.2601 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2601 2023/04/14 07:57:21 - mmengine - INFO - Epoch(train) [58][ 40/1879] lr: 2.0000e-03 eta: 8:18:11 time: 0.3171 data_time: 0.1103 memory: 6717 grad_norm: 3.1051 loss: 1.2980 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2980 2023/04/14 07:57:30 - mmengine - INFO - Epoch(train) [58][ 60/1879] lr: 2.0000e-03 eta: 8:18:05 time: 0.4455 data_time: 0.1978 memory: 6717 grad_norm: 3.0764 loss: 1.1595 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1595 2023/04/14 07:57:36 - mmengine - INFO - Epoch(train) [58][ 80/1879] lr: 2.0000e-03 eta: 8:17:57 time: 0.3209 data_time: 0.0783 memory: 6717 grad_norm: 2.9836 loss: 1.1109 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1109 2023/04/14 07:57:45 - mmengine - INFO - Epoch(train) [58][ 100/1879] lr: 2.0000e-03 eta: 8:17:50 time: 0.4137 data_time: 0.0287 memory: 6717 grad_norm: 3.0389 loss: 1.2039 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2039 2023/04/14 07:57:51 - mmengine - INFO - Epoch(train) [58][ 120/1879] lr: 2.0000e-03 eta: 8:17:42 time: 0.3178 data_time: 0.0400 memory: 6717 grad_norm: 3.1030 loss: 1.2383 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2383 2023/04/14 07:57:59 - mmengine - INFO - Epoch(train) [58][ 140/1879] lr: 2.0000e-03 eta: 8:17:35 time: 0.4126 data_time: 0.0151 memory: 6717 grad_norm: 3.0881 loss: 1.2475 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2475 2023/04/14 07:58:06 - mmengine - INFO - Epoch(train) [58][ 160/1879] lr: 2.0000e-03 eta: 8:17:28 time: 0.3529 data_time: 0.0139 memory: 6717 grad_norm: 3.0898 loss: 1.3122 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3122 2023/04/14 07:58:14 - mmengine - INFO - Epoch(train) [58][ 180/1879] lr: 2.0000e-03 eta: 8:17:21 time: 0.3908 data_time: 0.0151 memory: 6717 grad_norm: 3.1018 loss: 1.2833 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2833 2023/04/14 07:58:22 - mmengine - INFO - Epoch(train) [58][ 200/1879] lr: 2.0000e-03 eta: 8:17:13 time: 0.3853 data_time: 0.0139 memory: 6717 grad_norm: 3.0457 loss: 1.1580 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1580 2023/04/14 07:58:29 - mmengine - INFO - Epoch(train) [58][ 220/1879] lr: 2.0000e-03 eta: 8:17:06 time: 0.3750 data_time: 0.0149 memory: 6717 grad_norm: 3.1172 loss: 1.2161 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2161 2023/04/14 07:58:36 - mmengine - INFO - Epoch(train) [58][ 240/1879] lr: 2.0000e-03 eta: 8:16:58 time: 0.3171 data_time: 0.0136 memory: 6717 grad_norm: 3.0467 loss: 1.3570 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3570 2023/04/14 07:58:44 - mmengine - INFO - Epoch(train) [58][ 260/1879] lr: 2.0000e-03 eta: 8:16:51 time: 0.4376 data_time: 0.0185 memory: 6717 grad_norm: 3.0310 loss: 1.1727 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1727 2023/04/14 07:58:51 - mmengine - INFO - Epoch(train) [58][ 280/1879] lr: 2.0000e-03 eta: 8:16:43 time: 0.3272 data_time: 0.0126 memory: 6717 grad_norm: 3.1455 loss: 1.2407 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.2407 2023/04/14 07:58:59 - mmengine - INFO - Epoch(train) [58][ 300/1879] lr: 2.0000e-03 eta: 8:16:36 time: 0.3982 data_time: 0.0146 memory: 6717 grad_norm: 2.9767 loss: 1.1990 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1990 2023/04/14 07:59:05 - mmengine - INFO - Epoch(train) [58][ 320/1879] lr: 2.0000e-03 eta: 8:16:28 time: 0.3205 data_time: 0.0147 memory: 6717 grad_norm: 3.1151 loss: 1.1206 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1206 2023/04/14 07:59:13 - mmengine - INFO - Epoch(train) [58][ 340/1879] lr: 2.0000e-03 eta: 8:16:21 time: 0.3942 data_time: 0.0166 memory: 6717 grad_norm: 2.9852 loss: 1.0862 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0862 2023/04/14 07:59:21 - mmengine - INFO - Epoch(train) [58][ 360/1879] lr: 2.0000e-03 eta: 8:16:14 time: 0.3685 data_time: 0.0134 memory: 6717 grad_norm: 3.0224 loss: 1.1767 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1767 2023/04/14 07:59:28 - mmengine - INFO - Epoch(train) [58][ 380/1879] lr: 2.0000e-03 eta: 8:16:06 time: 0.3799 data_time: 0.0154 memory: 6717 grad_norm: 3.0011 loss: 1.3047 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3047 2023/04/14 07:59:36 - mmengine - INFO - Epoch(train) [58][ 400/1879] lr: 2.0000e-03 eta: 8:15:59 time: 0.3818 data_time: 0.0121 memory: 6717 grad_norm: 3.0903 loss: 1.5288 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5288 2023/04/14 07:59:43 - mmengine - INFO - Epoch(train) [58][ 420/1879] lr: 2.0000e-03 eta: 8:15:52 time: 0.3571 data_time: 0.0183 memory: 6717 grad_norm: 3.1093 loss: 1.2704 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2704 2023/04/14 07:59:50 - mmengine - INFO - Epoch(train) [58][ 440/1879] lr: 2.0000e-03 eta: 8:15:44 time: 0.3568 data_time: 0.0129 memory: 6717 grad_norm: 3.0942 loss: 1.2537 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2537 2023/04/14 07:59:58 - mmengine - INFO - Epoch(train) [58][ 460/1879] lr: 2.0000e-03 eta: 8:15:37 time: 0.4115 data_time: 0.0164 memory: 6717 grad_norm: 3.1460 loss: 1.3768 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.3768 2023/04/14 08:00:05 - mmengine - INFO - Epoch(train) [58][ 480/1879] lr: 2.0000e-03 eta: 8:15:29 time: 0.3285 data_time: 0.0130 memory: 6717 grad_norm: 3.0522 loss: 1.1162 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1162 2023/04/14 08:00:13 - mmengine - INFO - Epoch(train) [58][ 500/1879] lr: 2.0000e-03 eta: 8:15:23 time: 0.4239 data_time: 0.0157 memory: 6717 grad_norm: 3.1066 loss: 1.3659 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3659 2023/04/14 08:00:19 - mmengine - INFO - Epoch(train) [58][ 520/1879] lr: 2.0000e-03 eta: 8:15:14 time: 0.2997 data_time: 0.0135 memory: 6717 grad_norm: 3.0024 loss: 1.0243 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0243 2023/04/14 08:00:28 - mmengine - INFO - Epoch(train) [58][ 540/1879] lr: 2.0000e-03 eta: 8:15:08 time: 0.4389 data_time: 0.0156 memory: 6717 grad_norm: 2.9823 loss: 1.1058 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1058 2023/04/14 08:00:34 - mmengine - INFO - Epoch(train) [58][ 560/1879] lr: 2.0000e-03 eta: 8:15:00 time: 0.3126 data_time: 0.0138 memory: 6717 grad_norm: 3.1393 loss: 1.2999 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2999 2023/04/14 08:00:43 - mmengine - INFO - Epoch(train) [58][ 580/1879] lr: 2.0000e-03 eta: 8:14:53 time: 0.4225 data_time: 0.0162 memory: 6717 grad_norm: 3.0157 loss: 1.1502 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1502 2023/04/14 08:00:49 - mmengine - INFO - Epoch(train) [58][ 600/1879] lr: 2.0000e-03 eta: 8:14:45 time: 0.3139 data_time: 0.0131 memory: 6717 grad_norm: 3.0698 loss: 1.3162 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3162 2023/04/14 08:00:58 - mmengine - INFO - Epoch(train) [58][ 620/1879] lr: 2.0000e-03 eta: 8:14:38 time: 0.4256 data_time: 0.0168 memory: 6717 grad_norm: 2.9850 loss: 1.2576 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2576 2023/04/14 08:01:04 - mmengine - INFO - Epoch(train) [58][ 640/1879] lr: 2.0000e-03 eta: 8:14:30 time: 0.3217 data_time: 0.0169 memory: 6717 grad_norm: 3.0926 loss: 1.5031 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.5031 2023/04/14 08:01:12 - mmengine - INFO - Epoch(train) [58][ 660/1879] lr: 2.0000e-03 eta: 8:14:23 time: 0.4099 data_time: 0.0189 memory: 6717 grad_norm: 3.1585 loss: 1.3570 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3570 2023/04/14 08:01:19 - mmengine - INFO - Epoch(train) [58][ 680/1879] lr: 2.0000e-03 eta: 8:14:15 time: 0.3362 data_time: 0.0506 memory: 6717 grad_norm: 2.9958 loss: 1.3660 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.3660 2023/04/14 08:01:28 - mmengine - INFO - Epoch(train) [58][ 700/1879] lr: 2.0000e-03 eta: 8:14:09 time: 0.4213 data_time: 0.1896 memory: 6717 grad_norm: 3.0600 loss: 1.3737 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3737 2023/04/14 08:01:34 - mmengine - INFO - Epoch(train) [58][ 720/1879] lr: 2.0000e-03 eta: 8:14:00 time: 0.3125 data_time: 0.1618 memory: 6717 grad_norm: 3.0489 loss: 1.0984 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0984 2023/04/14 08:01:42 - mmengine - INFO - Epoch(train) [58][ 740/1879] lr: 2.0000e-03 eta: 8:13:53 time: 0.3977 data_time: 0.1597 memory: 6717 grad_norm: 3.1547 loss: 1.1428 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1428 2023/04/14 08:01:49 - mmengine - INFO - Epoch(train) [58][ 760/1879] lr: 2.0000e-03 eta: 8:13:46 time: 0.3389 data_time: 0.0498 memory: 6717 grad_norm: 3.0823 loss: 1.4221 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4221 2023/04/14 08:01:57 - mmengine - INFO - Epoch(train) [58][ 780/1879] lr: 2.0000e-03 eta: 8:13:39 time: 0.4160 data_time: 0.1358 memory: 6717 grad_norm: 2.9476 loss: 1.2746 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2746 2023/04/14 08:02:03 - mmengine - INFO - Epoch(train) [58][ 800/1879] lr: 2.0000e-03 eta: 8:13:30 time: 0.2985 data_time: 0.1165 memory: 6717 grad_norm: 3.0769 loss: 1.2319 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2319 2023/04/14 08:02:12 - mmengine - INFO - Epoch(train) [58][ 820/1879] lr: 2.0000e-03 eta: 8:13:24 time: 0.4335 data_time: 0.1823 memory: 6717 grad_norm: 3.1363 loss: 1.1932 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1932 2023/04/14 08:02:18 - mmengine - INFO - Epoch(train) [58][ 840/1879] lr: 2.0000e-03 eta: 8:13:16 time: 0.3128 data_time: 0.0975 memory: 6717 grad_norm: 3.1751 loss: 1.2508 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2508 2023/04/14 08:02:26 - mmengine - INFO - Epoch(train) [58][ 860/1879] lr: 2.0000e-03 eta: 8:13:09 time: 0.4133 data_time: 0.1484 memory: 6717 grad_norm: 3.0271 loss: 1.0364 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0364 2023/04/14 08:02:33 - mmengine - INFO - Epoch(train) [58][ 880/1879] lr: 2.0000e-03 eta: 8:13:01 time: 0.3369 data_time: 0.1554 memory: 6717 grad_norm: 3.0854 loss: 1.2203 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2203 2023/04/14 08:02:39 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 08:02:41 - mmengine - INFO - Epoch(train) [58][ 900/1879] lr: 2.0000e-03 eta: 8:12:54 time: 0.4259 data_time: 0.2612 memory: 6717 grad_norm: 3.1013 loss: 1.1267 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1267 2023/04/14 08:02:47 - mmengine - INFO - Epoch(train) [58][ 920/1879] lr: 2.0000e-03 eta: 8:12:46 time: 0.3053 data_time: 0.1685 memory: 6717 grad_norm: 3.0462 loss: 1.2339 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2339 2023/04/14 08:02:56 - mmengine - INFO - Epoch(train) [58][ 940/1879] lr: 2.0000e-03 eta: 8:12:39 time: 0.4287 data_time: 0.2788 memory: 6717 grad_norm: 3.1611 loss: 1.1377 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1377 2023/04/14 08:03:02 - mmengine - INFO - Epoch(train) [58][ 960/1879] lr: 2.0000e-03 eta: 8:12:31 time: 0.3079 data_time: 0.1556 memory: 6717 grad_norm: 3.0881 loss: 1.0908 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.0908 2023/04/14 08:03:10 - mmengine - INFO - Epoch(train) [58][ 980/1879] lr: 2.0000e-03 eta: 8:12:24 time: 0.3932 data_time: 0.1872 memory: 6717 grad_norm: 3.1301 loss: 1.4096 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4096 2023/04/14 08:03:17 - mmengine - INFO - Epoch(train) [58][1000/1879] lr: 2.0000e-03 eta: 8:12:16 time: 0.3441 data_time: 0.1890 memory: 6717 grad_norm: 3.0475 loss: 1.2960 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2960 2023/04/14 08:03:26 - mmengine - INFO - Epoch(train) [58][1020/1879] lr: 2.0000e-03 eta: 8:12:10 time: 0.4727 data_time: 0.3286 memory: 6717 grad_norm: 2.9878 loss: 1.2381 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2381 2023/04/14 08:03:32 - mmengine - INFO - Epoch(train) [58][1040/1879] lr: 2.0000e-03 eta: 8:12:02 time: 0.2911 data_time: 0.1508 memory: 6717 grad_norm: 3.1352 loss: 1.2020 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2020 2023/04/14 08:03:40 - mmengine - INFO - Epoch(train) [58][1060/1879] lr: 2.0000e-03 eta: 8:11:55 time: 0.4123 data_time: 0.2686 memory: 6717 grad_norm: 3.0693 loss: 1.2025 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2025 2023/04/14 08:03:47 - mmengine - INFO - Epoch(train) [58][1080/1879] lr: 2.0000e-03 eta: 8:11:47 time: 0.3266 data_time: 0.1837 memory: 6717 grad_norm: 3.0851 loss: 1.1675 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1675 2023/04/14 08:03:55 - mmengine - INFO - Epoch(train) [58][1100/1879] lr: 2.0000e-03 eta: 8:11:40 time: 0.4179 data_time: 0.2729 memory: 6717 grad_norm: 3.0887 loss: 1.2049 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2049 2023/04/14 08:04:02 - mmengine - INFO - Epoch(train) [58][1120/1879] lr: 2.0000e-03 eta: 8:11:33 time: 0.3450 data_time: 0.2062 memory: 6717 grad_norm: 3.1704 loss: 1.3408 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3408 2023/04/14 08:04:10 - mmengine - INFO - Epoch(train) [58][1140/1879] lr: 2.0000e-03 eta: 8:11:26 time: 0.4070 data_time: 0.2646 memory: 6717 grad_norm: 3.1191 loss: 1.1097 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1097 2023/04/14 08:04:16 - mmengine - INFO - Epoch(train) [58][1160/1879] lr: 2.0000e-03 eta: 8:11:17 time: 0.2936 data_time: 0.1554 memory: 6717 grad_norm: 3.0689 loss: 1.2704 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2704 2023/04/14 08:04:24 - mmengine - INFO - Epoch(train) [58][1180/1879] lr: 2.0000e-03 eta: 8:11:10 time: 0.3737 data_time: 0.2321 memory: 6717 grad_norm: 3.0617 loss: 1.2453 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2453 2023/04/14 08:04:31 - mmengine - INFO - Epoch(train) [58][1200/1879] lr: 2.0000e-03 eta: 8:11:02 time: 0.3500 data_time: 0.1587 memory: 6717 grad_norm: 3.0283 loss: 1.1281 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1281 2023/04/14 08:04:39 - mmengine - INFO - Epoch(train) [58][1220/1879] lr: 2.0000e-03 eta: 8:10:55 time: 0.4185 data_time: 0.2115 memory: 6717 grad_norm: 3.1376 loss: 1.0704 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0704 2023/04/14 08:04:46 - mmengine - INFO - Epoch(train) [58][1240/1879] lr: 2.0000e-03 eta: 8:10:48 time: 0.3427 data_time: 0.1258 memory: 6717 grad_norm: 3.0357 loss: 1.4599 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.4599 2023/04/14 08:04:54 - mmengine - INFO - Epoch(train) [58][1260/1879] lr: 2.0000e-03 eta: 8:10:40 time: 0.3871 data_time: 0.1112 memory: 6717 grad_norm: 3.0524 loss: 1.2229 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2229 2023/04/14 08:05:01 - mmengine - INFO - Epoch(train) [58][1280/1879] lr: 2.0000e-03 eta: 8:10:33 time: 0.3580 data_time: 0.0513 memory: 6717 grad_norm: 3.0220 loss: 1.1194 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1194 2023/04/14 08:05:09 - mmengine - INFO - Epoch(train) [58][1300/1879] lr: 2.0000e-03 eta: 8:10:26 time: 0.3900 data_time: 0.0800 memory: 6717 grad_norm: 3.0007 loss: 1.0528 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.0528 2023/04/14 08:05:15 - mmengine - INFO - Epoch(train) [58][1320/1879] lr: 2.0000e-03 eta: 8:10:18 time: 0.3377 data_time: 0.0949 memory: 6717 grad_norm: 3.1334 loss: 1.2167 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2167 2023/04/14 08:05:24 - mmengine - INFO - Epoch(train) [58][1340/1879] lr: 2.0000e-03 eta: 8:10:11 time: 0.4059 data_time: 0.1395 memory: 6717 grad_norm: 2.9829 loss: 1.0096 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0096 2023/04/14 08:05:30 - mmengine - INFO - Epoch(train) [58][1360/1879] lr: 2.0000e-03 eta: 8:10:03 time: 0.3310 data_time: 0.1692 memory: 6717 grad_norm: 2.9921 loss: 1.1144 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1144 2023/04/14 08:05:39 - mmengine - INFO - Epoch(train) [58][1380/1879] lr: 2.0000e-03 eta: 8:09:56 time: 0.4169 data_time: 0.2667 memory: 6717 grad_norm: 3.0870 loss: 1.0989 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0989 2023/04/14 08:05:45 - mmengine - INFO - Epoch(train) [58][1400/1879] lr: 2.0000e-03 eta: 8:09:48 time: 0.3162 data_time: 0.1560 memory: 6717 grad_norm: 3.0660 loss: 1.4060 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4060 2023/04/14 08:05:53 - mmengine - INFO - Epoch(train) [58][1420/1879] lr: 2.0000e-03 eta: 8:09:41 time: 0.4181 data_time: 0.1781 memory: 6717 grad_norm: 3.1417 loss: 1.2302 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2302 2023/04/14 08:06:00 - mmengine - INFO - Epoch(train) [58][1440/1879] lr: 2.0000e-03 eta: 8:09:33 time: 0.3263 data_time: 0.1137 memory: 6717 grad_norm: 3.0767 loss: 1.2639 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2639 2023/04/14 08:06:09 - mmengine - INFO - Epoch(train) [58][1460/1879] lr: 2.0000e-03 eta: 8:09:27 time: 0.4402 data_time: 0.0845 memory: 6717 grad_norm: 3.0158 loss: 1.1304 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1304 2023/04/14 08:06:14 - mmengine - INFO - Epoch(train) [58][1480/1879] lr: 2.0000e-03 eta: 8:09:18 time: 0.2915 data_time: 0.0268 memory: 6717 grad_norm: 3.0870 loss: 1.2817 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2817 2023/04/14 08:06:22 - mmengine - INFO - Epoch(train) [58][1500/1879] lr: 2.0000e-03 eta: 8:09:11 time: 0.3696 data_time: 0.1096 memory: 6717 grad_norm: 3.1112 loss: 1.0751 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0751 2023/04/14 08:06:29 - mmengine - INFO - Epoch(train) [58][1520/1879] lr: 2.0000e-03 eta: 8:09:03 time: 0.3464 data_time: 0.1814 memory: 6717 grad_norm: 3.2003 loss: 1.3317 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3317 2023/04/14 08:06:37 - mmengine - INFO - Epoch(train) [58][1540/1879] lr: 2.0000e-03 eta: 8:08:57 time: 0.4234 data_time: 0.2795 memory: 6717 grad_norm: 3.0850 loss: 1.1958 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1958 2023/04/14 08:06:43 - mmengine - INFO - Epoch(train) [58][1560/1879] lr: 2.0000e-03 eta: 8:08:48 time: 0.2929 data_time: 0.1509 memory: 6717 grad_norm: 3.1322 loss: 1.1909 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1909 2023/04/14 08:06:52 - mmengine - INFO - Epoch(train) [58][1580/1879] lr: 2.0000e-03 eta: 8:08:42 time: 0.4351 data_time: 0.2872 memory: 6717 grad_norm: 3.1078 loss: 1.2937 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2937 2023/04/14 08:06:58 - mmengine - INFO - Epoch(train) [58][1600/1879] lr: 2.0000e-03 eta: 8:08:34 time: 0.3241 data_time: 0.1831 memory: 6717 grad_norm: 3.0545 loss: 1.2850 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2850 2023/04/14 08:07:07 - mmengine - INFO - Epoch(train) [58][1620/1879] lr: 2.0000e-03 eta: 8:08:27 time: 0.4224 data_time: 0.2772 memory: 6717 grad_norm: 3.0588 loss: 1.1306 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1306 2023/04/14 08:07:13 - mmengine - INFO - Epoch(train) [58][1640/1879] lr: 2.0000e-03 eta: 8:08:19 time: 0.3042 data_time: 0.1661 memory: 6717 grad_norm: 3.1245 loss: 1.2824 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2824 2023/04/14 08:07:21 - mmengine - INFO - Epoch(train) [58][1660/1879] lr: 2.0000e-03 eta: 8:08:12 time: 0.4055 data_time: 0.2647 memory: 6717 grad_norm: 3.1064 loss: 1.4932 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.4932 2023/04/14 08:07:27 - mmengine - INFO - Epoch(train) [58][1680/1879] lr: 2.0000e-03 eta: 8:08:04 time: 0.3210 data_time: 0.1374 memory: 6717 grad_norm: 3.0661 loss: 1.3271 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3271 2023/04/14 08:07:36 - mmengine - INFO - Epoch(train) [58][1700/1879] lr: 2.0000e-03 eta: 8:07:57 time: 0.4313 data_time: 0.2836 memory: 6717 grad_norm: 3.1788 loss: 1.4841 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4841 2023/04/14 08:07:42 - mmengine - INFO - Epoch(train) [58][1720/1879] lr: 2.0000e-03 eta: 8:07:49 time: 0.3218 data_time: 0.1800 memory: 6717 grad_norm: 3.0998 loss: 1.1924 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1924 2023/04/14 08:07:51 - mmengine - INFO - Epoch(train) [58][1740/1879] lr: 2.0000e-03 eta: 8:07:42 time: 0.4151 data_time: 0.2754 memory: 6717 grad_norm: 3.1299 loss: 1.1644 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1644 2023/04/14 08:07:57 - mmengine - INFO - Epoch(train) [58][1760/1879] lr: 2.0000e-03 eta: 8:07:34 time: 0.3351 data_time: 0.1805 memory: 6717 grad_norm: 3.1168 loss: 1.2142 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2142 2023/04/14 08:08:05 - mmengine - INFO - Epoch(train) [58][1780/1879] lr: 2.0000e-03 eta: 8:07:27 time: 0.4005 data_time: 0.2202 memory: 6717 grad_norm: 3.1946 loss: 1.3664 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3664 2023/04/14 08:08:12 - mmengine - INFO - Epoch(train) [58][1800/1879] lr: 2.0000e-03 eta: 8:07:19 time: 0.3355 data_time: 0.1925 memory: 6717 grad_norm: 3.1033 loss: 1.3051 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3051 2023/04/14 08:08:21 - mmengine - INFO - Epoch(train) [58][1820/1879] lr: 2.0000e-03 eta: 8:07:13 time: 0.4313 data_time: 0.2872 memory: 6717 grad_norm: 3.1003 loss: 1.2257 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2257 2023/04/14 08:08:27 - mmengine - INFO - Epoch(train) [58][1840/1879] lr: 2.0000e-03 eta: 8:07:05 time: 0.3251 data_time: 0.1854 memory: 6717 grad_norm: 3.1071 loss: 1.3246 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3246 2023/04/14 08:08:35 - mmengine - INFO - Epoch(train) [58][1860/1879] lr: 2.0000e-03 eta: 8:06:58 time: 0.4089 data_time: 0.2549 memory: 6717 grad_norm: 3.1241 loss: 1.1490 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1490 2023/04/14 08:08:41 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 08:08:41 - mmengine - INFO - Epoch(train) [58][1879/1879] lr: 2.0000e-03 eta: 8:06:50 time: 0.2767 data_time: 0.1444 memory: 6717 grad_norm: 3.2170 loss: 1.1751 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 1.1751 2023/04/14 08:08:50 - mmengine - INFO - Epoch(val) [58][ 20/155] eta: 0:01:01 time: 0.4583 data_time: 0.4249 memory: 1391 2023/04/14 08:08:56 - mmengine - INFO - Epoch(val) [58][ 40/155] eta: 0:00:44 time: 0.3173 data_time: 0.2838 memory: 1391 2023/04/14 08:09:05 - mmengine - INFO - Epoch(val) [58][ 60/155] eta: 0:00:38 time: 0.4329 data_time: 0.3964 memory: 1391 2023/04/14 08:09:11 - mmengine - INFO - Epoch(val) [58][ 80/155] eta: 0:00:28 time: 0.3179 data_time: 0.2843 memory: 1391 2023/04/14 08:09:20 - mmengine - INFO - Epoch(val) [58][100/155] eta: 0:00:21 time: 0.4546 data_time: 0.4216 memory: 1391 2023/04/14 08:09:26 - mmengine - INFO - Epoch(val) [58][120/155] eta: 0:00:13 time: 0.2958 data_time: 0.2626 memory: 1391 2023/04/14 08:09:35 - mmengine - INFO - Epoch(val) [58][140/155] eta: 0:00:05 time: 0.4459 data_time: 0.4127 memory: 1391 2023/04/14 08:09:42 - mmengine - INFO - Epoch(val) [58][155/155] acc/top1: 0.6599 acc/top5: 0.8721 acc/mean1: 0.6599 data_time: 0.3683 time: 0.4006 2023/04/14 08:09:52 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 08:09:52 - mmengine - INFO - Epoch(train) [59][ 20/1879] lr: 2.0000e-03 eta: 8:06:44 time: 0.5019 data_time: 0.2375 memory: 6717 grad_norm: 3.0648 loss: 1.2546 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2546 2023/04/14 08:09:59 - mmengine - INFO - Epoch(train) [59][ 40/1879] lr: 2.0000e-03 eta: 8:06:36 time: 0.3117 data_time: 0.1099 memory: 6717 grad_norm: 2.9907 loss: 1.1784 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1784 2023/04/14 08:10:08 - mmengine - INFO - Epoch(train) [59][ 60/1879] lr: 2.0000e-03 eta: 8:06:30 time: 0.4617 data_time: 0.0888 memory: 6717 grad_norm: 3.1223 loss: 1.1110 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1110 2023/04/14 08:10:14 - mmengine - INFO - Epoch(train) [59][ 80/1879] lr: 2.0000e-03 eta: 8:06:22 time: 0.3068 data_time: 0.0129 memory: 6717 grad_norm: 3.1614 loss: 1.2247 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2247 2023/04/14 08:10:21 - mmengine - INFO - Epoch(train) [59][ 100/1879] lr: 2.0000e-03 eta: 8:06:14 time: 0.3663 data_time: 0.0563 memory: 6717 grad_norm: 3.1404 loss: 1.2337 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.2337 2023/04/14 08:10:28 - mmengine - INFO - Epoch(train) [59][ 120/1879] lr: 2.0000e-03 eta: 8:06:06 time: 0.3443 data_time: 0.0739 memory: 6717 grad_norm: 3.1345 loss: 1.3596 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3596 2023/04/14 08:10:36 - mmengine - INFO - Epoch(train) [59][ 140/1879] lr: 2.0000e-03 eta: 8:05:59 time: 0.4023 data_time: 0.0454 memory: 6717 grad_norm: 3.0528 loss: 1.2251 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2251 2023/04/14 08:10:43 - mmengine - INFO - Epoch(train) [59][ 160/1879] lr: 2.0000e-03 eta: 8:05:51 time: 0.3270 data_time: 0.0470 memory: 6717 grad_norm: 3.0221 loss: 1.2508 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2508 2023/04/14 08:10:51 - mmengine - INFO - Epoch(train) [59][ 180/1879] lr: 2.0000e-03 eta: 8:05:45 time: 0.4212 data_time: 0.0210 memory: 6717 grad_norm: 3.1107 loss: 1.2240 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2240 2023/04/14 08:10:58 - mmengine - INFO - Epoch(train) [59][ 200/1879] lr: 2.0000e-03 eta: 8:05:37 time: 0.3239 data_time: 0.0135 memory: 6717 grad_norm: 3.1131 loss: 1.2165 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2165 2023/04/14 08:11:06 - mmengine - INFO - Epoch(train) [59][ 220/1879] lr: 2.0000e-03 eta: 8:05:30 time: 0.4061 data_time: 0.0159 memory: 6717 grad_norm: 3.0689 loss: 1.1801 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1801 2023/04/14 08:11:12 - mmengine - INFO - Epoch(train) [59][ 240/1879] lr: 2.0000e-03 eta: 8:05:22 time: 0.3266 data_time: 0.0137 memory: 6717 grad_norm: 3.1899 loss: 1.4140 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.4140 2023/04/14 08:11:21 - mmengine - INFO - Epoch(train) [59][ 260/1879] lr: 2.0000e-03 eta: 8:05:15 time: 0.4472 data_time: 0.0385 memory: 6717 grad_norm: 3.0960 loss: 1.4187 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.4187 2023/04/14 08:11:27 - mmengine - INFO - Epoch(train) [59][ 280/1879] lr: 2.0000e-03 eta: 8:05:07 time: 0.3028 data_time: 0.0132 memory: 6717 grad_norm: 3.0176 loss: 1.0954 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0954 2023/04/14 08:11:35 - mmengine - INFO - Epoch(train) [59][ 300/1879] lr: 2.0000e-03 eta: 8:05:00 time: 0.4035 data_time: 0.0160 memory: 6717 grad_norm: 3.0356 loss: 1.1211 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1211 2023/04/14 08:11:43 - mmengine - INFO - Epoch(train) [59][ 320/1879] lr: 2.0000e-03 eta: 8:04:53 time: 0.3606 data_time: 0.0137 memory: 6717 grad_norm: 3.0466 loss: 1.3367 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3367 2023/04/14 08:11:51 - mmengine - INFO - Epoch(train) [59][ 340/1879] lr: 2.0000e-03 eta: 8:04:46 time: 0.3942 data_time: 0.0947 memory: 6717 grad_norm: 3.1026 loss: 1.1759 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1759 2023/04/14 08:11:58 - mmengine - INFO - Epoch(train) [59][ 360/1879] lr: 2.0000e-03 eta: 8:04:38 time: 0.3611 data_time: 0.0597 memory: 6717 grad_norm: 3.1452 loss: 1.2128 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2128 2023/04/14 08:12:06 - mmengine - INFO - Epoch(train) [59][ 380/1879] lr: 2.0000e-03 eta: 8:04:31 time: 0.4076 data_time: 0.0152 memory: 6717 grad_norm: 3.1517 loss: 1.1445 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1445 2023/04/14 08:12:13 - mmengine - INFO - Epoch(train) [59][ 400/1879] lr: 2.0000e-03 eta: 8:04:23 time: 0.3312 data_time: 0.0141 memory: 6717 grad_norm: 3.0162 loss: 1.3428 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3428 2023/04/14 08:12:20 - mmengine - INFO - Epoch(train) [59][ 420/1879] lr: 2.0000e-03 eta: 8:04:16 time: 0.3469 data_time: 0.0180 memory: 6717 grad_norm: 3.1035 loss: 1.0488 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.0488 2023/04/14 08:12:27 - mmengine - INFO - Epoch(train) [59][ 440/1879] lr: 2.0000e-03 eta: 8:04:08 time: 0.3652 data_time: 0.0143 memory: 6717 grad_norm: 3.0706 loss: 1.1073 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1073 2023/04/14 08:12:35 - mmengine - INFO - Epoch(train) [59][ 460/1879] lr: 2.0000e-03 eta: 8:04:01 time: 0.4079 data_time: 0.1180 memory: 6717 grad_norm: 3.0491 loss: 1.1297 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1297 2023/04/14 08:12:42 - mmengine - INFO - Epoch(train) [59][ 480/1879] lr: 2.0000e-03 eta: 8:03:53 time: 0.3369 data_time: 0.1913 memory: 6717 grad_norm: 3.0844 loss: 1.1035 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1035 2023/04/14 08:12:50 - mmengine - INFO - Epoch(train) [59][ 500/1879] lr: 2.0000e-03 eta: 8:03:47 time: 0.4377 data_time: 0.2959 memory: 6717 grad_norm: 3.1609 loss: 1.1060 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1060 2023/04/14 08:12:57 - mmengine - INFO - Epoch(train) [59][ 520/1879] lr: 2.0000e-03 eta: 8:03:39 time: 0.3090 data_time: 0.1679 memory: 6717 grad_norm: 3.1560 loss: 1.2419 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2419 2023/04/14 08:13:05 - mmengine - INFO - Epoch(train) [59][ 540/1879] lr: 2.0000e-03 eta: 8:03:32 time: 0.4168 data_time: 0.2733 memory: 6717 grad_norm: 3.0775 loss: 1.2732 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2732 2023/04/14 08:13:12 - mmengine - INFO - Epoch(train) [59][ 560/1879] lr: 2.0000e-03 eta: 8:03:24 time: 0.3698 data_time: 0.2292 memory: 6717 grad_norm: 3.0235 loss: 1.2454 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2454 2023/04/14 08:13:21 - mmengine - INFO - Epoch(train) [59][ 580/1879] lr: 2.0000e-03 eta: 8:03:18 time: 0.4219 data_time: 0.2787 memory: 6717 grad_norm: 3.0598 loss: 1.3524 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3524 2023/04/14 08:13:27 - mmengine - INFO - Epoch(train) [59][ 600/1879] lr: 2.0000e-03 eta: 8:03:10 time: 0.3266 data_time: 0.1858 memory: 6717 grad_norm: 3.0829 loss: 1.2106 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2106 2023/04/14 08:13:37 - mmengine - INFO - Epoch(train) [59][ 620/1879] lr: 2.0000e-03 eta: 8:03:04 time: 0.4701 data_time: 0.3328 memory: 6717 grad_norm: 3.0775 loss: 1.2112 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2112 2023/04/14 08:13:43 - mmengine - INFO - Epoch(train) [59][ 640/1879] lr: 2.0000e-03 eta: 8:02:55 time: 0.2862 data_time: 0.1492 memory: 6717 grad_norm: 3.1315 loss: 1.2623 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2623 2023/04/14 08:13:51 - mmengine - INFO - Epoch(train) [59][ 660/1879] lr: 2.0000e-03 eta: 8:02:49 time: 0.4423 data_time: 0.3019 memory: 6717 grad_norm: 2.9906 loss: 1.1606 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1606 2023/04/14 08:13:57 - mmengine - INFO - Epoch(train) [59][ 680/1879] lr: 2.0000e-03 eta: 8:02:40 time: 0.3052 data_time: 0.1670 memory: 6717 grad_norm: 3.1365 loss: 1.2865 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2865 2023/04/14 08:14:06 - mmengine - INFO - Epoch(train) [59][ 700/1879] lr: 2.0000e-03 eta: 8:02:34 time: 0.4210 data_time: 0.2822 memory: 6717 grad_norm: 3.1209 loss: 1.2377 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2377 2023/04/14 08:14:13 - mmengine - INFO - Epoch(train) [59][ 720/1879] lr: 2.0000e-03 eta: 8:02:26 time: 0.3490 data_time: 0.2070 memory: 6717 grad_norm: 3.1318 loss: 1.2604 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2604 2023/04/14 08:14:21 - mmengine - INFO - Epoch(train) [59][ 740/1879] lr: 2.0000e-03 eta: 8:02:19 time: 0.3844 data_time: 0.2462 memory: 6717 grad_norm: 3.1402 loss: 1.2074 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2074 2023/04/14 08:14:26 - mmengine - INFO - Epoch(train) [59][ 760/1879] lr: 2.0000e-03 eta: 8:02:10 time: 0.2867 data_time: 0.1454 memory: 6717 grad_norm: 3.0118 loss: 1.0278 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0278 2023/04/14 08:14:34 - mmengine - INFO - Epoch(train) [59][ 780/1879] lr: 2.0000e-03 eta: 8:02:03 time: 0.3912 data_time: 0.2100 memory: 6717 grad_norm: 3.1551 loss: 1.1695 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1695 2023/04/14 08:14:42 - mmengine - INFO - Epoch(train) [59][ 800/1879] lr: 2.0000e-03 eta: 8:01:56 time: 0.3682 data_time: 0.0999 memory: 6717 grad_norm: 3.0824 loss: 1.1970 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1970 2023/04/14 08:14:50 - mmengine - INFO - Epoch(train) [59][ 820/1879] lr: 2.0000e-03 eta: 8:01:49 time: 0.4029 data_time: 0.0791 memory: 6717 grad_norm: 3.2123 loss: 1.3458 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3458 2023/04/14 08:14:56 - mmengine - INFO - Epoch(train) [59][ 840/1879] lr: 2.0000e-03 eta: 8:01:41 time: 0.3442 data_time: 0.0614 memory: 6717 grad_norm: 3.1370 loss: 1.2228 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2228 2023/04/14 08:15:04 - mmengine - INFO - Epoch(train) [59][ 860/1879] lr: 2.0000e-03 eta: 8:01:34 time: 0.3944 data_time: 0.0945 memory: 6717 grad_norm: 3.0592 loss: 1.3570 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3570 2023/04/14 08:15:11 - mmengine - INFO - Epoch(train) [59][ 880/1879] lr: 2.0000e-03 eta: 8:01:26 time: 0.3227 data_time: 0.1030 memory: 6717 grad_norm: 3.1008 loss: 1.2177 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2177 2023/04/14 08:15:19 - mmengine - INFO - Epoch(train) [59][ 900/1879] lr: 2.0000e-03 eta: 8:01:19 time: 0.4040 data_time: 0.1362 memory: 6717 grad_norm: 3.0028 loss: 0.9988 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9988 2023/04/14 08:15:25 - mmengine - INFO - Epoch(train) [59][ 920/1879] lr: 2.0000e-03 eta: 8:01:11 time: 0.3295 data_time: 0.0801 memory: 6717 grad_norm: 3.0996 loss: 1.1637 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1637 2023/04/14 08:15:33 - mmengine - INFO - Epoch(train) [59][ 940/1879] lr: 2.0000e-03 eta: 8:01:04 time: 0.3981 data_time: 0.0815 memory: 6717 grad_norm: 3.1467 loss: 1.2436 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2436 2023/04/14 08:15:40 - mmengine - INFO - Epoch(train) [59][ 960/1879] lr: 2.0000e-03 eta: 8:00:56 time: 0.3072 data_time: 0.0575 memory: 6717 grad_norm: 3.0426 loss: 1.0849 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0849 2023/04/14 08:15:48 - mmengine - INFO - Epoch(train) [59][ 980/1879] lr: 2.0000e-03 eta: 8:00:49 time: 0.4152 data_time: 0.2494 memory: 6717 grad_norm: 3.0310 loss: 1.1575 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1575 2023/04/14 08:15:54 - mmengine - INFO - Epoch(train) [59][1000/1879] lr: 2.0000e-03 eta: 8:00:41 time: 0.3169 data_time: 0.1769 memory: 6717 grad_norm: 3.1272 loss: 1.3713 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3713 2023/04/14 08:16:02 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 08:16:02 - mmengine - INFO - Epoch(train) [59][1020/1879] lr: 2.0000e-03 eta: 8:00:34 time: 0.3925 data_time: 0.2200 memory: 6717 grad_norm: 3.1992 loss: 1.1750 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1750 2023/04/14 08:16:09 - mmengine - INFO - Epoch(train) [59][1040/1879] lr: 2.0000e-03 eta: 8:00:26 time: 0.3622 data_time: 0.1438 memory: 6717 grad_norm: 3.0492 loss: 1.1071 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1071 2023/04/14 08:16:17 - mmengine - INFO - Epoch(train) [59][1060/1879] lr: 2.0000e-03 eta: 8:00:19 time: 0.3899 data_time: 0.1194 memory: 6717 grad_norm: 3.0731 loss: 1.2847 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2847 2023/04/14 08:16:25 - mmengine - INFO - Epoch(train) [59][1080/1879] lr: 2.0000e-03 eta: 8:00:12 time: 0.3829 data_time: 0.0135 memory: 6717 grad_norm: 3.1739 loss: 1.3848 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.3848 2023/04/14 08:16:32 - mmengine - INFO - Epoch(train) [59][1100/1879] lr: 2.0000e-03 eta: 8:00:04 time: 0.3701 data_time: 0.0168 memory: 6717 grad_norm: 3.1922 loss: 1.3312 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3312 2023/04/14 08:16:40 - mmengine - INFO - Epoch(train) [59][1120/1879] lr: 2.0000e-03 eta: 7:59:57 time: 0.3707 data_time: 0.0247 memory: 6717 grad_norm: 3.0966 loss: 1.2522 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2522 2023/04/14 08:16:47 - mmengine - INFO - Epoch(train) [59][1140/1879] lr: 2.0000e-03 eta: 7:59:50 time: 0.3621 data_time: 0.0157 memory: 6717 grad_norm: 3.0787 loss: 1.1333 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1333 2023/04/14 08:16:54 - mmengine - INFO - Epoch(train) [59][1160/1879] lr: 2.0000e-03 eta: 7:59:42 time: 0.3742 data_time: 0.0125 memory: 6717 grad_norm: 3.0556 loss: 1.1532 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1532 2023/04/14 08:17:01 - mmengine - INFO - Epoch(train) [59][1180/1879] lr: 2.0000e-03 eta: 7:59:35 time: 0.3534 data_time: 0.0150 memory: 6717 grad_norm: 3.0898 loss: 1.1058 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1058 2023/04/14 08:17:09 - mmengine - INFO - Epoch(train) [59][1200/1879] lr: 2.0000e-03 eta: 7:59:27 time: 0.3870 data_time: 0.0148 memory: 6717 grad_norm: 3.1196 loss: 1.0066 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0066 2023/04/14 08:17:16 - mmengine - INFO - Epoch(train) [59][1220/1879] lr: 2.0000e-03 eta: 7:59:19 time: 0.3374 data_time: 0.0132 memory: 6717 grad_norm: 3.1677 loss: 1.1590 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1590 2023/04/14 08:17:23 - mmengine - INFO - Epoch(train) [59][1240/1879] lr: 2.0000e-03 eta: 7:59:12 time: 0.3727 data_time: 0.0150 memory: 6717 grad_norm: 4.2988 loss: 1.2673 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2673 2023/04/14 08:17:30 - mmengine - INFO - Epoch(train) [59][1260/1879] lr: 2.0000e-03 eta: 7:59:04 time: 0.3470 data_time: 0.0138 memory: 6717 grad_norm: 3.1458 loss: 1.2846 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2846 2023/04/14 08:17:38 - mmengine - INFO - Epoch(train) [59][1280/1879] lr: 2.0000e-03 eta: 7:58:57 time: 0.3872 data_time: 0.0135 memory: 6717 grad_norm: 3.1023 loss: 1.2396 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2396 2023/04/14 08:17:45 - mmengine - INFO - Epoch(train) [59][1300/1879] lr: 2.0000e-03 eta: 7:58:50 time: 0.3499 data_time: 0.0144 memory: 6717 grad_norm: 3.0754 loss: 1.1886 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1886 2023/04/14 08:17:53 - mmengine - INFO - Epoch(train) [59][1320/1879] lr: 2.0000e-03 eta: 7:58:42 time: 0.3751 data_time: 0.0144 memory: 6717 grad_norm: 3.0739 loss: 1.2246 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2246 2023/04/14 08:18:01 - mmengine - INFO - Epoch(train) [59][1340/1879] lr: 2.0000e-03 eta: 7:58:35 time: 0.4073 data_time: 0.0146 memory: 6717 grad_norm: 3.1175 loss: 0.9025 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9025 2023/04/14 08:18:07 - mmengine - INFO - Epoch(train) [59][1360/1879] lr: 2.0000e-03 eta: 7:58:27 time: 0.3273 data_time: 0.0130 memory: 6717 grad_norm: 3.1131 loss: 1.2204 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2204 2023/04/14 08:18:14 - mmengine - INFO - Epoch(train) [59][1380/1879] lr: 2.0000e-03 eta: 7:58:20 time: 0.3474 data_time: 0.0148 memory: 6717 grad_norm: 3.1554 loss: 1.2561 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2561 2023/04/14 08:18:23 - mmengine - INFO - Epoch(train) [59][1400/1879] lr: 2.0000e-03 eta: 7:58:13 time: 0.4133 data_time: 0.0138 memory: 6717 grad_norm: 3.1372 loss: 1.2967 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2967 2023/04/14 08:18:29 - mmengine - INFO - Epoch(train) [59][1420/1879] lr: 2.0000e-03 eta: 7:58:05 time: 0.3319 data_time: 0.0146 memory: 6717 grad_norm: 3.0604 loss: 1.3254 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3254 2023/04/14 08:18:38 - mmengine - INFO - Epoch(train) [59][1440/1879] lr: 2.0000e-03 eta: 7:57:58 time: 0.4246 data_time: 0.0139 memory: 6717 grad_norm: 3.1365 loss: 1.2756 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.2756 2023/04/14 08:18:44 - mmengine - INFO - Epoch(train) [59][1460/1879] lr: 2.0000e-03 eta: 7:57:50 time: 0.3315 data_time: 0.0157 memory: 6717 grad_norm: 3.0213 loss: 1.0886 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0886 2023/04/14 08:18:52 - mmengine - INFO - Epoch(train) [59][1480/1879] lr: 2.0000e-03 eta: 7:57:43 time: 0.4095 data_time: 0.0151 memory: 6717 grad_norm: 3.1455 loss: 1.2387 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2387 2023/04/14 08:18:59 - mmengine - INFO - Epoch(train) [59][1500/1879] lr: 2.0000e-03 eta: 7:57:35 time: 0.3254 data_time: 0.0150 memory: 6717 grad_norm: 3.1694 loss: 1.2934 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2934 2023/04/14 08:19:07 - mmengine - INFO - Epoch(train) [59][1520/1879] lr: 2.0000e-03 eta: 7:57:28 time: 0.4043 data_time: 0.0139 memory: 6717 grad_norm: 3.1728 loss: 1.2819 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2819 2023/04/14 08:19:14 - mmengine - INFO - Epoch(train) [59][1540/1879] lr: 2.0000e-03 eta: 7:57:21 time: 0.3458 data_time: 0.0140 memory: 6717 grad_norm: 3.1004 loss: 1.1537 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1537 2023/04/14 08:19:21 - mmengine - INFO - Epoch(train) [59][1560/1879] lr: 2.0000e-03 eta: 7:57:13 time: 0.3503 data_time: 0.0156 memory: 6717 grad_norm: 3.1576 loss: 1.1193 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1193 2023/04/14 08:19:28 - mmengine - INFO - Epoch(train) [59][1580/1879] lr: 2.0000e-03 eta: 7:57:05 time: 0.3568 data_time: 0.0141 memory: 6717 grad_norm: 3.1180 loss: 1.0801 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0801 2023/04/14 08:19:36 - mmengine - INFO - Epoch(train) [59][1600/1879] lr: 2.0000e-03 eta: 7:56:58 time: 0.3881 data_time: 0.0144 memory: 6717 grad_norm: 3.1167 loss: 1.1485 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1485 2023/04/14 08:19:42 - mmengine - INFO - Epoch(train) [59][1620/1879] lr: 2.0000e-03 eta: 7:56:50 time: 0.3237 data_time: 0.0152 memory: 6717 grad_norm: 3.1249 loss: 1.1434 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1434 2023/04/14 08:19:51 - mmengine - INFO - Epoch(train) [59][1640/1879] lr: 2.0000e-03 eta: 7:56:44 time: 0.4247 data_time: 0.0131 memory: 6717 grad_norm: 3.0910 loss: 1.3003 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3003 2023/04/14 08:19:58 - mmengine - INFO - Epoch(train) [59][1660/1879] lr: 2.0000e-03 eta: 7:56:36 time: 0.3668 data_time: 0.0150 memory: 6717 grad_norm: 3.1134 loss: 1.1818 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1818 2023/04/14 08:20:05 - mmengine - INFO - Epoch(train) [59][1680/1879] lr: 2.0000e-03 eta: 7:56:29 time: 0.3513 data_time: 0.0139 memory: 6717 grad_norm: 3.1000 loss: 1.1292 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1292 2023/04/14 08:20:14 - mmengine - INFO - Epoch(train) [59][1700/1879] lr: 2.0000e-03 eta: 7:56:22 time: 0.4133 data_time: 0.0152 memory: 6717 grad_norm: 3.1243 loss: 1.1886 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1886 2023/04/14 08:20:20 - mmengine - INFO - Epoch(train) [59][1720/1879] lr: 2.0000e-03 eta: 7:56:14 time: 0.3295 data_time: 0.0143 memory: 6717 grad_norm: 3.1624 loss: 1.0896 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0896 2023/04/14 08:20:28 - mmengine - INFO - Epoch(train) [59][1740/1879] lr: 2.0000e-03 eta: 7:56:07 time: 0.4016 data_time: 0.0136 memory: 6717 grad_norm: 3.1479 loss: 1.4069 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.4069 2023/04/14 08:20:35 - mmengine - INFO - Epoch(train) [59][1760/1879] lr: 2.0000e-03 eta: 7:55:59 time: 0.3420 data_time: 0.0141 memory: 6717 grad_norm: 3.2178 loss: 1.3139 top1_acc: 0.3125 top5_acc: 0.5625 loss_cls: 1.3139 2023/04/14 08:20:43 - mmengine - INFO - Epoch(train) [59][1780/1879] lr: 2.0000e-03 eta: 7:55:52 time: 0.3762 data_time: 0.0155 memory: 6717 grad_norm: 3.0473 loss: 1.3339 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3339 2023/04/14 08:20:50 - mmengine - INFO - Epoch(train) [59][1800/1879] lr: 2.0000e-03 eta: 7:55:45 time: 0.3944 data_time: 0.0130 memory: 6717 grad_norm: 3.1497 loss: 1.2191 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2191 2023/04/14 08:20:57 - mmengine - INFO - Epoch(train) [59][1820/1879] lr: 2.0000e-03 eta: 7:55:37 time: 0.3236 data_time: 0.0156 memory: 6717 grad_norm: 3.0913 loss: 1.2409 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2409 2023/04/14 08:21:05 - mmengine - INFO - Epoch(train) [59][1840/1879] lr: 2.0000e-03 eta: 7:55:29 time: 0.3944 data_time: 0.0133 memory: 6717 grad_norm: 3.1211 loss: 1.1466 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1466 2023/04/14 08:21:12 - mmengine - INFO - Epoch(train) [59][1860/1879] lr: 2.0000e-03 eta: 7:55:22 time: 0.3460 data_time: 0.0154 memory: 6717 grad_norm: 3.1828 loss: 1.3440 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3440 2023/04/14 08:21:19 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 08:21:19 - mmengine - INFO - Epoch(train) [59][1879/1879] lr: 2.0000e-03 eta: 7:55:15 time: 0.3531 data_time: 0.0128 memory: 6717 grad_norm: 3.2248 loss: 1.1729 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.1729 2023/04/14 08:21:28 - mmengine - INFO - Epoch(val) [59][ 20/155] eta: 0:01:02 time: 0.4612 data_time: 0.4283 memory: 1391 2023/04/14 08:21:34 - mmengine - INFO - Epoch(val) [59][ 40/155] eta: 0:00:44 time: 0.3152 data_time: 0.2818 memory: 1391 2023/04/14 08:21:43 - mmengine - INFO - Epoch(val) [59][ 60/155] eta: 0:00:38 time: 0.4356 data_time: 0.4025 memory: 1391 2023/04/14 08:21:49 - mmengine - INFO - Epoch(val) [59][ 80/155] eta: 0:00:28 time: 0.3174 data_time: 0.2840 memory: 1391 2023/04/14 08:21:58 - mmengine - INFO - Epoch(val) [59][100/155] eta: 0:00:21 time: 0.4538 data_time: 0.4214 memory: 1391 2023/04/14 08:22:04 - mmengine - INFO - Epoch(val) [59][120/155] eta: 0:00:13 time: 0.2969 data_time: 0.2635 memory: 1391 2023/04/14 08:22:13 - mmengine - INFO - Epoch(val) [59][140/155] eta: 0:00:05 time: 0.4446 data_time: 0.4114 memory: 1391 2023/04/14 08:22:20 - mmengine - INFO - Epoch(val) [59][155/155] acc/top1: 0.6619 acc/top5: 0.8708 acc/mean1: 0.6618 data_time: 0.3669 time: 0.3992 2023/04/14 08:22:30 - mmengine - INFO - Epoch(train) [60][ 20/1879] lr: 2.0000e-03 eta: 7:55:09 time: 0.4840 data_time: 0.2356 memory: 6717 grad_norm: 3.1035 loss: 1.1521 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1521 2023/04/14 08:22:37 - mmengine - INFO - Epoch(train) [60][ 40/1879] lr: 2.0000e-03 eta: 7:55:01 time: 0.3307 data_time: 0.0197 memory: 6717 grad_norm: 3.0472 loss: 1.1395 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.1395 2023/04/14 08:22:45 - mmengine - INFO - Epoch(train) [60][ 60/1879] lr: 2.0000e-03 eta: 7:54:54 time: 0.3985 data_time: 0.0151 memory: 6717 grad_norm: 3.2125 loss: 1.1815 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1815 2023/04/14 08:22:51 - mmengine - INFO - Epoch(train) [60][ 80/1879] lr: 2.0000e-03 eta: 7:54:46 time: 0.3356 data_time: 0.0139 memory: 6717 grad_norm: 3.1148 loss: 1.1254 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1254 2023/04/14 08:23:00 - mmengine - INFO - Epoch(train) [60][ 100/1879] lr: 2.0000e-03 eta: 7:54:39 time: 0.4116 data_time: 0.0152 memory: 6717 grad_norm: 2.9811 loss: 1.2426 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2426 2023/04/14 08:23:06 - mmengine - INFO - Epoch(train) [60][ 120/1879] lr: 2.0000e-03 eta: 7:54:31 time: 0.3436 data_time: 0.0148 memory: 6717 grad_norm: 3.1156 loss: 1.4259 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.4259 2023/04/14 08:23:14 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 08:23:15 - mmengine - INFO - Epoch(train) [60][ 140/1879] lr: 2.0000e-03 eta: 7:54:25 time: 0.4455 data_time: 0.0137 memory: 6717 grad_norm: 3.1063 loss: 1.2188 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2188 2023/04/14 08:23:22 - mmengine - INFO - Epoch(train) [60][ 160/1879] lr: 2.0000e-03 eta: 7:54:17 time: 0.3420 data_time: 0.0143 memory: 6717 grad_norm: 3.1182 loss: 1.1235 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1235 2023/04/14 08:23:30 - mmengine - INFO - Epoch(train) [60][ 180/1879] lr: 2.0000e-03 eta: 7:54:10 time: 0.4083 data_time: 0.0130 memory: 6717 grad_norm: 3.1229 loss: 1.1527 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1527 2023/04/14 08:23:37 - mmengine - INFO - Epoch(train) [60][ 200/1879] lr: 2.0000e-03 eta: 7:54:02 time: 0.3256 data_time: 0.0156 memory: 6717 grad_norm: 3.0329 loss: 1.2150 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2150 2023/04/14 08:23:45 - mmengine - INFO - Epoch(train) [60][ 220/1879] lr: 2.0000e-03 eta: 7:53:55 time: 0.4115 data_time: 0.0146 memory: 6717 grad_norm: 3.1415 loss: 1.2100 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2100 2023/04/14 08:23:51 - mmengine - INFO - Epoch(train) [60][ 240/1879] lr: 2.0000e-03 eta: 7:53:47 time: 0.3154 data_time: 0.0150 memory: 6717 grad_norm: 3.1303 loss: 1.1731 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1731 2023/04/14 08:23:59 - mmengine - INFO - Epoch(train) [60][ 260/1879] lr: 2.0000e-03 eta: 7:53:40 time: 0.3955 data_time: 0.0140 memory: 6717 grad_norm: 3.0941 loss: 1.3277 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3277 2023/04/14 08:24:07 - mmengine - INFO - Epoch(train) [60][ 280/1879] lr: 2.0000e-03 eta: 7:53:33 time: 0.3656 data_time: 0.0149 memory: 6717 grad_norm: 3.0966 loss: 1.1457 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1457 2023/04/14 08:24:15 - mmengine - INFO - Epoch(train) [60][ 300/1879] lr: 2.0000e-03 eta: 7:53:26 time: 0.3941 data_time: 0.0136 memory: 6717 grad_norm: 3.1086 loss: 1.1638 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1638 2023/04/14 08:24:21 - mmengine - INFO - Epoch(train) [60][ 320/1879] lr: 2.0000e-03 eta: 7:53:18 time: 0.3273 data_time: 0.0141 memory: 6717 grad_norm: 3.1177 loss: 1.1924 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1924 2023/04/14 08:24:29 - mmengine - INFO - Epoch(train) [60][ 340/1879] lr: 2.0000e-03 eta: 7:53:11 time: 0.4027 data_time: 0.0142 memory: 6717 grad_norm: 3.1991 loss: 1.1037 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1037 2023/04/14 08:24:36 - mmengine - INFO - Epoch(train) [60][ 360/1879] lr: 2.0000e-03 eta: 7:53:03 time: 0.3463 data_time: 0.0151 memory: 6717 grad_norm: 3.1329 loss: 1.1696 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1696 2023/04/14 08:24:44 - mmengine - INFO - Epoch(train) [60][ 380/1879] lr: 2.0000e-03 eta: 7:52:56 time: 0.3940 data_time: 0.0156 memory: 6717 grad_norm: 3.0557 loss: 1.2007 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2007 2023/04/14 08:24:51 - mmengine - INFO - Epoch(train) [60][ 400/1879] lr: 2.0000e-03 eta: 7:52:48 time: 0.3304 data_time: 0.0129 memory: 6717 grad_norm: 3.1634 loss: 1.4078 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.4078 2023/04/14 08:24:58 - mmengine - INFO - Epoch(train) [60][ 420/1879] lr: 2.0000e-03 eta: 7:52:41 time: 0.3772 data_time: 0.0154 memory: 6717 grad_norm: 3.1973 loss: 1.0696 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0696 2023/04/14 08:25:05 - mmengine - INFO - Epoch(train) [60][ 440/1879] lr: 2.0000e-03 eta: 7:52:33 time: 0.3639 data_time: 0.0143 memory: 6717 grad_norm: 3.0769 loss: 1.2052 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2052 2023/04/14 08:25:13 - mmengine - INFO - Epoch(train) [60][ 460/1879] lr: 2.0000e-03 eta: 7:52:26 time: 0.4038 data_time: 0.0136 memory: 6717 grad_norm: 3.0666 loss: 1.3085 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3085 2023/04/14 08:25:19 - mmengine - INFO - Epoch(train) [60][ 480/1879] lr: 2.0000e-03 eta: 7:52:18 time: 0.2925 data_time: 0.0157 memory: 6717 grad_norm: 3.0866 loss: 1.1338 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1338 2023/04/14 08:25:28 - mmengine - INFO - Epoch(train) [60][ 500/1879] lr: 2.0000e-03 eta: 7:52:11 time: 0.4155 data_time: 0.0137 memory: 6717 grad_norm: 3.1747 loss: 1.2496 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2496 2023/04/14 08:25:34 - mmengine - INFO - Epoch(train) [60][ 520/1879] lr: 2.0000e-03 eta: 7:52:03 time: 0.3338 data_time: 0.0151 memory: 6717 grad_norm: 3.1303 loss: 1.3584 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3584 2023/04/14 08:25:42 - mmengine - INFO - Epoch(train) [60][ 540/1879] lr: 2.0000e-03 eta: 7:51:56 time: 0.4010 data_time: 0.0133 memory: 6717 grad_norm: 3.1261 loss: 1.2591 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2591 2023/04/14 08:25:49 - mmengine - INFO - Epoch(train) [60][ 560/1879] lr: 2.0000e-03 eta: 7:51:48 time: 0.3176 data_time: 0.0161 memory: 6717 grad_norm: 3.1256 loss: 1.2315 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2315 2023/04/14 08:25:57 - mmengine - INFO - Epoch(train) [60][ 580/1879] lr: 2.0000e-03 eta: 7:51:41 time: 0.4007 data_time: 0.0144 memory: 6717 grad_norm: 3.0415 loss: 1.4201 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4201 2023/04/14 08:26:04 - mmengine - INFO - Epoch(train) [60][ 600/1879] lr: 2.0000e-03 eta: 7:51:33 time: 0.3436 data_time: 0.0139 memory: 6717 grad_norm: 3.1812 loss: 1.1235 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1235 2023/04/14 08:26:11 - mmengine - INFO - Epoch(train) [60][ 620/1879] lr: 2.0000e-03 eta: 7:51:26 time: 0.3894 data_time: 0.0135 memory: 6717 grad_norm: 3.0796 loss: 1.2343 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2343 2023/04/14 08:26:18 - mmengine - INFO - Epoch(train) [60][ 640/1879] lr: 2.0000e-03 eta: 7:51:19 time: 0.3537 data_time: 0.0160 memory: 6717 grad_norm: 3.1153 loss: 1.1756 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1756 2023/04/14 08:26:26 - mmengine - INFO - Epoch(train) [60][ 660/1879] lr: 2.0000e-03 eta: 7:51:11 time: 0.3853 data_time: 0.0146 memory: 6717 grad_norm: 3.1231 loss: 1.0707 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0707 2023/04/14 08:26:33 - mmengine - INFO - Epoch(train) [60][ 680/1879] lr: 2.0000e-03 eta: 7:51:04 time: 0.3631 data_time: 0.0144 memory: 6717 grad_norm: 3.0986 loss: 1.3259 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.3259 2023/04/14 08:26:40 - mmengine - INFO - Epoch(train) [60][ 700/1879] lr: 2.0000e-03 eta: 7:50:56 time: 0.3343 data_time: 0.0143 memory: 6717 grad_norm: 3.1039 loss: 1.1364 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1364 2023/04/14 08:26:48 - mmengine - INFO - Epoch(train) [60][ 720/1879] lr: 2.0000e-03 eta: 7:50:49 time: 0.4070 data_time: 0.0135 memory: 6717 grad_norm: 3.1638 loss: 1.0819 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0819 2023/04/14 08:26:55 - mmengine - INFO - Epoch(train) [60][ 740/1879] lr: 2.0000e-03 eta: 7:50:41 time: 0.3300 data_time: 0.0140 memory: 6717 grad_norm: 3.1118 loss: 1.2111 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2111 2023/04/14 08:27:04 - mmengine - INFO - Epoch(train) [60][ 760/1879] lr: 2.0000e-03 eta: 7:50:35 time: 0.4401 data_time: 0.0141 memory: 6717 grad_norm: 3.0540 loss: 1.2994 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2994 2023/04/14 08:27:10 - mmengine - INFO - Epoch(train) [60][ 780/1879] lr: 2.0000e-03 eta: 7:50:26 time: 0.3082 data_time: 0.0145 memory: 6717 grad_norm: 3.1846 loss: 1.1553 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.1553 2023/04/14 08:27:17 - mmengine - INFO - Epoch(train) [60][ 800/1879] lr: 2.0000e-03 eta: 7:50:19 time: 0.3703 data_time: 0.0136 memory: 6717 grad_norm: 3.2126 loss: 1.3459 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3459 2023/04/14 08:27:24 - mmengine - INFO - Epoch(train) [60][ 820/1879] lr: 2.0000e-03 eta: 7:50:11 time: 0.3441 data_time: 0.0154 memory: 6717 grad_norm: 3.0771 loss: 1.2461 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2461 2023/04/14 08:27:33 - mmengine - INFO - Epoch(train) [60][ 840/1879] lr: 2.0000e-03 eta: 7:50:05 time: 0.4506 data_time: 0.0130 memory: 6717 grad_norm: 3.0058 loss: 1.1183 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1183 2023/04/14 08:27:39 - mmengine - INFO - Epoch(train) [60][ 860/1879] lr: 2.0000e-03 eta: 7:49:57 time: 0.3042 data_time: 0.0159 memory: 6717 grad_norm: 3.0802 loss: 1.2374 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.2374 2023/04/14 08:27:48 - mmengine - INFO - Epoch(train) [60][ 880/1879] lr: 2.0000e-03 eta: 7:49:50 time: 0.4280 data_time: 0.0131 memory: 6717 grad_norm: 3.1422 loss: 1.3811 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3811 2023/04/14 08:27:54 - mmengine - INFO - Epoch(train) [60][ 900/1879] lr: 2.0000e-03 eta: 7:49:42 time: 0.3196 data_time: 0.0178 memory: 6717 grad_norm: 3.0974 loss: 1.2943 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2943 2023/04/14 08:28:03 - mmengine - INFO - Epoch(train) [60][ 920/1879] lr: 2.0000e-03 eta: 7:49:35 time: 0.4343 data_time: 0.0125 memory: 6717 grad_norm: 3.0328 loss: 1.0499 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0499 2023/04/14 08:28:09 - mmengine - INFO - Epoch(train) [60][ 940/1879] lr: 2.0000e-03 eta: 7:49:27 time: 0.3192 data_time: 0.0151 memory: 6717 grad_norm: 3.0878 loss: 1.1717 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1717 2023/04/14 08:28:17 - mmengine - INFO - Epoch(train) [60][ 960/1879] lr: 2.0000e-03 eta: 7:49:20 time: 0.4031 data_time: 0.0127 memory: 6717 grad_norm: 3.0679 loss: 1.2631 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2631 2023/04/14 08:28:24 - mmengine - INFO - Epoch(train) [60][ 980/1879] lr: 2.0000e-03 eta: 7:49:12 time: 0.3145 data_time: 0.0151 memory: 6717 grad_norm: 3.1822 loss: 1.1868 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1868 2023/04/14 08:28:32 - mmengine - INFO - Epoch(train) [60][1000/1879] lr: 2.0000e-03 eta: 7:49:05 time: 0.3972 data_time: 0.0126 memory: 6717 grad_norm: 3.1085 loss: 1.1644 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1644 2023/04/14 08:28:40 - mmengine - INFO - Epoch(train) [60][1020/1879] lr: 2.0000e-03 eta: 7:48:58 time: 0.3955 data_time: 0.0203 memory: 6717 grad_norm: 3.1739 loss: 1.2767 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2767 2023/04/14 08:28:46 - mmengine - INFO - Epoch(train) [60][1040/1879] lr: 2.0000e-03 eta: 7:48:50 time: 0.3246 data_time: 0.0132 memory: 6717 grad_norm: 3.0425 loss: 1.3693 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3693 2023/04/14 08:28:54 - mmengine - INFO - Epoch(train) [60][1060/1879] lr: 2.0000e-03 eta: 7:48:43 time: 0.3907 data_time: 0.0197 memory: 6717 grad_norm: 3.1440 loss: 1.1862 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1862 2023/04/14 08:29:01 - mmengine - INFO - Epoch(train) [60][1080/1879] lr: 2.0000e-03 eta: 7:48:35 time: 0.3526 data_time: 0.0213 memory: 6717 grad_norm: 3.1390 loss: 1.2078 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2078 2023/04/14 08:29:09 - mmengine - INFO - Epoch(train) [60][1100/1879] lr: 2.0000e-03 eta: 7:48:28 time: 0.4006 data_time: 0.0736 memory: 6717 grad_norm: 3.0915 loss: 1.2484 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2484 2023/04/14 08:29:16 - mmengine - INFO - Epoch(train) [60][1120/1879] lr: 2.0000e-03 eta: 7:48:20 time: 0.3348 data_time: 0.0747 memory: 6717 grad_norm: 3.0936 loss: 1.1752 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1752 2023/04/14 08:29:23 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 08:29:23 - mmengine - INFO - Epoch(train) [60][1140/1879] lr: 2.0000e-03 eta: 7:48:13 time: 0.3921 data_time: 0.1331 memory: 6717 grad_norm: 3.1501 loss: 1.1354 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1354 2023/04/14 08:29:30 - mmengine - INFO - Epoch(train) [60][1160/1879] lr: 2.0000e-03 eta: 7:48:05 time: 0.3173 data_time: 0.0628 memory: 6717 grad_norm: 3.0418 loss: 1.3656 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3656 2023/04/14 08:29:38 - mmengine - INFO - Epoch(train) [60][1180/1879] lr: 2.0000e-03 eta: 7:47:58 time: 0.4078 data_time: 0.1276 memory: 6717 grad_norm: 3.0354 loss: 1.1733 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1733 2023/04/14 08:29:45 - mmengine - INFO - Epoch(train) [60][1200/1879] lr: 2.0000e-03 eta: 7:47:50 time: 0.3368 data_time: 0.0958 memory: 6717 grad_norm: 3.0906 loss: 1.3222 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3222 2023/04/14 08:29:53 - mmengine - INFO - Epoch(train) [60][1220/1879] lr: 2.0000e-03 eta: 7:47:44 time: 0.4057 data_time: 0.1034 memory: 6717 grad_norm: 3.1276 loss: 1.1939 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1939 2023/04/14 08:30:00 - mmengine - INFO - Epoch(train) [60][1240/1879] lr: 2.0000e-03 eta: 7:47:36 time: 0.3417 data_time: 0.1039 memory: 6717 grad_norm: 3.1695 loss: 1.2666 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2666 2023/04/14 08:30:08 - mmengine - INFO - Epoch(train) [60][1260/1879] lr: 2.0000e-03 eta: 7:47:29 time: 0.4138 data_time: 0.1576 memory: 6717 grad_norm: 3.1969 loss: 1.1485 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1485 2023/04/14 08:30:14 - mmengine - INFO - Epoch(train) [60][1280/1879] lr: 2.0000e-03 eta: 7:47:21 time: 0.3143 data_time: 0.1384 memory: 6717 grad_norm: 3.1085 loss: 1.3317 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3317 2023/04/14 08:30:21 - mmengine - INFO - Epoch(train) [60][1300/1879] lr: 2.0000e-03 eta: 7:47:13 time: 0.3632 data_time: 0.1788 memory: 6717 grad_norm: 3.1632 loss: 1.4194 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4194 2023/04/14 08:30:28 - mmengine - INFO - Epoch(train) [60][1320/1879] lr: 2.0000e-03 eta: 7:47:05 time: 0.3169 data_time: 0.1357 memory: 6717 grad_norm: 3.0920 loss: 1.2479 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.2479 2023/04/14 08:30:36 - mmengine - INFO - Epoch(train) [60][1340/1879] lr: 2.0000e-03 eta: 7:46:58 time: 0.3911 data_time: 0.1971 memory: 6717 grad_norm: 3.1576 loss: 1.2644 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2644 2023/04/14 08:30:42 - mmengine - INFO - Epoch(train) [60][1360/1879] lr: 2.0000e-03 eta: 7:46:50 time: 0.3405 data_time: 0.1298 memory: 6717 grad_norm: 3.0263 loss: 1.1532 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1532 2023/04/14 08:30:51 - mmengine - INFO - Epoch(train) [60][1380/1879] lr: 2.0000e-03 eta: 7:46:43 time: 0.4109 data_time: 0.0557 memory: 6717 grad_norm: 3.1687 loss: 1.3336 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3336 2023/04/14 08:30:57 - mmengine - INFO - Epoch(train) [60][1400/1879] lr: 2.0000e-03 eta: 7:46:35 time: 0.3263 data_time: 0.0269 memory: 6717 grad_norm: 3.1673 loss: 1.3233 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3233 2023/04/14 08:31:05 - mmengine - INFO - Epoch(train) [60][1420/1879] lr: 2.0000e-03 eta: 7:46:28 time: 0.3863 data_time: 0.1432 memory: 6717 grad_norm: 3.1351 loss: 1.1717 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1717 2023/04/14 08:31:12 - mmengine - INFO - Epoch(train) [60][1440/1879] lr: 2.0000e-03 eta: 7:46:20 time: 0.3333 data_time: 0.0636 memory: 6717 grad_norm: 3.0890 loss: 1.1952 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1952 2023/04/14 08:31:20 - mmengine - INFO - Epoch(train) [60][1460/1879] lr: 2.0000e-03 eta: 7:46:13 time: 0.4066 data_time: 0.1272 memory: 6717 grad_norm: 3.0453 loss: 1.3180 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3180 2023/04/14 08:31:27 - mmengine - INFO - Epoch(train) [60][1480/1879] lr: 2.0000e-03 eta: 7:46:06 time: 0.3453 data_time: 0.0809 memory: 6717 grad_norm: 3.0704 loss: 1.3652 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3652 2023/04/14 08:31:34 - mmengine - INFO - Epoch(train) [60][1500/1879] lr: 2.0000e-03 eta: 7:45:58 time: 0.3714 data_time: 0.0839 memory: 6717 grad_norm: 3.0429 loss: 1.2149 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2149 2023/04/14 08:31:43 - mmengine - INFO - Epoch(train) [60][1520/1879] lr: 2.0000e-03 eta: 7:45:52 time: 0.4311 data_time: 0.0770 memory: 6717 grad_norm: 3.1443 loss: 0.9924 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9924 2023/04/14 08:31:49 - mmengine - INFO - Epoch(train) [60][1540/1879] lr: 2.0000e-03 eta: 7:45:44 time: 0.3273 data_time: 0.0312 memory: 6717 grad_norm: 3.0618 loss: 1.1804 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1804 2023/04/14 08:31:57 - mmengine - INFO - Epoch(train) [60][1560/1879] lr: 2.0000e-03 eta: 7:45:37 time: 0.4021 data_time: 0.0562 memory: 6717 grad_norm: 3.1024 loss: 1.2545 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2545 2023/04/14 08:32:04 - mmengine - INFO - Epoch(train) [60][1580/1879] lr: 2.0000e-03 eta: 7:45:29 time: 0.3501 data_time: 0.0174 memory: 6717 grad_norm: 3.1949 loss: 1.2497 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2497 2023/04/14 08:32:13 - mmengine - INFO - Epoch(train) [60][1600/1879] lr: 2.0000e-03 eta: 7:45:22 time: 0.4213 data_time: 0.0169 memory: 6717 grad_norm: 3.1101 loss: 1.2227 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2227 2023/04/14 08:32:19 - mmengine - INFO - Epoch(train) [60][1620/1879] lr: 2.0000e-03 eta: 7:45:14 time: 0.3013 data_time: 0.0270 memory: 6717 grad_norm: 3.1730 loss: 1.2040 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2040 2023/04/14 08:32:27 - mmengine - INFO - Epoch(train) [60][1640/1879] lr: 2.0000e-03 eta: 7:45:07 time: 0.4283 data_time: 0.0628 memory: 6717 grad_norm: 3.0661 loss: 1.1591 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1591 2023/04/14 08:32:34 - mmengine - INFO - Epoch(train) [60][1660/1879] lr: 2.0000e-03 eta: 7:44:59 time: 0.3217 data_time: 0.0133 memory: 6717 grad_norm: 3.1551 loss: 1.2535 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.2535 2023/04/14 08:32:42 - mmengine - INFO - Epoch(train) [60][1680/1879] lr: 2.0000e-03 eta: 7:44:53 time: 0.4132 data_time: 0.0932 memory: 6717 grad_norm: 3.2164 loss: 1.1985 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1985 2023/04/14 08:32:48 - mmengine - INFO - Epoch(train) [60][1700/1879] lr: 2.0000e-03 eta: 7:44:44 time: 0.3037 data_time: 0.0583 memory: 6717 grad_norm: 3.1182 loss: 1.3328 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3328 2023/04/14 08:32:57 - mmengine - INFO - Epoch(train) [60][1720/1879] lr: 2.0000e-03 eta: 7:44:38 time: 0.4268 data_time: 0.0147 memory: 6717 grad_norm: 3.2388 loss: 1.1504 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1504 2023/04/14 08:33:02 - mmengine - INFO - Epoch(train) [60][1740/1879] lr: 2.0000e-03 eta: 7:44:29 time: 0.2829 data_time: 0.0144 memory: 6717 grad_norm: 3.0967 loss: 1.2779 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.2779 2023/04/14 08:33:11 - mmengine - INFO - Epoch(train) [60][1760/1879] lr: 2.0000e-03 eta: 7:44:23 time: 0.4498 data_time: 0.0674 memory: 6717 grad_norm: 3.0494 loss: 1.1866 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1866 2023/04/14 08:33:17 - mmengine - INFO - Epoch(train) [60][1780/1879] lr: 2.0000e-03 eta: 7:44:14 time: 0.3021 data_time: 0.0131 memory: 6717 grad_norm: 3.1842 loss: 1.2848 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2848 2023/04/14 08:33:26 - mmengine - INFO - Epoch(train) [60][1800/1879] lr: 2.0000e-03 eta: 7:44:08 time: 0.4277 data_time: 0.0151 memory: 6717 grad_norm: 3.2427 loss: 1.0979 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0979 2023/04/14 08:33:32 - mmengine - INFO - Epoch(train) [60][1820/1879] lr: 2.0000e-03 eta: 7:43:59 time: 0.3060 data_time: 0.0137 memory: 6717 grad_norm: 3.0264 loss: 1.0800 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0800 2023/04/14 08:33:40 - mmengine - INFO - Epoch(train) [60][1840/1879] lr: 2.0000e-03 eta: 7:43:52 time: 0.3856 data_time: 0.0146 memory: 6717 grad_norm: 3.1252 loss: 1.1515 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1515 2023/04/14 08:33:47 - mmengine - INFO - Epoch(train) [60][1860/1879] lr: 2.0000e-03 eta: 7:43:45 time: 0.3599 data_time: 0.0146 memory: 6717 grad_norm: 3.0861 loss: 1.3902 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3902 2023/04/14 08:33:54 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 08:33:54 - mmengine - INFO - Epoch(train) [60][1879/1879] lr: 2.0000e-03 eta: 7:43:37 time: 0.3353 data_time: 0.0124 memory: 6717 grad_norm: 3.1939 loss: 1.2965 top1_acc: 0.4286 top5_acc: 0.5714 loss_cls: 1.2965 2023/04/14 08:33:54 - mmengine - INFO - Saving checkpoint at 60 epochs 2023/04/14 08:34:03 - mmengine - INFO - Epoch(val) [60][ 20/155] eta: 0:01:01 time: 0.4573 data_time: 0.4243 memory: 1391 2023/04/14 08:34:10 - mmengine - INFO - Epoch(val) [60][ 40/155] eta: 0:00:44 time: 0.3213 data_time: 0.2887 memory: 1391 2023/04/14 08:34:18 - mmengine - INFO - Epoch(val) [60][ 60/155] eta: 0:00:38 time: 0.4264 data_time: 0.3934 memory: 1391 2023/04/14 08:34:25 - mmengine - INFO - Epoch(val) [60][ 80/155] eta: 0:00:28 time: 0.3129 data_time: 0.2800 memory: 1391 2023/04/14 08:34:33 - mmengine - INFO - Epoch(val) [60][100/155] eta: 0:00:21 time: 0.4235 data_time: 0.3899 memory: 1391 2023/04/14 08:34:40 - mmengine - INFO - Epoch(val) [60][120/155] eta: 0:00:13 time: 0.3395 data_time: 0.3069 memory: 1391 2023/04/14 08:34:50 - mmengine - INFO - Epoch(val) [60][140/155] eta: 0:00:05 time: 0.4864 data_time: 0.4535 memory: 1391 2023/04/14 08:34:57 - mmengine - INFO - Epoch(val) [60][155/155] acc/top1: 0.6630 acc/top5: 0.8731 acc/mean1: 0.6629 data_time: 0.4188 time: 0.4502 2023/04/14 08:35:06 - mmengine - INFO - Epoch(train) [61][ 20/1879] lr: 2.0000e-03 eta: 7:43:31 time: 0.4835 data_time: 0.3256 memory: 6717 grad_norm: 3.0616 loss: 1.1254 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1254 2023/04/14 08:35:13 - mmengine - INFO - Epoch(train) [61][ 40/1879] lr: 2.0000e-03 eta: 7:43:23 time: 0.3250 data_time: 0.1857 memory: 6717 grad_norm: 3.0757 loss: 1.3228 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3228 2023/04/14 08:35:21 - mmengine - INFO - Epoch(train) [61][ 60/1879] lr: 2.0000e-03 eta: 7:43:17 time: 0.4112 data_time: 0.1837 memory: 6717 grad_norm: 3.0888 loss: 1.1107 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.1107 2023/04/14 08:35:27 - mmengine - INFO - Epoch(train) [61][ 80/1879] lr: 2.0000e-03 eta: 7:43:09 time: 0.3162 data_time: 0.0302 memory: 6717 grad_norm: 3.1312 loss: 1.1710 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1710 2023/04/14 08:35:36 - mmengine - INFO - Epoch(train) [61][ 100/1879] lr: 2.0000e-03 eta: 7:43:02 time: 0.4169 data_time: 0.0609 memory: 6717 grad_norm: 3.1419 loss: 1.3378 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3378 2023/04/14 08:35:42 - mmengine - INFO - Epoch(train) [61][ 120/1879] lr: 2.0000e-03 eta: 7:42:53 time: 0.3075 data_time: 0.0195 memory: 6717 grad_norm: 3.2523 loss: 1.3045 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3045 2023/04/14 08:35:50 - mmengine - INFO - Epoch(train) [61][ 140/1879] lr: 2.0000e-03 eta: 7:42:47 time: 0.4073 data_time: 0.0155 memory: 6717 grad_norm: 3.1889 loss: 1.1877 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1877 2023/04/14 08:35:57 - mmengine - INFO - Epoch(train) [61][ 160/1879] lr: 2.0000e-03 eta: 7:42:39 time: 0.3686 data_time: 0.0132 memory: 6717 grad_norm: 3.1766 loss: 1.1718 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1718 2023/04/14 08:36:05 - mmengine - INFO - Epoch(train) [61][ 180/1879] lr: 2.0000e-03 eta: 7:42:32 time: 0.3780 data_time: 0.0153 memory: 6717 grad_norm: 3.0882 loss: 1.1575 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1575 2023/04/14 08:36:12 - mmengine - INFO - Epoch(train) [61][ 200/1879] lr: 2.0000e-03 eta: 7:42:24 time: 0.3467 data_time: 0.0149 memory: 6717 grad_norm: 3.0594 loss: 1.0761 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0761 2023/04/14 08:36:20 - mmengine - INFO - Epoch(train) [61][ 220/1879] lr: 2.0000e-03 eta: 7:42:17 time: 0.3881 data_time: 0.0142 memory: 6717 grad_norm: 3.0764 loss: 1.1066 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1066 2023/04/14 08:36:28 - mmengine - INFO - Epoch(train) [61][ 240/1879] lr: 2.0000e-03 eta: 7:42:10 time: 0.4133 data_time: 0.0190 memory: 6717 grad_norm: 3.2480 loss: 1.2695 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2695 2023/04/14 08:36:34 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 08:36:34 - mmengine - INFO - Epoch(train) [61][ 260/1879] lr: 2.0000e-03 eta: 7:42:02 time: 0.3091 data_time: 0.0135 memory: 6717 grad_norm: 3.1820 loss: 1.3407 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3407 2023/04/14 08:36:43 - mmengine - INFO - Epoch(train) [61][ 280/1879] lr: 2.0000e-03 eta: 7:41:55 time: 0.4268 data_time: 0.0146 memory: 6717 grad_norm: 3.1940 loss: 1.2753 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2753 2023/04/14 08:36:49 - mmengine - INFO - Epoch(train) [61][ 300/1879] lr: 2.0000e-03 eta: 7:41:47 time: 0.3011 data_time: 0.0138 memory: 6717 grad_norm: 3.0857 loss: 1.2624 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2624 2023/04/14 08:36:57 - mmengine - INFO - Epoch(train) [61][ 320/1879] lr: 2.0000e-03 eta: 7:41:40 time: 0.4255 data_time: 0.0138 memory: 6717 grad_norm: 3.2032 loss: 1.2422 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2422 2023/04/14 08:37:03 - mmengine - INFO - Epoch(train) [61][ 340/1879] lr: 2.0000e-03 eta: 7:41:32 time: 0.3027 data_time: 0.0130 memory: 6717 grad_norm: 3.1449 loss: 1.3719 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.3719 2023/04/14 08:37:11 - mmengine - INFO - Epoch(train) [61][ 360/1879] lr: 2.0000e-03 eta: 7:41:25 time: 0.4014 data_time: 0.0155 memory: 6717 grad_norm: 3.0570 loss: 1.0163 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0163 2023/04/14 08:37:18 - mmengine - INFO - Epoch(train) [61][ 380/1879] lr: 2.0000e-03 eta: 7:41:17 time: 0.3319 data_time: 0.0396 memory: 6717 grad_norm: 3.1598 loss: 1.0841 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0841 2023/04/14 08:37:26 - mmengine - INFO - Epoch(train) [61][ 400/1879] lr: 2.0000e-03 eta: 7:41:10 time: 0.4123 data_time: 0.0258 memory: 6717 grad_norm: 3.2311 loss: 1.2312 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2312 2023/04/14 08:37:33 - mmengine - INFO - Epoch(train) [61][ 420/1879] lr: 2.0000e-03 eta: 7:41:03 time: 0.3469 data_time: 0.0135 memory: 6717 grad_norm: 3.1115 loss: 1.2324 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2324 2023/04/14 08:37:41 - mmengine - INFO - Epoch(train) [61][ 440/1879] lr: 2.0000e-03 eta: 7:40:55 time: 0.3830 data_time: 0.0156 memory: 6717 grad_norm: 3.0638 loss: 1.1398 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1398 2023/04/14 08:37:47 - mmengine - INFO - Epoch(train) [61][ 460/1879] lr: 2.0000e-03 eta: 7:40:47 time: 0.3274 data_time: 0.0153 memory: 6717 grad_norm: 3.0859 loss: 1.1420 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1420 2023/04/14 08:37:56 - mmengine - INFO - Epoch(train) [61][ 480/1879] lr: 2.0000e-03 eta: 7:40:41 time: 0.4331 data_time: 0.0146 memory: 6717 grad_norm: 3.2301 loss: 1.1789 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1789 2023/04/14 08:38:03 - mmengine - INFO - Epoch(train) [61][ 500/1879] lr: 2.0000e-03 eta: 7:40:33 time: 0.3276 data_time: 0.0138 memory: 6717 grad_norm: 3.2986 loss: 1.3981 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3981 2023/04/14 08:38:10 - mmengine - INFO - Epoch(train) [61][ 520/1879] lr: 2.0000e-03 eta: 7:40:26 time: 0.3877 data_time: 0.0150 memory: 6717 grad_norm: 3.0665 loss: 1.1076 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1076 2023/04/14 08:38:17 - mmengine - INFO - Epoch(train) [61][ 540/1879] lr: 2.0000e-03 eta: 7:40:18 time: 0.3341 data_time: 0.0366 memory: 6717 grad_norm: 3.1521 loss: 1.2747 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2747 2023/04/14 08:38:25 - mmengine - INFO - Epoch(train) [61][ 560/1879] lr: 2.0000e-03 eta: 7:40:11 time: 0.3942 data_time: 0.1025 memory: 6717 grad_norm: 3.1448 loss: 1.1144 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1144 2023/04/14 08:38:32 - mmengine - INFO - Epoch(train) [61][ 580/1879] lr: 2.0000e-03 eta: 7:40:03 time: 0.3422 data_time: 0.0634 memory: 6717 grad_norm: 3.1473 loss: 1.2816 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2816 2023/04/14 08:38:39 - mmengine - INFO - Epoch(train) [61][ 600/1879] lr: 2.0000e-03 eta: 7:39:55 time: 0.3482 data_time: 0.0230 memory: 6717 grad_norm: 3.0974 loss: 1.1343 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1343 2023/04/14 08:38:46 - mmengine - INFO - Epoch(train) [61][ 620/1879] lr: 2.0000e-03 eta: 7:39:48 time: 0.3793 data_time: 0.0418 memory: 6717 grad_norm: 3.0800 loss: 1.2075 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2075 2023/04/14 08:38:54 - mmengine - INFO - Epoch(train) [61][ 640/1879] lr: 2.0000e-03 eta: 7:39:41 time: 0.3753 data_time: 0.0162 memory: 6717 grad_norm: 3.1561 loss: 1.2016 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2016 2023/04/14 08:39:01 - mmengine - INFO - Epoch(train) [61][ 660/1879] lr: 2.0000e-03 eta: 7:39:33 time: 0.3713 data_time: 0.0270 memory: 6717 grad_norm: 3.1148 loss: 1.1666 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1666 2023/04/14 08:39:08 - mmengine - INFO - Epoch(train) [61][ 680/1879] lr: 2.0000e-03 eta: 7:39:26 time: 0.3600 data_time: 0.0336 memory: 6717 grad_norm: 3.0909 loss: 1.3034 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3034 2023/04/14 08:39:16 - mmengine - INFO - Epoch(train) [61][ 700/1879] lr: 2.0000e-03 eta: 7:39:19 time: 0.3867 data_time: 0.0335 memory: 6717 grad_norm: 3.1497 loss: 1.1936 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1936 2023/04/14 08:39:23 - mmengine - INFO - Epoch(train) [61][ 720/1879] lr: 2.0000e-03 eta: 7:39:11 time: 0.3560 data_time: 0.0801 memory: 6717 grad_norm: 3.1703 loss: 1.2169 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2169 2023/04/14 08:39:31 - mmengine - INFO - Epoch(train) [61][ 740/1879] lr: 2.0000e-03 eta: 7:39:03 time: 0.3601 data_time: 0.1401 memory: 6717 grad_norm: 3.1835 loss: 1.2248 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2248 2023/04/14 08:39:39 - mmengine - INFO - Epoch(train) [61][ 760/1879] lr: 2.0000e-03 eta: 7:38:57 time: 0.4153 data_time: 0.2647 memory: 6717 grad_norm: 3.1454 loss: 1.2722 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2722 2023/04/14 08:39:45 - mmengine - INFO - Epoch(train) [61][ 780/1879] lr: 2.0000e-03 eta: 7:38:49 time: 0.3299 data_time: 0.1902 memory: 6717 grad_norm: 3.2167 loss: 1.2634 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2634 2023/04/14 08:39:53 - mmengine - INFO - Epoch(train) [61][ 800/1879] lr: 2.0000e-03 eta: 7:38:42 time: 0.3920 data_time: 0.2435 memory: 6717 grad_norm: 3.1516 loss: 1.2486 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2486 2023/04/14 08:40:00 - mmengine - INFO - Epoch(train) [61][ 820/1879] lr: 2.0000e-03 eta: 7:38:34 time: 0.3365 data_time: 0.1530 memory: 6717 grad_norm: 3.0337 loss: 1.3166 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3166 2023/04/14 08:40:08 - mmengine - INFO - Epoch(train) [61][ 840/1879] lr: 2.0000e-03 eta: 7:38:27 time: 0.4139 data_time: 0.2688 memory: 6717 grad_norm: 3.1608 loss: 1.2334 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2334 2023/04/14 08:40:16 - mmengine - INFO - Epoch(train) [61][ 860/1879] lr: 2.0000e-03 eta: 7:38:19 time: 0.3659 data_time: 0.2235 memory: 6717 grad_norm: 3.1461 loss: 1.1465 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1465 2023/04/14 08:40:24 - mmengine - INFO - Epoch(train) [61][ 880/1879] lr: 2.0000e-03 eta: 7:38:12 time: 0.3967 data_time: 0.2528 memory: 6717 grad_norm: 3.1050 loss: 1.2384 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2384 2023/04/14 08:40:30 - mmengine - INFO - Epoch(train) [61][ 900/1879] lr: 2.0000e-03 eta: 7:38:04 time: 0.3280 data_time: 0.1864 memory: 6717 grad_norm: 3.1353 loss: 1.0262 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0262 2023/04/14 08:40:38 - mmengine - INFO - Epoch(train) [61][ 920/1879] lr: 2.0000e-03 eta: 7:37:57 time: 0.3740 data_time: 0.2314 memory: 6717 grad_norm: 3.1401 loss: 1.3430 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3430 2023/04/14 08:40:45 - mmengine - INFO - Epoch(train) [61][ 940/1879] lr: 2.0000e-03 eta: 7:37:49 time: 0.3430 data_time: 0.1961 memory: 6717 grad_norm: 3.2403 loss: 1.3404 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3404 2023/04/14 08:40:52 - mmengine - INFO - Epoch(train) [61][ 960/1879] lr: 2.0000e-03 eta: 7:37:42 time: 0.3836 data_time: 0.2233 memory: 6717 grad_norm: 3.1301 loss: 1.2267 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2267 2023/04/14 08:40:59 - mmengine - INFO - Epoch(train) [61][ 980/1879] lr: 2.0000e-03 eta: 7:37:34 time: 0.3374 data_time: 0.1379 memory: 6717 grad_norm: 3.0993 loss: 1.1750 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1750 2023/04/14 08:41:08 - mmengine - INFO - Epoch(train) [61][1000/1879] lr: 2.0000e-03 eta: 7:37:28 time: 0.4631 data_time: 0.3002 memory: 6717 grad_norm: 3.0357 loss: 1.1796 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1796 2023/04/14 08:41:15 - mmengine - INFO - Epoch(train) [61][1020/1879] lr: 2.0000e-03 eta: 7:37:20 time: 0.3185 data_time: 0.1769 memory: 6717 grad_norm: 3.1681 loss: 1.2321 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2321 2023/04/14 08:41:22 - mmengine - INFO - Epoch(train) [61][1040/1879] lr: 2.0000e-03 eta: 7:37:13 time: 0.3651 data_time: 0.1218 memory: 6717 grad_norm: 3.2831 loss: 1.3113 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3113 2023/04/14 08:41:29 - mmengine - INFO - Epoch(train) [61][1060/1879] lr: 2.0000e-03 eta: 7:37:05 time: 0.3624 data_time: 0.0969 memory: 6717 grad_norm: 3.1306 loss: 1.2647 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2647 2023/04/14 08:41:38 - mmengine - INFO - Epoch(train) [61][1080/1879] lr: 2.0000e-03 eta: 7:36:58 time: 0.4274 data_time: 0.2867 memory: 6717 grad_norm: 3.1321 loss: 1.2150 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2150 2023/04/14 08:41:44 - mmengine - INFO - Epoch(train) [61][1100/1879] lr: 2.0000e-03 eta: 7:36:50 time: 0.3142 data_time: 0.1759 memory: 6717 grad_norm: 3.0710 loss: 1.2700 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2700 2023/04/14 08:41:53 - mmengine - INFO - Epoch(train) [61][1120/1879] lr: 2.0000e-03 eta: 7:36:44 time: 0.4345 data_time: 0.2934 memory: 6717 grad_norm: 3.1326 loss: 1.3753 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3753 2023/04/14 08:41:58 - mmengine - INFO - Epoch(train) [61][1140/1879] lr: 2.0000e-03 eta: 7:36:35 time: 0.2858 data_time: 0.1467 memory: 6717 grad_norm: 3.2062 loss: 1.2331 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2331 2023/04/14 08:42:06 - mmengine - INFO - Epoch(train) [61][1160/1879] lr: 2.0000e-03 eta: 7:36:28 time: 0.4021 data_time: 0.2497 memory: 6717 grad_norm: 3.1132 loss: 1.2501 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2501 2023/04/14 08:42:13 - mmengine - INFO - Epoch(train) [61][1180/1879] lr: 2.0000e-03 eta: 7:36:20 time: 0.3278 data_time: 0.1867 memory: 6717 grad_norm: 3.1061 loss: 1.1996 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1996 2023/04/14 08:42:21 - mmengine - INFO - Epoch(train) [61][1200/1879] lr: 2.0000e-03 eta: 7:36:13 time: 0.4001 data_time: 0.2410 memory: 6717 grad_norm: 3.1793 loss: 1.1362 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1362 2023/04/14 08:42:28 - mmengine - INFO - Epoch(train) [61][1220/1879] lr: 2.0000e-03 eta: 7:36:06 time: 0.3504 data_time: 0.1553 memory: 6717 grad_norm: 3.1701 loss: 1.2617 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2617 2023/04/14 08:42:36 - mmengine - INFO - Epoch(train) [61][1240/1879] lr: 2.0000e-03 eta: 7:35:59 time: 0.4183 data_time: 0.2723 memory: 6717 grad_norm: 3.1555 loss: 1.2206 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2206 2023/04/14 08:42:43 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 08:42:43 - mmengine - INFO - Epoch(train) [61][1260/1879] lr: 2.0000e-03 eta: 7:35:51 time: 0.3151 data_time: 0.1756 memory: 6717 grad_norm: 3.2128 loss: 1.2005 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2005 2023/04/14 08:42:50 - mmengine - INFO - Epoch(train) [61][1280/1879] lr: 2.0000e-03 eta: 7:35:43 time: 0.3654 data_time: 0.2243 memory: 6717 grad_norm: 3.1791 loss: 1.2284 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2284 2023/04/14 08:42:56 - mmengine - INFO - Epoch(train) [61][1300/1879] lr: 2.0000e-03 eta: 7:35:35 time: 0.3144 data_time: 0.1363 memory: 6717 grad_norm: 3.1226 loss: 1.3255 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3255 2023/04/14 08:43:05 - mmengine - INFO - Epoch(train) [61][1320/1879] lr: 2.0000e-03 eta: 7:35:28 time: 0.4233 data_time: 0.1489 memory: 6717 grad_norm: 3.0346 loss: 1.2391 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2391 2023/04/14 08:43:11 - mmengine - INFO - Epoch(train) [61][1340/1879] lr: 2.0000e-03 eta: 7:35:20 time: 0.3350 data_time: 0.0624 memory: 6717 grad_norm: 3.1371 loss: 1.0839 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0839 2023/04/14 08:43:19 - mmengine - INFO - Epoch(train) [61][1360/1879] lr: 2.0000e-03 eta: 7:35:13 time: 0.3894 data_time: 0.1100 memory: 6717 grad_norm: 3.1348 loss: 1.2134 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2134 2023/04/14 08:43:27 - mmengine - INFO - Epoch(train) [61][1380/1879] lr: 2.0000e-03 eta: 7:35:06 time: 0.3961 data_time: 0.0131 memory: 6717 grad_norm: 3.2679 loss: 1.3456 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3456 2023/04/14 08:43:35 - mmengine - INFO - Epoch(train) [61][1400/1879] lr: 2.0000e-03 eta: 7:34:59 time: 0.3776 data_time: 0.0155 memory: 6717 grad_norm: 3.0344 loss: 1.1854 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1854 2023/04/14 08:43:42 - mmengine - INFO - Epoch(train) [61][1420/1879] lr: 2.0000e-03 eta: 7:34:52 time: 0.3811 data_time: 0.0148 memory: 6717 grad_norm: 3.0930 loss: 1.1175 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1175 2023/04/14 08:43:50 - mmengine - INFO - Epoch(train) [61][1440/1879] lr: 2.0000e-03 eta: 7:34:44 time: 0.3735 data_time: 0.0139 memory: 6717 grad_norm: 3.1327 loss: 1.1099 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1099 2023/04/14 08:43:56 - mmengine - INFO - Epoch(train) [61][1460/1879] lr: 2.0000e-03 eta: 7:34:36 time: 0.3285 data_time: 0.0144 memory: 6717 grad_norm: 3.1334 loss: 1.1900 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1900 2023/04/14 08:44:05 - mmengine - INFO - Epoch(train) [61][1480/1879] lr: 2.0000e-03 eta: 7:34:30 time: 0.4453 data_time: 0.0153 memory: 6717 grad_norm: 3.2243 loss: 1.2597 top1_acc: 0.3750 top5_acc: 0.9375 loss_cls: 1.2597 2023/04/14 08:44:12 - mmengine - INFO - Epoch(train) [61][1500/1879] lr: 2.0000e-03 eta: 7:34:22 time: 0.3384 data_time: 0.0137 memory: 6717 grad_norm: 3.1388 loss: 1.1611 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1611 2023/04/14 08:44:20 - mmengine - INFO - Epoch(train) [61][1520/1879] lr: 2.0000e-03 eta: 7:34:15 time: 0.4088 data_time: 0.0154 memory: 6717 grad_norm: 3.1232 loss: 1.2457 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2457 2023/04/14 08:44:27 - mmengine - INFO - Epoch(train) [61][1540/1879] lr: 2.0000e-03 eta: 7:34:08 time: 0.3461 data_time: 0.0143 memory: 6717 grad_norm: 3.0697 loss: 1.0982 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0982 2023/04/14 08:44:35 - mmengine - INFO - Epoch(train) [61][1560/1879] lr: 2.0000e-03 eta: 7:34:00 time: 0.3732 data_time: 0.0147 memory: 6717 grad_norm: 3.1590 loss: 1.1276 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1276 2023/04/14 08:44:41 - mmengine - INFO - Epoch(train) [61][1580/1879] lr: 2.0000e-03 eta: 7:33:52 time: 0.3258 data_time: 0.0153 memory: 6717 grad_norm: 3.1413 loss: 1.1391 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1391 2023/04/14 08:44:49 - mmengine - INFO - Epoch(train) [61][1600/1879] lr: 2.0000e-03 eta: 7:33:45 time: 0.4120 data_time: 0.0135 memory: 6717 grad_norm: 3.1666 loss: 1.1548 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1548 2023/04/14 08:44:56 - mmengine - INFO - Epoch(train) [61][1620/1879] lr: 2.0000e-03 eta: 7:33:37 time: 0.3177 data_time: 0.0160 memory: 6717 grad_norm: 3.1132 loss: 1.2414 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2414 2023/04/14 08:45:04 - mmengine - INFO - Epoch(train) [61][1640/1879] lr: 2.0000e-03 eta: 7:33:30 time: 0.4068 data_time: 0.0147 memory: 6717 grad_norm: 3.1561 loss: 1.0928 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0928 2023/04/14 08:45:10 - mmengine - INFO - Epoch(train) [61][1660/1879] lr: 2.0000e-03 eta: 7:33:22 time: 0.3168 data_time: 0.0133 memory: 6717 grad_norm: 3.0905 loss: 1.2821 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2821 2023/04/14 08:45:19 - mmengine - INFO - Epoch(train) [61][1680/1879] lr: 2.0000e-03 eta: 7:33:16 time: 0.4400 data_time: 0.0170 memory: 6717 grad_norm: 3.1287 loss: 1.2236 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2236 2023/04/14 08:45:25 - mmengine - INFO - Epoch(train) [61][1700/1879] lr: 2.0000e-03 eta: 7:33:08 time: 0.3095 data_time: 0.0132 memory: 6717 grad_norm: 3.2129 loss: 1.1279 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1279 2023/04/14 08:45:34 - mmengine - INFO - Epoch(train) [61][1720/1879] lr: 2.0000e-03 eta: 7:33:01 time: 0.4114 data_time: 0.0493 memory: 6717 grad_norm: 3.1582 loss: 1.3246 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.3246 2023/04/14 08:45:40 - mmengine - INFO - Epoch(train) [61][1740/1879] lr: 2.0000e-03 eta: 7:32:53 time: 0.3151 data_time: 0.0952 memory: 6717 grad_norm: 3.2364 loss: 1.2519 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2519 2023/04/14 08:45:49 - mmengine - INFO - Epoch(train) [61][1760/1879] lr: 2.0000e-03 eta: 7:32:46 time: 0.4496 data_time: 0.0857 memory: 6717 grad_norm: 3.0812 loss: 1.1116 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1116 2023/04/14 08:45:55 - mmengine - INFO - Epoch(train) [61][1780/1879] lr: 2.0000e-03 eta: 7:32:38 time: 0.3289 data_time: 0.0470 memory: 6717 grad_norm: 3.1517 loss: 1.2738 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2738 2023/04/14 08:46:03 - mmengine - INFO - Epoch(train) [61][1800/1879] lr: 2.0000e-03 eta: 7:32:31 time: 0.3808 data_time: 0.0511 memory: 6717 grad_norm: 3.1190 loss: 1.1930 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1930 2023/04/14 08:46:10 - mmengine - INFO - Epoch(train) [61][1820/1879] lr: 2.0000e-03 eta: 7:32:23 time: 0.3261 data_time: 0.1607 memory: 6717 grad_norm: 3.2424 loss: 1.1633 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1633 2023/04/14 08:46:18 - mmengine - INFO - Epoch(train) [61][1840/1879] lr: 2.0000e-03 eta: 7:32:16 time: 0.4060 data_time: 0.2313 memory: 6717 grad_norm: 3.1066 loss: 1.2099 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2099 2023/04/14 08:46:24 - mmengine - INFO - Epoch(train) [61][1860/1879] lr: 2.0000e-03 eta: 7:32:08 time: 0.3337 data_time: 0.1482 memory: 6717 grad_norm: 3.2005 loss: 1.1745 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.1745 2023/04/14 08:46:30 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 08:46:30 - mmengine - INFO - Epoch(train) [61][1879/1879] lr: 2.0000e-03 eta: 7:32:00 time: 0.2941 data_time: 0.1341 memory: 6717 grad_norm: 3.1965 loss: 1.2227 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.2227 2023/04/14 08:46:39 - mmengine - INFO - Epoch(val) [61][ 20/155] eta: 0:00:58 time: 0.4349 data_time: 0.4010 memory: 1391 2023/04/14 08:46:46 - mmengine - INFO - Epoch(val) [61][ 40/155] eta: 0:00:44 time: 0.3435 data_time: 0.3109 memory: 1391 2023/04/14 08:46:53 - mmengine - INFO - Epoch(val) [61][ 60/155] eta: 0:00:36 time: 0.3679 data_time: 0.3338 memory: 1391 2023/04/14 08:47:01 - mmengine - INFO - Epoch(val) [61][ 80/155] eta: 0:00:28 time: 0.3840 data_time: 0.3501 memory: 1391 2023/04/14 08:47:09 - mmengine - INFO - Epoch(val) [61][100/155] eta: 0:00:21 time: 0.4181 data_time: 0.3838 memory: 1391 2023/04/14 08:47:15 - mmengine - INFO - Epoch(val) [61][120/155] eta: 0:00:13 time: 0.3177 data_time: 0.2840 memory: 1391 2023/04/14 08:47:23 - mmengine - INFO - Epoch(val) [61][140/155] eta: 0:00:05 time: 0.3695 data_time: 0.3357 memory: 1391 2023/04/14 08:47:32 - mmengine - INFO - Epoch(val) [61][155/155] acc/top1: 0.6628 acc/top5: 0.8712 acc/mean1: 0.6627 data_time: 0.3091 time: 0.3420 2023/04/14 08:47:42 - mmengine - INFO - Epoch(train) [62][ 20/1879] lr: 2.0000e-03 eta: 7:31:55 time: 0.5079 data_time: 0.3134 memory: 6717 grad_norm: 3.1273 loss: 1.0904 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0904 2023/04/14 08:47:49 - mmengine - INFO - Epoch(train) [62][ 40/1879] lr: 2.0000e-03 eta: 7:31:47 time: 0.3500 data_time: 0.1012 memory: 6717 grad_norm: 3.1457 loss: 1.0797 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0797 2023/04/14 08:47:57 - mmengine - INFO - Epoch(train) [62][ 60/1879] lr: 2.0000e-03 eta: 7:31:40 time: 0.3994 data_time: 0.0311 memory: 6717 grad_norm: 3.1694 loss: 1.1331 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1331 2023/04/14 08:48:03 - mmengine - INFO - Epoch(train) [62][ 80/1879] lr: 2.0000e-03 eta: 7:31:32 time: 0.3285 data_time: 0.0546 memory: 6717 grad_norm: 3.1228 loss: 1.2604 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2604 2023/04/14 08:48:11 - mmengine - INFO - Epoch(train) [62][ 100/1879] lr: 2.0000e-03 eta: 7:31:25 time: 0.3885 data_time: 0.1139 memory: 6717 grad_norm: 3.2260 loss: 1.2161 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2161 2023/04/14 08:48:18 - mmengine - INFO - Epoch(train) [62][ 120/1879] lr: 2.0000e-03 eta: 7:31:17 time: 0.3140 data_time: 0.1477 memory: 6717 grad_norm: 3.1245 loss: 1.0914 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0914 2023/04/14 08:48:26 - mmengine - INFO - Epoch(train) [62][ 140/1879] lr: 2.0000e-03 eta: 7:31:10 time: 0.4143 data_time: 0.2676 memory: 6717 grad_norm: 3.1522 loss: 1.1685 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1685 2023/04/14 08:48:32 - mmengine - INFO - Epoch(train) [62][ 160/1879] lr: 2.0000e-03 eta: 7:31:02 time: 0.3192 data_time: 0.1402 memory: 6717 grad_norm: 3.0886 loss: 1.3515 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3515 2023/04/14 08:48:40 - mmengine - INFO - Epoch(train) [62][ 180/1879] lr: 2.0000e-03 eta: 7:30:55 time: 0.4039 data_time: 0.1307 memory: 6717 grad_norm: 3.0987 loss: 1.0530 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0530 2023/04/14 08:48:47 - mmengine - INFO - Epoch(train) [62][ 200/1879] lr: 2.0000e-03 eta: 7:30:47 time: 0.3370 data_time: 0.0846 memory: 6717 grad_norm: 3.1708 loss: 1.2471 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2471 2023/04/14 08:48:56 - mmengine - INFO - Epoch(train) [62][ 220/1879] lr: 2.0000e-03 eta: 7:30:41 time: 0.4346 data_time: 0.1016 memory: 6717 grad_norm: 3.2147 loss: 1.2914 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2914 2023/04/14 08:49:02 - mmengine - INFO - Epoch(train) [62][ 240/1879] lr: 2.0000e-03 eta: 7:30:33 time: 0.3222 data_time: 0.0775 memory: 6717 grad_norm: 3.0504 loss: 1.1081 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1081 2023/04/14 08:49:11 - mmengine - INFO - Epoch(train) [62][ 260/1879] lr: 2.0000e-03 eta: 7:30:26 time: 0.4183 data_time: 0.1498 memory: 6717 grad_norm: 3.1144 loss: 1.1830 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1830 2023/04/14 08:49:17 - mmengine - INFO - Epoch(train) [62][ 280/1879] lr: 2.0000e-03 eta: 7:30:18 time: 0.3368 data_time: 0.0564 memory: 6717 grad_norm: 3.1481 loss: 1.2470 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2470 2023/04/14 08:49:25 - mmengine - INFO - Epoch(train) [62][ 300/1879] lr: 2.0000e-03 eta: 7:30:11 time: 0.3940 data_time: 0.1315 memory: 6717 grad_norm: 3.1624 loss: 1.1678 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1678 2023/04/14 08:49:32 - mmengine - INFO - Epoch(train) [62][ 320/1879] lr: 2.0000e-03 eta: 7:30:03 time: 0.3363 data_time: 0.0387 memory: 6717 grad_norm: 3.1346 loss: 1.2016 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2016 2023/04/14 08:49:40 - mmengine - INFO - Epoch(train) [62][ 340/1879] lr: 2.0000e-03 eta: 7:29:56 time: 0.3912 data_time: 0.0255 memory: 6717 grad_norm: 3.1086 loss: 1.0918 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0918 2023/04/14 08:49:47 - mmengine - INFO - Epoch(train) [62][ 360/1879] lr: 2.0000e-03 eta: 7:29:48 time: 0.3418 data_time: 0.0163 memory: 6717 grad_norm: 3.1436 loss: 1.1310 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1310 2023/04/14 08:49:55 - mmengine - INFO - Epoch(train) [62][ 380/1879] lr: 2.0000e-03 eta: 7:29:41 time: 0.4199 data_time: 0.0144 memory: 6717 grad_norm: 3.2291 loss: 1.0735 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0735 2023/04/14 08:49:55 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 08:50:02 - mmengine - INFO - Epoch(train) [62][ 400/1879] lr: 2.0000e-03 eta: 7:29:34 time: 0.3464 data_time: 0.0157 memory: 6717 grad_norm: 3.2259 loss: 1.2406 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2406 2023/04/14 08:50:10 - mmengine - INFO - Epoch(train) [62][ 420/1879] lr: 2.0000e-03 eta: 7:29:27 time: 0.4192 data_time: 0.0147 memory: 6717 grad_norm: 3.0680 loss: 1.0879 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0879 2023/04/14 08:50:17 - mmengine - INFO - Epoch(train) [62][ 440/1879] lr: 2.0000e-03 eta: 7:29:19 time: 0.3117 data_time: 0.0137 memory: 6717 grad_norm: 3.1904 loss: 1.1703 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1703 2023/04/14 08:50:25 - mmengine - INFO - Epoch(train) [62][ 460/1879] lr: 2.0000e-03 eta: 7:29:12 time: 0.4341 data_time: 0.0155 memory: 6717 grad_norm: 3.1742 loss: 1.2009 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2009 2023/04/14 08:50:32 - mmengine - INFO - Epoch(train) [62][ 480/1879] lr: 2.0000e-03 eta: 7:29:04 time: 0.3219 data_time: 0.0128 memory: 6717 grad_norm: 3.1915 loss: 1.2715 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2715 2023/04/14 08:50:40 - mmengine - INFO - Epoch(train) [62][ 500/1879] lr: 2.0000e-03 eta: 7:28:57 time: 0.4176 data_time: 0.0137 memory: 6717 grad_norm: 3.1225 loss: 1.1808 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1808 2023/04/14 08:50:46 - mmengine - INFO - Epoch(train) [62][ 520/1879] lr: 2.0000e-03 eta: 7:28:49 time: 0.3100 data_time: 0.0157 memory: 6717 grad_norm: 3.1480 loss: 1.0623 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.0623 2023/04/14 08:50:54 - mmengine - INFO - Epoch(train) [62][ 540/1879] lr: 2.0000e-03 eta: 7:28:42 time: 0.3876 data_time: 0.0141 memory: 6717 grad_norm: 3.0497 loss: 1.1973 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1973 2023/04/14 08:51:01 - mmengine - INFO - Epoch(train) [62][ 560/1879] lr: 2.0000e-03 eta: 7:28:34 time: 0.3397 data_time: 0.0157 memory: 6717 grad_norm: 3.1653 loss: 1.1769 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1769 2023/04/14 08:51:09 - mmengine - INFO - Epoch(train) [62][ 580/1879] lr: 2.0000e-03 eta: 7:28:27 time: 0.4111 data_time: 0.0155 memory: 6717 grad_norm: 3.1593 loss: 1.1918 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1918 2023/04/14 08:51:15 - mmengine - INFO - Epoch(train) [62][ 600/1879] lr: 2.0000e-03 eta: 7:28:19 time: 0.3047 data_time: 0.0137 memory: 6717 grad_norm: 3.0372 loss: 1.1508 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1508 2023/04/14 08:51:23 - mmengine - INFO - Epoch(train) [62][ 620/1879] lr: 2.0000e-03 eta: 7:28:12 time: 0.4009 data_time: 0.0140 memory: 6717 grad_norm: 3.1672 loss: 1.1621 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1621 2023/04/14 08:51:30 - mmengine - INFO - Epoch(train) [62][ 640/1879] lr: 2.0000e-03 eta: 7:28:04 time: 0.3499 data_time: 0.0139 memory: 6717 grad_norm: 3.1874 loss: 1.1740 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1740 2023/04/14 08:51:38 - mmengine - INFO - Epoch(train) [62][ 660/1879] lr: 2.0000e-03 eta: 7:27:57 time: 0.3822 data_time: 0.0152 memory: 6717 grad_norm: 3.2696 loss: 1.1796 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1796 2023/04/14 08:51:44 - mmengine - INFO - Epoch(train) [62][ 680/1879] lr: 2.0000e-03 eta: 7:27:49 time: 0.3339 data_time: 0.0123 memory: 6717 grad_norm: 3.1873 loss: 1.1422 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1422 2023/04/14 08:51:52 - mmengine - INFO - Epoch(train) [62][ 700/1879] lr: 2.0000e-03 eta: 7:27:42 time: 0.3569 data_time: 0.0154 memory: 6717 grad_norm: 3.0682 loss: 1.0520 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0520 2023/04/14 08:52:00 - mmengine - INFO - Epoch(train) [62][ 720/1879] lr: 2.0000e-03 eta: 7:27:35 time: 0.4163 data_time: 0.0137 memory: 6717 grad_norm: 3.0904 loss: 1.3678 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3678 2023/04/14 08:52:06 - mmengine - INFO - Epoch(train) [62][ 740/1879] lr: 2.0000e-03 eta: 7:27:27 time: 0.2964 data_time: 0.0146 memory: 6717 grad_norm: 3.1568 loss: 1.1786 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1786 2023/04/14 08:52:15 - mmengine - INFO - Epoch(train) [62][ 760/1879] lr: 2.0000e-03 eta: 7:27:20 time: 0.4682 data_time: 0.0135 memory: 6717 grad_norm: 3.1449 loss: 1.3389 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3389 2023/04/14 08:52:21 - mmengine - INFO - Epoch(train) [62][ 780/1879] lr: 2.0000e-03 eta: 7:27:12 time: 0.3029 data_time: 0.0144 memory: 6717 grad_norm: 3.1676 loss: 1.2715 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2715 2023/04/14 08:52:29 - mmengine - INFO - Epoch(train) [62][ 800/1879] lr: 2.0000e-03 eta: 7:27:05 time: 0.3785 data_time: 0.0134 memory: 6717 grad_norm: 3.1081 loss: 1.1006 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1006 2023/04/14 08:52:36 - mmengine - INFO - Epoch(train) [62][ 820/1879] lr: 2.0000e-03 eta: 7:26:57 time: 0.3718 data_time: 0.0149 memory: 6717 grad_norm: 3.1499 loss: 1.0463 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0463 2023/04/14 08:52:44 - mmengine - INFO - Epoch(train) [62][ 840/1879] lr: 2.0000e-03 eta: 7:26:50 time: 0.3718 data_time: 0.0135 memory: 6717 grad_norm: 3.2003 loss: 1.2414 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2414 2023/04/14 08:52:51 - mmengine - INFO - Epoch(train) [62][ 860/1879] lr: 2.0000e-03 eta: 7:26:43 time: 0.3748 data_time: 0.0141 memory: 6717 grad_norm: 3.2056 loss: 1.2352 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2352 2023/04/14 08:52:59 - mmengine - INFO - Epoch(train) [62][ 880/1879] lr: 2.0000e-03 eta: 7:26:36 time: 0.3884 data_time: 0.0139 memory: 6717 grad_norm: 3.1904 loss: 1.1767 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1767 2023/04/14 08:53:06 - mmengine - INFO - Epoch(train) [62][ 900/1879] lr: 2.0000e-03 eta: 7:26:28 time: 0.3592 data_time: 0.0134 memory: 6717 grad_norm: 3.1279 loss: 1.1199 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1199 2023/04/14 08:53:14 - mmengine - INFO - Epoch(train) [62][ 920/1879] lr: 2.0000e-03 eta: 7:26:21 time: 0.3815 data_time: 0.0150 memory: 6717 grad_norm: 3.1421 loss: 1.2054 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2054 2023/04/14 08:53:21 - mmengine - INFO - Epoch(train) [62][ 940/1879] lr: 2.0000e-03 eta: 7:26:13 time: 0.3612 data_time: 0.0142 memory: 6717 grad_norm: 3.1867 loss: 1.3212 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3212 2023/04/14 08:53:29 - mmengine - INFO - Epoch(train) [62][ 960/1879] lr: 2.0000e-03 eta: 7:26:06 time: 0.3960 data_time: 0.0144 memory: 6717 grad_norm: 3.1329 loss: 1.0823 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0823 2023/04/14 08:53:36 - mmengine - INFO - Epoch(train) [62][ 980/1879] lr: 2.0000e-03 eta: 7:25:58 time: 0.3337 data_time: 0.0157 memory: 6717 grad_norm: 3.1726 loss: 1.2229 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2229 2023/04/14 08:53:44 - mmengine - INFO - Epoch(train) [62][1000/1879] lr: 2.0000e-03 eta: 7:25:51 time: 0.3967 data_time: 0.0135 memory: 6717 grad_norm: 3.2142 loss: 1.1494 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1494 2023/04/14 08:53:50 - mmengine - INFO - Epoch(train) [62][1020/1879] lr: 2.0000e-03 eta: 7:25:43 time: 0.3207 data_time: 0.0170 memory: 6717 grad_norm: 3.2026 loss: 1.2248 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2248 2023/04/14 08:53:58 - mmengine - INFO - Epoch(train) [62][1040/1879] lr: 2.0000e-03 eta: 7:25:36 time: 0.3993 data_time: 0.0129 memory: 6717 grad_norm: 3.1010 loss: 1.1645 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1645 2023/04/14 08:54:05 - mmengine - INFO - Epoch(train) [62][1060/1879] lr: 2.0000e-03 eta: 7:25:28 time: 0.3316 data_time: 0.0141 memory: 6717 grad_norm: 3.1434 loss: 1.1754 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1754 2023/04/14 08:54:12 - mmengine - INFO - Epoch(train) [62][1080/1879] lr: 2.0000e-03 eta: 7:25:21 time: 0.3890 data_time: 0.0137 memory: 6717 grad_norm: 3.1604 loss: 1.3217 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3217 2023/04/14 08:54:19 - mmengine - INFO - Epoch(train) [62][1100/1879] lr: 2.0000e-03 eta: 7:25:13 time: 0.3281 data_time: 0.0134 memory: 6717 grad_norm: 3.1652 loss: 1.0033 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0033 2023/04/14 08:54:27 - mmengine - INFO - Epoch(train) [62][1120/1879] lr: 2.0000e-03 eta: 7:25:06 time: 0.4180 data_time: 0.0157 memory: 6717 grad_norm: 3.1591 loss: 1.2066 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2066 2023/04/14 08:54:35 - mmengine - INFO - Epoch(train) [62][1140/1879] lr: 2.0000e-03 eta: 7:24:59 time: 0.3784 data_time: 0.0122 memory: 6717 grad_norm: 3.1167 loss: 1.2151 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2151 2023/04/14 08:54:43 - mmengine - INFO - Epoch(train) [62][1160/1879] lr: 2.0000e-03 eta: 7:24:52 time: 0.3865 data_time: 0.0142 memory: 6717 grad_norm: 3.1693 loss: 1.4526 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.4526 2023/04/14 08:54:49 - mmengine - INFO - Epoch(train) [62][1180/1879] lr: 2.0000e-03 eta: 7:24:44 time: 0.3046 data_time: 0.0157 memory: 6717 grad_norm: 3.2096 loss: 1.2881 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.2881 2023/04/14 08:54:57 - mmengine - INFO - Epoch(train) [62][1200/1879] lr: 2.0000e-03 eta: 7:24:37 time: 0.3914 data_time: 0.0135 memory: 6717 grad_norm: 3.1872 loss: 1.3556 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.3556 2023/04/14 08:55:03 - mmengine - INFO - Epoch(train) [62][1220/1879] lr: 2.0000e-03 eta: 7:24:29 time: 0.3231 data_time: 0.0141 memory: 6717 grad_norm: 3.1336 loss: 1.1581 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1581 2023/04/14 08:55:12 - mmengine - INFO - Epoch(train) [62][1240/1879] lr: 2.0000e-03 eta: 7:24:22 time: 0.4220 data_time: 0.0141 memory: 6717 grad_norm: 3.0859 loss: 1.0386 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0386 2023/04/14 08:55:18 - mmengine - INFO - Epoch(train) [62][1260/1879] lr: 2.0000e-03 eta: 7:24:14 time: 0.3198 data_time: 0.0152 memory: 6717 grad_norm: 3.2196 loss: 1.2309 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2309 2023/04/14 08:55:26 - mmengine - INFO - Epoch(train) [62][1280/1879] lr: 2.0000e-03 eta: 7:24:07 time: 0.4060 data_time: 0.0178 memory: 6717 grad_norm: 3.2320 loss: 1.2441 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2441 2023/04/14 08:55:33 - mmengine - INFO - Epoch(train) [62][1300/1879] lr: 2.0000e-03 eta: 7:23:59 time: 0.3526 data_time: 0.0366 memory: 6717 grad_norm: 3.1854 loss: 1.3607 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3607 2023/04/14 08:55:40 - mmengine - INFO - Epoch(train) [62][1320/1879] lr: 2.0000e-03 eta: 7:23:51 time: 0.3446 data_time: 0.0982 memory: 6717 grad_norm: 3.1527 loss: 1.1763 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1763 2023/04/14 08:55:48 - mmengine - INFO - Epoch(train) [62][1340/1879] lr: 2.0000e-03 eta: 7:23:45 time: 0.4086 data_time: 0.1727 memory: 6717 grad_norm: 3.1706 loss: 1.2557 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2557 2023/04/14 08:55:55 - mmengine - INFO - Epoch(train) [62][1360/1879] lr: 2.0000e-03 eta: 7:23:37 time: 0.3437 data_time: 0.1529 memory: 6717 grad_norm: 3.1733 loss: 1.3177 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3177 2023/04/14 08:56:04 - mmengine - INFO - Epoch(train) [62][1380/1879] lr: 2.0000e-03 eta: 7:23:30 time: 0.4247 data_time: 0.2157 memory: 6717 grad_norm: 3.1699 loss: 1.2852 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2852 2023/04/14 08:56:04 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 08:56:09 - mmengine - INFO - Epoch(train) [62][1400/1879] lr: 2.0000e-03 eta: 7:23:22 time: 0.2957 data_time: 0.0571 memory: 6717 grad_norm: 3.2090 loss: 1.2050 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2050 2023/04/14 08:56:17 - mmengine - INFO - Epoch(train) [62][1420/1879] lr: 2.0000e-03 eta: 7:23:14 time: 0.3748 data_time: 0.0769 memory: 6717 grad_norm: 3.2271 loss: 1.2090 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2090 2023/04/14 08:56:24 - mmengine - INFO - Epoch(train) [62][1440/1879] lr: 2.0000e-03 eta: 7:23:07 time: 0.3712 data_time: 0.0907 memory: 6717 grad_norm: 3.2579 loss: 1.1386 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1386 2023/04/14 08:56:31 - mmengine - INFO - Epoch(train) [62][1460/1879] lr: 2.0000e-03 eta: 7:22:59 time: 0.3505 data_time: 0.0697 memory: 6717 grad_norm: 3.1559 loss: 1.3119 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3119 2023/04/14 08:56:39 - mmengine - INFO - Epoch(train) [62][1480/1879] lr: 2.0000e-03 eta: 7:22:52 time: 0.3621 data_time: 0.0731 memory: 6717 grad_norm: 3.1118 loss: 1.5089 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5089 2023/04/14 08:56:46 - mmengine - INFO - Epoch(train) [62][1500/1879] lr: 2.0000e-03 eta: 7:22:44 time: 0.3639 data_time: 0.0987 memory: 6717 grad_norm: 3.1477 loss: 1.1946 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1946 2023/04/14 08:56:53 - mmengine - INFO - Epoch(train) [62][1520/1879] lr: 2.0000e-03 eta: 7:22:37 time: 0.3386 data_time: 0.1023 memory: 6717 grad_norm: 3.2566 loss: 1.2400 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2400 2023/04/14 08:57:01 - mmengine - INFO - Epoch(train) [62][1540/1879] lr: 2.0000e-03 eta: 7:22:30 time: 0.4276 data_time: 0.1112 memory: 6717 grad_norm: 3.2022 loss: 1.1720 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1720 2023/04/14 08:57:08 - mmengine - INFO - Epoch(train) [62][1560/1879] lr: 2.0000e-03 eta: 7:22:22 time: 0.3207 data_time: 0.0308 memory: 6717 grad_norm: 3.1490 loss: 1.1471 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1471 2023/04/14 08:57:17 - mmengine - INFO - Epoch(train) [62][1580/1879] lr: 2.0000e-03 eta: 7:22:16 time: 0.4483 data_time: 0.0181 memory: 6717 grad_norm: 3.1043 loss: 1.1802 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1802 2023/04/14 08:57:23 - mmengine - INFO - Epoch(train) [62][1600/1879] lr: 2.0000e-03 eta: 7:22:08 time: 0.3288 data_time: 0.0135 memory: 6717 grad_norm: 3.1045 loss: 1.2925 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2925 2023/04/14 08:57:31 - mmengine - INFO - Epoch(train) [62][1620/1879] lr: 2.0000e-03 eta: 7:22:01 time: 0.4062 data_time: 0.0142 memory: 6717 grad_norm: 3.1391 loss: 1.2460 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2460 2023/04/14 08:57:38 - mmengine - INFO - Epoch(train) [62][1640/1879] lr: 2.0000e-03 eta: 7:21:53 time: 0.3357 data_time: 0.0156 memory: 6717 grad_norm: 3.0994 loss: 1.1383 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1383 2023/04/14 08:57:46 - mmengine - INFO - Epoch(train) [62][1660/1879] lr: 2.0000e-03 eta: 7:21:46 time: 0.4120 data_time: 0.0170 memory: 6717 grad_norm: 3.1361 loss: 1.1497 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1497 2023/04/14 08:57:53 - mmengine - INFO - Epoch(train) [62][1680/1879] lr: 2.0000e-03 eta: 7:21:38 time: 0.3147 data_time: 0.0143 memory: 6717 grad_norm: 3.1111 loss: 1.2598 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2598 2023/04/14 08:58:01 - mmengine - INFO - Epoch(train) [62][1700/1879] lr: 2.0000e-03 eta: 7:21:31 time: 0.4327 data_time: 0.0141 memory: 6717 grad_norm: 3.1445 loss: 1.1443 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1443 2023/04/14 08:58:07 - mmengine - INFO - Epoch(train) [62][1720/1879] lr: 2.0000e-03 eta: 7:21:23 time: 0.3030 data_time: 0.0159 memory: 6717 grad_norm: 3.1971 loss: 1.3239 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3239 2023/04/14 08:58:16 - mmengine - INFO - Epoch(train) [62][1740/1879] lr: 2.0000e-03 eta: 7:21:16 time: 0.4107 data_time: 0.0132 memory: 6717 grad_norm: 3.1384 loss: 1.3645 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.3645 2023/04/14 08:58:22 - mmengine - INFO - Epoch(train) [62][1760/1879] lr: 2.0000e-03 eta: 7:21:08 time: 0.3008 data_time: 0.0152 memory: 6717 grad_norm: 3.1181 loss: 1.2100 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2100 2023/04/14 08:58:29 - mmengine - INFO - Epoch(train) [62][1780/1879] lr: 2.0000e-03 eta: 7:21:01 time: 0.3876 data_time: 0.0189 memory: 6717 grad_norm: 3.1938 loss: 1.3679 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3679 2023/04/14 08:58:36 - mmengine - INFO - Epoch(train) [62][1800/1879] lr: 2.0000e-03 eta: 7:20:53 time: 0.3546 data_time: 0.0994 memory: 6717 grad_norm: 3.0906 loss: 1.1360 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1360 2023/04/14 08:58:44 - mmengine - INFO - Epoch(train) [62][1820/1879] lr: 2.0000e-03 eta: 7:20:46 time: 0.3800 data_time: 0.1016 memory: 6717 grad_norm: 3.1254 loss: 1.2893 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2893 2023/04/14 08:58:51 - mmengine - INFO - Epoch(train) [62][1840/1879] lr: 2.0000e-03 eta: 7:20:38 time: 0.3549 data_time: 0.0717 memory: 6717 grad_norm: 3.1281 loss: 1.3047 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3047 2023/04/14 08:58:59 - mmengine - INFO - Epoch(train) [62][1860/1879] lr: 2.0000e-03 eta: 7:20:31 time: 0.3894 data_time: 0.0560 memory: 6717 grad_norm: 3.1537 loss: 1.1710 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1710 2023/04/14 08:59:05 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 08:59:05 - mmengine - INFO - Epoch(train) [62][1879/1879] lr: 2.0000e-03 eta: 7:20:23 time: 0.3074 data_time: 0.0815 memory: 6717 grad_norm: 3.1970 loss: 1.1167 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.1167 2023/04/14 08:59:14 - mmengine - INFO - Epoch(val) [62][ 20/155] eta: 0:01:00 time: 0.4482 data_time: 0.4146 memory: 1391 2023/04/14 08:59:20 - mmengine - INFO - Epoch(val) [62][ 40/155] eta: 0:00:44 time: 0.3205 data_time: 0.2871 memory: 1391 2023/04/14 08:59:29 - mmengine - INFO - Epoch(val) [62][ 60/155] eta: 0:00:38 time: 0.4468 data_time: 0.4132 memory: 1391 2023/04/14 08:59:36 - mmengine - INFO - Epoch(val) [62][ 80/155] eta: 0:00:28 time: 0.3153 data_time: 0.2816 memory: 1391 2023/04/14 08:59:45 - mmengine - INFO - Epoch(val) [62][100/155] eta: 0:00:21 time: 0.4565 data_time: 0.4230 memory: 1391 2023/04/14 08:59:51 - mmengine - INFO - Epoch(val) [62][120/155] eta: 0:00:13 time: 0.2983 data_time: 0.2649 memory: 1391 2023/04/14 09:00:00 - mmengine - INFO - Epoch(val) [62][140/155] eta: 0:00:05 time: 0.4854 data_time: 0.4524 memory: 1391 2023/04/14 09:00:08 - mmengine - INFO - Epoch(val) [62][155/155] acc/top1: 0.6636 acc/top5: 0.8721 acc/mean1: 0.6635 data_time: 0.4226 time: 0.4550 2023/04/14 09:00:17 - mmengine - INFO - Epoch(train) [63][ 20/1879] lr: 2.0000e-03 eta: 7:20:17 time: 0.4749 data_time: 0.1752 memory: 6717 grad_norm: 3.1859 loss: 1.1055 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1055 2023/04/14 09:00:24 - mmengine - INFO - Epoch(train) [63][ 40/1879] lr: 2.0000e-03 eta: 7:20:10 time: 0.3464 data_time: 0.0189 memory: 6717 grad_norm: 3.1281 loss: 1.2604 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2604 2023/04/14 09:00:33 - mmengine - INFO - Epoch(train) [63][ 60/1879] lr: 2.0000e-03 eta: 7:20:03 time: 0.4136 data_time: 0.0143 memory: 6717 grad_norm: 3.1672 loss: 1.3200 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3200 2023/04/14 09:00:39 - mmengine - INFO - Epoch(train) [63][ 80/1879] lr: 2.0000e-03 eta: 7:19:55 time: 0.3324 data_time: 0.0143 memory: 6717 grad_norm: 3.0716 loss: 1.2763 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2763 2023/04/14 09:00:48 - mmengine - INFO - Epoch(train) [63][ 100/1879] lr: 2.0000e-03 eta: 7:19:48 time: 0.4325 data_time: 0.0140 memory: 6717 grad_norm: 3.0620 loss: 1.5023 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.5023 2023/04/14 09:00:54 - mmengine - INFO - Epoch(train) [63][ 120/1879] lr: 2.0000e-03 eta: 7:19:40 time: 0.2908 data_time: 0.0149 memory: 6717 grad_norm: 3.0882 loss: 1.1436 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1436 2023/04/14 09:01:02 - mmengine - INFO - Epoch(train) [63][ 140/1879] lr: 2.0000e-03 eta: 7:19:33 time: 0.4230 data_time: 0.0150 memory: 6717 grad_norm: 3.2338 loss: 1.0076 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0076 2023/04/14 09:01:09 - mmengine - INFO - Epoch(train) [63][ 160/1879] lr: 2.0000e-03 eta: 7:19:25 time: 0.3531 data_time: 0.0413 memory: 6717 grad_norm: 3.1419 loss: 1.3546 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.3546 2023/04/14 09:01:17 - mmengine - INFO - Epoch(train) [63][ 180/1879] lr: 2.0000e-03 eta: 7:19:18 time: 0.3853 data_time: 0.0149 memory: 6717 grad_norm: 3.1833 loss: 1.2215 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2215 2023/04/14 09:01:24 - mmengine - INFO - Epoch(train) [63][ 200/1879] lr: 2.0000e-03 eta: 7:19:11 time: 0.3482 data_time: 0.0140 memory: 6717 grad_norm: 3.1288 loss: 1.1186 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1186 2023/04/14 09:01:32 - mmengine - INFO - Epoch(train) [63][ 220/1879] lr: 2.0000e-03 eta: 7:19:03 time: 0.3842 data_time: 0.0257 memory: 6717 grad_norm: 3.1190 loss: 1.1089 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1089 2023/04/14 09:01:38 - mmengine - INFO - Epoch(train) [63][ 240/1879] lr: 2.0000e-03 eta: 7:18:56 time: 0.3437 data_time: 0.0177 memory: 6717 grad_norm: 3.1801 loss: 1.1516 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1516 2023/04/14 09:01:46 - mmengine - INFO - Epoch(train) [63][ 260/1879] lr: 2.0000e-03 eta: 7:18:48 time: 0.3807 data_time: 0.0211 memory: 6717 grad_norm: 3.2262 loss: 1.3327 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3327 2023/04/14 09:01:53 - mmengine - INFO - Epoch(train) [63][ 280/1879] lr: 2.0000e-03 eta: 7:18:40 time: 0.3257 data_time: 0.0319 memory: 6717 grad_norm: 3.1212 loss: 1.2035 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2035 2023/04/14 09:02:01 - mmengine - INFO - Epoch(train) [63][ 300/1879] lr: 2.0000e-03 eta: 7:18:33 time: 0.4013 data_time: 0.0280 memory: 6717 grad_norm: 3.0794 loss: 1.2419 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.2419 2023/04/14 09:02:07 - mmengine - INFO - Epoch(train) [63][ 320/1879] lr: 2.0000e-03 eta: 7:18:25 time: 0.3288 data_time: 0.0138 memory: 6717 grad_norm: 3.1796 loss: 1.1494 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1494 2023/04/14 09:02:15 - mmengine - INFO - Epoch(train) [63][ 340/1879] lr: 2.0000e-03 eta: 7:18:18 time: 0.3997 data_time: 0.0777 memory: 6717 grad_norm: 3.1693 loss: 1.2372 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2372 2023/04/14 09:02:22 - mmengine - INFO - Epoch(train) [63][ 360/1879] lr: 2.0000e-03 eta: 7:18:11 time: 0.3525 data_time: 0.1485 memory: 6717 grad_norm: 3.0880 loss: 1.2292 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2292 2023/04/14 09:02:30 - mmengine - INFO - Epoch(train) [63][ 380/1879] lr: 2.0000e-03 eta: 7:18:04 time: 0.3957 data_time: 0.0559 memory: 6717 grad_norm: 3.2987 loss: 1.2364 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2364 2023/04/14 09:02:37 - mmengine - INFO - Epoch(train) [63][ 400/1879] lr: 2.0000e-03 eta: 7:17:56 time: 0.3200 data_time: 0.0144 memory: 6717 grad_norm: 3.1769 loss: 1.0889 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0889 2023/04/14 09:02:44 - mmengine - INFO - Epoch(train) [63][ 420/1879] lr: 2.0000e-03 eta: 7:17:49 time: 0.3861 data_time: 0.0217 memory: 6717 grad_norm: 3.1032 loss: 1.2659 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2659 2023/04/14 09:02:52 - mmengine - INFO - Epoch(train) [63][ 440/1879] lr: 2.0000e-03 eta: 7:17:41 time: 0.3646 data_time: 0.1077 memory: 6717 grad_norm: 3.0825 loss: 1.1840 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1840 2023/04/14 09:02:59 - mmengine - INFO - Epoch(train) [63][ 460/1879] lr: 2.0000e-03 eta: 7:17:34 time: 0.3604 data_time: 0.1007 memory: 6717 grad_norm: 3.1617 loss: 1.0599 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0599 2023/04/14 09:03:07 - mmengine - INFO - Epoch(train) [63][ 480/1879] lr: 2.0000e-03 eta: 7:17:26 time: 0.3882 data_time: 0.1890 memory: 6717 grad_norm: 3.1833 loss: 1.1402 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1402 2023/04/14 09:03:14 - mmengine - INFO - Epoch(train) [63][ 500/1879] lr: 2.0000e-03 eta: 7:17:19 time: 0.3574 data_time: 0.1417 memory: 6717 grad_norm: 3.1178 loss: 1.3142 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3142 2023/04/14 09:03:15 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 09:03:22 - mmengine - INFO - Epoch(train) [63][ 520/1879] lr: 2.0000e-03 eta: 7:17:12 time: 0.3978 data_time: 0.2382 memory: 6717 grad_norm: 3.1421 loss: 1.2670 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2670 2023/04/14 09:03:29 - mmengine - INFO - Epoch(train) [63][ 540/1879] lr: 2.0000e-03 eta: 7:17:04 time: 0.3505 data_time: 0.1845 memory: 6717 grad_norm: 3.2141 loss: 1.2028 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2028 2023/04/14 09:03:37 - mmengine - INFO - Epoch(train) [63][ 560/1879] lr: 2.0000e-03 eta: 7:16:57 time: 0.4007 data_time: 0.2595 memory: 6717 grad_norm: 3.1612 loss: 1.2463 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2463 2023/04/14 09:03:44 - mmengine - INFO - Epoch(train) [63][ 580/1879] lr: 2.0000e-03 eta: 7:16:49 time: 0.3513 data_time: 0.2129 memory: 6717 grad_norm: 3.2411 loss: 1.4256 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.4256 2023/04/14 09:03:51 - mmengine - INFO - Epoch(train) [63][ 600/1879] lr: 2.0000e-03 eta: 7:16:42 time: 0.3758 data_time: 0.2372 memory: 6717 grad_norm: 3.0964 loss: 1.1887 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1887 2023/04/14 09:03:58 - mmengine - INFO - Epoch(train) [63][ 620/1879] lr: 2.0000e-03 eta: 7:16:34 time: 0.3161 data_time: 0.1785 memory: 6717 grad_norm: 3.1149 loss: 1.0990 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0990 2023/04/14 09:04:06 - mmengine - INFO - Epoch(train) [63][ 640/1879] lr: 2.0000e-03 eta: 7:16:27 time: 0.4012 data_time: 0.2590 memory: 6717 grad_norm: 3.1861 loss: 1.2260 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2260 2023/04/14 09:04:12 - mmengine - INFO - Epoch(train) [63][ 660/1879] lr: 2.0000e-03 eta: 7:16:19 time: 0.3100 data_time: 0.1680 memory: 6717 grad_norm: 3.1123 loss: 1.1511 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1511 2023/04/14 09:04:20 - mmengine - INFO - Epoch(train) [63][ 680/1879] lr: 2.0000e-03 eta: 7:16:12 time: 0.4248 data_time: 0.2135 memory: 6717 grad_norm: 3.2240 loss: 1.1512 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1512 2023/04/14 09:04:27 - mmengine - INFO - Epoch(train) [63][ 700/1879] lr: 2.0000e-03 eta: 7:16:04 time: 0.3391 data_time: 0.1474 memory: 6717 grad_norm: 3.0601 loss: 1.4024 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4024 2023/04/14 09:04:35 - mmengine - INFO - Epoch(train) [63][ 720/1879] lr: 2.0000e-03 eta: 7:15:57 time: 0.3991 data_time: 0.2145 memory: 6717 grad_norm: 3.1706 loss: 1.0630 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0630 2023/04/14 09:04:42 - mmengine - INFO - Epoch(train) [63][ 740/1879] lr: 2.0000e-03 eta: 7:15:49 time: 0.3286 data_time: 0.0818 memory: 6717 grad_norm: 3.1112 loss: 1.2538 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2538 2023/04/14 09:04:50 - mmengine - INFO - Epoch(train) [63][ 760/1879] lr: 2.0000e-03 eta: 7:15:43 time: 0.4160 data_time: 0.0846 memory: 6717 grad_norm: 3.1604 loss: 1.2967 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2967 2023/04/14 09:04:57 - mmengine - INFO - Epoch(train) [63][ 780/1879] lr: 2.0000e-03 eta: 7:15:35 time: 0.3282 data_time: 0.0308 memory: 6717 grad_norm: 3.1728 loss: 1.2805 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2805 2023/04/14 09:05:05 - mmengine - INFO - Epoch(train) [63][ 800/1879] lr: 2.0000e-03 eta: 7:15:28 time: 0.4122 data_time: 0.0777 memory: 6717 grad_norm: 3.2010 loss: 1.1476 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1476 2023/04/14 09:05:11 - mmengine - INFO - Epoch(train) [63][ 820/1879] lr: 2.0000e-03 eta: 7:15:20 time: 0.3018 data_time: 0.0784 memory: 6717 grad_norm: 3.2429 loss: 1.1369 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1369 2023/04/14 09:05:19 - mmengine - INFO - Epoch(train) [63][ 840/1879] lr: 2.0000e-03 eta: 7:15:13 time: 0.4072 data_time: 0.1034 memory: 6717 grad_norm: 3.2189 loss: 1.0456 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0456 2023/04/14 09:05:26 - mmengine - INFO - Epoch(train) [63][ 860/1879] lr: 2.0000e-03 eta: 7:15:05 time: 0.3510 data_time: 0.0123 memory: 6717 grad_norm: 3.0522 loss: 0.9303 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9303 2023/04/14 09:05:34 - mmengine - INFO - Epoch(train) [63][ 880/1879] lr: 2.0000e-03 eta: 7:14:58 time: 0.3953 data_time: 0.0169 memory: 6717 grad_norm: 3.0862 loss: 1.2321 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2321 2023/04/14 09:05:41 - mmengine - INFO - Epoch(train) [63][ 900/1879] lr: 2.0000e-03 eta: 7:14:50 time: 0.3437 data_time: 0.0136 memory: 6717 grad_norm: 3.1237 loss: 1.1096 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.1096 2023/04/14 09:05:48 - mmengine - INFO - Epoch(train) [63][ 920/1879] lr: 2.0000e-03 eta: 7:14:43 time: 0.3537 data_time: 0.0156 memory: 6717 grad_norm: 3.1423 loss: 1.2880 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2880 2023/04/14 09:05:56 - mmengine - INFO - Epoch(train) [63][ 940/1879] lr: 2.0000e-03 eta: 7:14:35 time: 0.3886 data_time: 0.0265 memory: 6717 grad_norm: 3.1921 loss: 1.1954 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1954 2023/04/14 09:06:03 - mmengine - INFO - Epoch(train) [63][ 960/1879] lr: 2.0000e-03 eta: 7:14:28 time: 0.3802 data_time: 0.1411 memory: 6717 grad_norm: 3.1646 loss: 1.2476 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2476 2023/04/14 09:06:10 - mmengine - INFO - Epoch(train) [63][ 980/1879] lr: 2.0000e-03 eta: 7:14:20 time: 0.3304 data_time: 0.1050 memory: 6717 grad_norm: 3.1035 loss: 1.0725 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0725 2023/04/14 09:06:18 - mmengine - INFO - Epoch(train) [63][1000/1879] lr: 2.0000e-03 eta: 7:14:13 time: 0.4216 data_time: 0.0481 memory: 6717 grad_norm: 3.1956 loss: 1.1924 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1924 2023/04/14 09:06:24 - mmengine - INFO - Epoch(train) [63][1020/1879] lr: 2.0000e-03 eta: 7:14:05 time: 0.2960 data_time: 0.1147 memory: 6717 grad_norm: 3.1499 loss: 1.3247 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.3247 2023/04/14 09:06:32 - mmengine - INFO - Epoch(train) [63][1040/1879] lr: 2.0000e-03 eta: 7:13:58 time: 0.4090 data_time: 0.1755 memory: 6717 grad_norm: 3.1266 loss: 1.1068 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1068 2023/04/14 09:06:39 - mmengine - INFO - Epoch(train) [63][1060/1879] lr: 2.0000e-03 eta: 7:13:50 time: 0.3356 data_time: 0.0200 memory: 6717 grad_norm: 3.1705 loss: 1.2533 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.2533 2023/04/14 09:06:47 - mmengine - INFO - Epoch(train) [63][1080/1879] lr: 2.0000e-03 eta: 7:13:43 time: 0.4133 data_time: 0.0557 memory: 6717 grad_norm: 3.0719 loss: 1.2859 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2859 2023/04/14 09:06:54 - mmengine - INFO - Epoch(train) [63][1100/1879] lr: 2.0000e-03 eta: 7:13:36 time: 0.3261 data_time: 0.0314 memory: 6717 grad_norm: 3.1823 loss: 1.2278 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2278 2023/04/14 09:07:03 - mmengine - INFO - Epoch(train) [63][1120/1879] lr: 2.0000e-03 eta: 7:13:29 time: 0.4406 data_time: 0.0310 memory: 6717 grad_norm: 3.2512 loss: 1.1704 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1704 2023/04/14 09:07:09 - mmengine - INFO - Epoch(train) [63][1140/1879] lr: 2.0000e-03 eta: 7:13:21 time: 0.3218 data_time: 0.0211 memory: 6717 grad_norm: 3.1804 loss: 1.2132 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2132 2023/04/14 09:07:17 - mmengine - INFO - Epoch(train) [63][1160/1879] lr: 2.0000e-03 eta: 7:13:14 time: 0.4104 data_time: 0.0401 memory: 6717 grad_norm: 3.2082 loss: 1.1413 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1413 2023/04/14 09:07:24 - mmengine - INFO - Epoch(train) [63][1180/1879] lr: 2.0000e-03 eta: 7:13:06 time: 0.3114 data_time: 0.0319 memory: 6717 grad_norm: 3.2190 loss: 1.2658 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2658 2023/04/14 09:07:31 - mmengine - INFO - Epoch(train) [63][1200/1879] lr: 2.0000e-03 eta: 7:12:59 time: 0.3721 data_time: 0.1159 memory: 6717 grad_norm: 3.2074 loss: 1.4366 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.4366 2023/04/14 09:07:38 - mmengine - INFO - Epoch(train) [63][1220/1879] lr: 2.0000e-03 eta: 7:12:51 time: 0.3362 data_time: 0.1796 memory: 6717 grad_norm: 3.0231 loss: 1.1259 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1259 2023/04/14 09:07:46 - mmengine - INFO - Epoch(train) [63][1240/1879] lr: 2.0000e-03 eta: 7:12:44 time: 0.3956 data_time: 0.2116 memory: 6717 grad_norm: 3.0912 loss: 1.1473 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1473 2023/04/14 09:07:52 - mmengine - INFO - Epoch(train) [63][1260/1879] lr: 2.0000e-03 eta: 7:12:36 time: 0.3358 data_time: 0.1896 memory: 6717 grad_norm: 3.1871 loss: 1.0652 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0652 2023/04/14 09:08:00 - mmengine - INFO - Epoch(train) [63][1280/1879] lr: 2.0000e-03 eta: 7:12:29 time: 0.3902 data_time: 0.2083 memory: 6717 grad_norm: 3.1896 loss: 1.1797 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1797 2023/04/14 09:08:07 - mmengine - INFO - Epoch(train) [63][1300/1879] lr: 2.0000e-03 eta: 7:12:21 time: 0.3302 data_time: 0.1644 memory: 6717 grad_norm: 3.1369 loss: 1.1751 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1751 2023/04/14 09:08:15 - mmengine - INFO - Epoch(train) [63][1320/1879] lr: 2.0000e-03 eta: 7:12:14 time: 0.4223 data_time: 0.2228 memory: 6717 grad_norm: 3.1543 loss: 1.3837 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3837 2023/04/14 09:08:22 - mmengine - INFO - Epoch(train) [63][1340/1879] lr: 2.0000e-03 eta: 7:12:06 time: 0.3357 data_time: 0.1957 memory: 6717 grad_norm: 3.1278 loss: 1.1329 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1329 2023/04/14 09:08:31 - mmengine - INFO - Epoch(train) [63][1360/1879] lr: 2.0000e-03 eta: 7:12:00 time: 0.4244 data_time: 0.2761 memory: 6717 grad_norm: 3.2132 loss: 1.1589 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1589 2023/04/14 09:08:37 - mmengine - INFO - Epoch(train) [63][1380/1879] lr: 2.0000e-03 eta: 7:11:51 time: 0.3150 data_time: 0.1698 memory: 6717 grad_norm: 3.1660 loss: 1.2689 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2689 2023/04/14 09:08:45 - mmengine - INFO - Epoch(train) [63][1400/1879] lr: 2.0000e-03 eta: 7:11:45 time: 0.4090 data_time: 0.2626 memory: 6717 grad_norm: 3.2137 loss: 1.1867 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1867 2023/04/14 09:08:51 - mmengine - INFO - Epoch(train) [63][1420/1879] lr: 2.0000e-03 eta: 7:11:37 time: 0.3163 data_time: 0.1581 memory: 6717 grad_norm: 3.2083 loss: 1.3449 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3449 2023/04/14 09:09:00 - mmengine - INFO - Epoch(train) [63][1440/1879] lr: 2.0000e-03 eta: 7:11:30 time: 0.4201 data_time: 0.2667 memory: 6717 grad_norm: 3.1591 loss: 1.3266 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3266 2023/04/14 09:09:06 - mmengine - INFO - Epoch(train) [63][1460/1879] lr: 2.0000e-03 eta: 7:11:21 time: 0.2976 data_time: 0.1599 memory: 6717 grad_norm: 3.2252 loss: 1.3255 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3255 2023/04/14 09:09:14 - mmengine - INFO - Epoch(train) [63][1480/1879] lr: 2.0000e-03 eta: 7:11:15 time: 0.4284 data_time: 0.2426 memory: 6717 grad_norm: 3.1025 loss: 1.1989 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1989 2023/04/14 09:09:20 - mmengine - INFO - Epoch(train) [63][1500/1879] lr: 2.0000e-03 eta: 7:11:07 time: 0.3046 data_time: 0.1680 memory: 6717 grad_norm: 3.0883 loss: 1.2800 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2800 2023/04/14 09:09:22 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 09:09:29 - mmengine - INFO - Epoch(train) [63][1520/1879] lr: 2.0000e-03 eta: 7:11:00 time: 0.4442 data_time: 0.1817 memory: 6717 grad_norm: 3.2099 loss: 1.2306 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2306 2023/04/14 09:09:36 - mmengine - INFO - Epoch(train) [63][1540/1879] lr: 2.0000e-03 eta: 7:10:52 time: 0.3321 data_time: 0.0150 memory: 6717 grad_norm: 3.2222 loss: 1.2330 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.2330 2023/04/14 09:09:45 - mmengine - INFO - Epoch(train) [63][1560/1879] lr: 2.0000e-03 eta: 7:10:46 time: 0.4615 data_time: 0.0148 memory: 6717 grad_norm: 3.1727 loss: 1.3350 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.3350 2023/04/14 09:09:52 - mmengine - INFO - Epoch(train) [63][1580/1879] lr: 2.0000e-03 eta: 7:10:38 time: 0.3219 data_time: 0.0133 memory: 6717 grad_norm: 3.1368 loss: 1.2131 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2131 2023/04/14 09:09:59 - mmengine - INFO - Epoch(train) [63][1600/1879] lr: 2.0000e-03 eta: 7:10:31 time: 0.3708 data_time: 0.0151 memory: 6717 grad_norm: 3.1668 loss: 1.0842 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0842 2023/04/14 09:10:06 - mmengine - INFO - Epoch(train) [63][1620/1879] lr: 2.0000e-03 eta: 7:10:23 time: 0.3558 data_time: 0.0140 memory: 6717 grad_norm: 3.2171 loss: 1.2640 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2640 2023/04/14 09:10:14 - mmengine - INFO - Epoch(train) [63][1640/1879] lr: 2.0000e-03 eta: 7:10:16 time: 0.4010 data_time: 0.0151 memory: 6717 grad_norm: 3.1895 loss: 1.3605 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.3605 2023/04/14 09:10:21 - mmengine - INFO - Epoch(train) [63][1660/1879] lr: 2.0000e-03 eta: 7:10:08 time: 0.3301 data_time: 0.0131 memory: 6717 grad_norm: 3.1895 loss: 1.3725 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3725 2023/04/14 09:10:29 - mmengine - INFO - Epoch(train) [63][1680/1879] lr: 2.0000e-03 eta: 7:10:01 time: 0.4166 data_time: 0.0133 memory: 6717 grad_norm: 3.2519 loss: 1.1819 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1819 2023/04/14 09:10:35 - mmengine - INFO - Epoch(train) [63][1700/1879] lr: 2.0000e-03 eta: 7:09:53 time: 0.3027 data_time: 0.0138 memory: 6717 grad_norm: 3.2261 loss: 1.2286 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2286 2023/04/14 09:10:43 - mmengine - INFO - Epoch(train) [63][1720/1879] lr: 2.0000e-03 eta: 7:09:46 time: 0.3934 data_time: 0.0149 memory: 6717 grad_norm: 3.0892 loss: 1.0479 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0479 2023/04/14 09:10:49 - mmengine - INFO - Epoch(train) [63][1740/1879] lr: 2.0000e-03 eta: 7:09:38 time: 0.3229 data_time: 0.0158 memory: 6717 grad_norm: 3.2155 loss: 1.2612 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2612 2023/04/14 09:10:58 - mmengine - INFO - Epoch(train) [63][1760/1879] lr: 2.0000e-03 eta: 7:09:31 time: 0.4151 data_time: 0.0134 memory: 6717 grad_norm: 3.1904 loss: 1.2081 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2081 2023/04/14 09:11:05 - mmengine - INFO - Epoch(train) [63][1780/1879] lr: 2.0000e-03 eta: 7:09:23 time: 0.3535 data_time: 0.0144 memory: 6717 grad_norm: 3.2134 loss: 1.1099 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1099 2023/04/14 09:11:13 - mmengine - INFO - Epoch(train) [63][1800/1879] lr: 2.0000e-03 eta: 7:09:16 time: 0.3890 data_time: 0.0149 memory: 6717 grad_norm: 3.1723 loss: 1.2427 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2427 2023/04/14 09:11:19 - mmengine - INFO - Epoch(train) [63][1820/1879] lr: 2.0000e-03 eta: 7:09:08 time: 0.3397 data_time: 0.0168 memory: 6717 grad_norm: 3.2374 loss: 1.2172 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2172 2023/04/14 09:11:27 - mmengine - INFO - Epoch(train) [63][1840/1879] lr: 2.0000e-03 eta: 7:09:01 time: 0.3869 data_time: 0.0163 memory: 6717 grad_norm: 3.1487 loss: 1.0896 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0896 2023/04/14 09:11:34 - mmengine - INFO - Epoch(train) [63][1860/1879] lr: 2.0000e-03 eta: 7:08:53 time: 0.3342 data_time: 0.0124 memory: 6717 grad_norm: 3.1759 loss: 1.2870 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2870 2023/04/14 09:11:40 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 09:11:40 - mmengine - INFO - Epoch(train) [63][1879/1879] lr: 2.0000e-03 eta: 7:08:46 time: 0.3329 data_time: 0.0134 memory: 6717 grad_norm: 3.2396 loss: 1.1424 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.1424 2023/04/14 09:11:40 - mmengine - INFO - Saving checkpoint at 63 epochs 2023/04/14 09:11:50 - mmengine - INFO - Epoch(val) [63][ 20/155] eta: 0:01:02 time: 0.4619 data_time: 0.4290 memory: 1391 2023/04/14 09:11:56 - mmengine - INFO - Epoch(val) [63][ 40/155] eta: 0:00:44 time: 0.3064 data_time: 0.2732 memory: 1391 2023/04/14 09:12:05 - mmengine - INFO - Epoch(val) [63][ 60/155] eta: 0:00:38 time: 0.4349 data_time: 0.4016 memory: 1391 2023/04/14 09:12:11 - mmengine - INFO - Epoch(val) [63][ 80/155] eta: 0:00:28 time: 0.3188 data_time: 0.2857 memory: 1391 2023/04/14 09:12:20 - mmengine - INFO - Epoch(val) [63][100/155] eta: 0:00:21 time: 0.4241 data_time: 0.3899 memory: 1391 2023/04/14 09:12:27 - mmengine - INFO - Epoch(val) [63][120/155] eta: 0:00:13 time: 0.3381 data_time: 0.3052 memory: 1391 2023/04/14 09:12:36 - mmengine - INFO - Epoch(val) [63][140/155] eta: 0:00:05 time: 0.4844 data_time: 0.4518 memory: 1391 2023/04/14 09:12:44 - mmengine - INFO - Epoch(val) [63][155/155] acc/top1: 0.6643 acc/top5: 0.8731 acc/mean1: 0.6643 data_time: 0.4168 time: 0.4489 2023/04/14 09:12:44 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_53.pth is removed 2023/04/14 09:12:44 - mmengine - INFO - The best checkpoint with 0.6643 acc/top1 at 63 epoch is saved to best_acc_top1_epoch_63.pth. 2023/04/14 09:12:54 - mmengine - INFO - Epoch(train) [64][ 20/1879] lr: 2.0000e-03 eta: 7:08:40 time: 0.4856 data_time: 0.3465 memory: 6717 grad_norm: 3.2609 loss: 1.2658 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2658 2023/04/14 09:13:01 - mmengine - INFO - Epoch(train) [64][ 40/1879] lr: 2.0000e-03 eta: 7:08:33 time: 0.3552 data_time: 0.2216 memory: 6717 grad_norm: 3.1491 loss: 1.3068 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3068 2023/04/14 09:13:09 - mmengine - INFO - Epoch(train) [64][ 60/1879] lr: 2.0000e-03 eta: 7:08:26 time: 0.4172 data_time: 0.2800 memory: 6717 grad_norm: 3.2131 loss: 1.3390 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3390 2023/04/14 09:13:16 - mmengine - INFO - Epoch(train) [64][ 80/1879] lr: 2.0000e-03 eta: 7:08:18 time: 0.3276 data_time: 0.1595 memory: 6717 grad_norm: 3.1355 loss: 1.0707 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0707 2023/04/14 09:13:24 - mmengine - INFO - Epoch(train) [64][ 100/1879] lr: 2.0000e-03 eta: 7:08:11 time: 0.4076 data_time: 0.2178 memory: 6717 grad_norm: 3.2584 loss: 1.3147 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3147 2023/04/14 09:13:30 - mmengine - INFO - Epoch(train) [64][ 120/1879] lr: 2.0000e-03 eta: 7:08:03 time: 0.3204 data_time: 0.1378 memory: 6717 grad_norm: 3.1373 loss: 1.3360 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3360 2023/04/14 09:13:39 - mmengine - INFO - Epoch(train) [64][ 140/1879] lr: 2.0000e-03 eta: 7:07:56 time: 0.4155 data_time: 0.2494 memory: 6717 grad_norm: 3.1628 loss: 1.2334 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2334 2023/04/14 09:13:45 - mmengine - INFO - Epoch(train) [64][ 160/1879] lr: 2.0000e-03 eta: 7:07:48 time: 0.3081 data_time: 0.1574 memory: 6717 grad_norm: 3.1259 loss: 1.1136 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1136 2023/04/14 09:13:53 - mmengine - INFO - Epoch(train) [64][ 180/1879] lr: 2.0000e-03 eta: 7:07:41 time: 0.3976 data_time: 0.1929 memory: 6717 grad_norm: 3.2000 loss: 1.1315 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1315 2023/04/14 09:14:00 - mmengine - INFO - Epoch(train) [64][ 200/1879] lr: 2.0000e-03 eta: 7:07:33 time: 0.3663 data_time: 0.0871 memory: 6717 grad_norm: 3.0698 loss: 1.0830 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0830 2023/04/14 09:14:08 - mmengine - INFO - Epoch(train) [64][ 220/1879] lr: 2.0000e-03 eta: 7:07:26 time: 0.3812 data_time: 0.0393 memory: 6717 grad_norm: 3.1236 loss: 1.0023 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0023 2023/04/14 09:14:15 - mmengine - INFO - Epoch(train) [64][ 240/1879] lr: 2.0000e-03 eta: 7:07:19 time: 0.3607 data_time: 0.0134 memory: 6717 grad_norm: 3.1467 loss: 1.0505 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0505 2023/04/14 09:14:23 - mmengine - INFO - Epoch(train) [64][ 260/1879] lr: 2.0000e-03 eta: 7:07:12 time: 0.4108 data_time: 0.0149 memory: 6717 grad_norm: 3.2495 loss: 1.2261 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2261 2023/04/14 09:14:30 - mmengine - INFO - Epoch(train) [64][ 280/1879] lr: 2.0000e-03 eta: 7:07:04 time: 0.3429 data_time: 0.0141 memory: 6717 grad_norm: 3.1653 loss: 1.1552 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1552 2023/04/14 09:14:37 - mmengine - INFO - Epoch(train) [64][ 300/1879] lr: 2.0000e-03 eta: 7:06:56 time: 0.3693 data_time: 0.0148 memory: 6717 grad_norm: 3.1643 loss: 1.0272 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0272 2023/04/14 09:14:44 - mmengine - INFO - Epoch(train) [64][ 320/1879] lr: 2.0000e-03 eta: 7:06:49 time: 0.3375 data_time: 0.0146 memory: 6717 grad_norm: 3.2056 loss: 1.1718 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1718 2023/04/14 09:14:52 - mmengine - INFO - Epoch(train) [64][ 340/1879] lr: 2.0000e-03 eta: 7:06:42 time: 0.4063 data_time: 0.0154 memory: 6717 grad_norm: 3.2008 loss: 1.2665 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2665 2023/04/14 09:14:59 - mmengine - INFO - Epoch(train) [64][ 360/1879] lr: 2.0000e-03 eta: 7:06:34 time: 0.3373 data_time: 0.0143 memory: 6717 grad_norm: 3.1779 loss: 1.0691 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0691 2023/04/14 09:15:08 - mmengine - INFO - Epoch(train) [64][ 380/1879] lr: 2.0000e-03 eta: 7:06:27 time: 0.4237 data_time: 0.0139 memory: 6717 grad_norm: 3.2483 loss: 1.2283 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2283 2023/04/14 09:15:14 - mmengine - INFO - Epoch(train) [64][ 400/1879] lr: 2.0000e-03 eta: 7:06:19 time: 0.3159 data_time: 0.0141 memory: 6717 grad_norm: 3.2044 loss: 1.2553 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.2553 2023/04/14 09:15:22 - mmengine - INFO - Epoch(train) [64][ 420/1879] lr: 2.0000e-03 eta: 7:06:12 time: 0.3883 data_time: 0.0149 memory: 6717 grad_norm: 3.2538 loss: 1.1452 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1452 2023/04/14 09:15:28 - mmengine - INFO - Epoch(train) [64][ 440/1879] lr: 2.0000e-03 eta: 7:06:04 time: 0.3292 data_time: 0.0154 memory: 6717 grad_norm: 3.1604 loss: 1.2394 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2394 2023/04/14 09:15:35 - mmengine - INFO - Epoch(train) [64][ 460/1879] lr: 2.0000e-03 eta: 7:05:56 time: 0.3543 data_time: 0.0159 memory: 6717 grad_norm: 3.2251 loss: 1.2594 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.2594 2023/04/14 09:15:43 - mmengine - INFO - Epoch(train) [64][ 480/1879] lr: 2.0000e-03 eta: 7:05:49 time: 0.4019 data_time: 0.0134 memory: 6717 grad_norm: 3.1621 loss: 1.0056 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0056 2023/04/14 09:15:51 - mmengine - INFO - Epoch(train) [64][ 500/1879] lr: 2.0000e-03 eta: 7:05:42 time: 0.3750 data_time: 0.0162 memory: 6717 grad_norm: 3.1788 loss: 1.0602 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0602 2023/04/14 09:15:58 - mmengine - INFO - Epoch(train) [64][ 520/1879] lr: 2.0000e-03 eta: 7:05:34 time: 0.3475 data_time: 0.0135 memory: 6717 grad_norm: 3.1917 loss: 1.2915 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2915 2023/04/14 09:16:06 - mmengine - INFO - Epoch(train) [64][ 540/1879] lr: 2.0000e-03 eta: 7:05:28 time: 0.4249 data_time: 0.0149 memory: 6717 grad_norm: 3.2755 loss: 1.2864 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2864 2023/04/14 09:16:13 - mmengine - INFO - Epoch(train) [64][ 560/1879] lr: 2.0000e-03 eta: 7:05:20 time: 0.3503 data_time: 0.0153 memory: 6717 grad_norm: 3.2162 loss: 1.2346 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2346 2023/04/14 09:16:21 - mmengine - INFO - Epoch(train) [64][ 580/1879] lr: 2.0000e-03 eta: 7:05:13 time: 0.3616 data_time: 0.0151 memory: 6717 grad_norm: 3.0707 loss: 1.1217 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1217 2023/04/14 09:16:28 - mmengine - INFO - Epoch(train) [64][ 600/1879] lr: 2.0000e-03 eta: 7:05:05 time: 0.3896 data_time: 0.0163 memory: 6717 grad_norm: 3.1738 loss: 1.2089 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2089 2023/04/14 09:16:35 - mmengine - INFO - Epoch(train) [64][ 620/1879] lr: 2.0000e-03 eta: 7:04:58 time: 0.3462 data_time: 0.0139 memory: 6717 grad_norm: 3.1189 loss: 1.2526 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2526 2023/04/14 09:16:37 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 09:16:43 - mmengine - INFO - Epoch(train) [64][ 640/1879] lr: 2.0000e-03 eta: 7:04:50 time: 0.3679 data_time: 0.0160 memory: 6717 grad_norm: 3.1523 loss: 1.2574 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.2574 2023/04/14 09:16:49 - mmengine - INFO - Epoch(train) [64][ 660/1879] lr: 2.0000e-03 eta: 7:04:42 time: 0.3382 data_time: 0.0135 memory: 6717 grad_norm: 3.2106 loss: 1.1940 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1940 2023/04/14 09:16:57 - mmengine - INFO - Epoch(train) [64][ 680/1879] lr: 2.0000e-03 eta: 7:04:35 time: 0.3811 data_time: 0.0159 memory: 6717 grad_norm: 3.1335 loss: 1.2282 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2282 2023/04/14 09:17:03 - mmengine - INFO - Epoch(train) [64][ 700/1879] lr: 2.0000e-03 eta: 7:04:27 time: 0.3078 data_time: 0.0129 memory: 6717 grad_norm: 3.2143 loss: 1.2845 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2845 2023/04/14 09:17:12 - mmengine - INFO - Epoch(train) [64][ 720/1879] lr: 2.0000e-03 eta: 7:04:20 time: 0.4277 data_time: 0.0158 memory: 6717 grad_norm: 3.2108 loss: 1.2267 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.2267 2023/04/14 09:17:18 - mmengine - INFO - Epoch(train) [64][ 740/1879] lr: 2.0000e-03 eta: 7:04:12 time: 0.3261 data_time: 0.0151 memory: 6717 grad_norm: 3.1601 loss: 1.2898 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2898 2023/04/14 09:17:26 - mmengine - INFO - Epoch(train) [64][ 760/1879] lr: 2.0000e-03 eta: 7:04:05 time: 0.3870 data_time: 0.0162 memory: 6717 grad_norm: 3.2389 loss: 1.2106 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2106 2023/04/14 09:17:33 - mmengine - INFO - Epoch(train) [64][ 780/1879] lr: 2.0000e-03 eta: 7:03:58 time: 0.3535 data_time: 0.0346 memory: 6717 grad_norm: 3.1855 loss: 1.2881 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2881 2023/04/14 09:17:42 - mmengine - INFO - Epoch(train) [64][ 800/1879] lr: 2.0000e-03 eta: 7:03:51 time: 0.4325 data_time: 0.0155 memory: 6717 grad_norm: 3.1924 loss: 1.1842 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1842 2023/04/14 09:17:48 - mmengine - INFO - Epoch(train) [64][ 820/1879] lr: 2.0000e-03 eta: 7:03:43 time: 0.2960 data_time: 0.0128 memory: 6717 grad_norm: 3.1630 loss: 1.1984 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1984 2023/04/14 09:17:57 - mmengine - INFO - Epoch(train) [64][ 840/1879] lr: 2.0000e-03 eta: 7:03:36 time: 0.4500 data_time: 0.0152 memory: 6717 grad_norm: 3.2048 loss: 1.2220 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2220 2023/04/14 09:18:03 - mmengine - INFO - Epoch(train) [64][ 860/1879] lr: 2.0000e-03 eta: 7:03:28 time: 0.3324 data_time: 0.0137 memory: 6717 grad_norm: 3.2121 loss: 1.4660 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4660 2023/04/14 09:18:12 - mmengine - INFO - Epoch(train) [64][ 880/1879] lr: 2.0000e-03 eta: 7:03:21 time: 0.4132 data_time: 0.0158 memory: 6717 grad_norm: 3.1284 loss: 1.3447 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3447 2023/04/14 09:18:18 - mmengine - INFO - Epoch(train) [64][ 900/1879] lr: 2.0000e-03 eta: 7:03:13 time: 0.3140 data_time: 0.0137 memory: 6717 grad_norm: 3.1281 loss: 1.1244 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1244 2023/04/14 09:18:26 - mmengine - INFO - Epoch(train) [64][ 920/1879] lr: 2.0000e-03 eta: 7:03:06 time: 0.3815 data_time: 0.0157 memory: 6717 grad_norm: 3.1690 loss: 1.0656 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0656 2023/04/14 09:18:32 - mmengine - INFO - Epoch(train) [64][ 940/1879] lr: 2.0000e-03 eta: 7:02:58 time: 0.3205 data_time: 0.0129 memory: 6717 grad_norm: 3.1979 loss: 1.1968 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1968 2023/04/14 09:18:40 - mmengine - INFO - Epoch(train) [64][ 960/1879] lr: 2.0000e-03 eta: 7:02:51 time: 0.4079 data_time: 0.0147 memory: 6717 grad_norm: 3.2094 loss: 1.1150 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1150 2023/04/14 09:18:47 - mmengine - INFO - Epoch(train) [64][ 980/1879] lr: 2.0000e-03 eta: 7:02:43 time: 0.3279 data_time: 0.0140 memory: 6717 grad_norm: 3.2037 loss: 1.3450 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3450 2023/04/14 09:18:55 - mmengine - INFO - Epoch(train) [64][1000/1879] lr: 2.0000e-03 eta: 7:02:37 time: 0.4206 data_time: 0.0155 memory: 6717 grad_norm: 3.1419 loss: 1.2792 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2792 2023/04/14 09:19:01 - mmengine - INFO - Epoch(train) [64][1020/1879] lr: 2.0000e-03 eta: 7:02:29 time: 0.3146 data_time: 0.0135 memory: 6717 grad_norm: 3.1700 loss: 1.1423 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1423 2023/04/14 09:19:10 - mmengine - INFO - Epoch(train) [64][1040/1879] lr: 2.0000e-03 eta: 7:02:22 time: 0.4148 data_time: 0.0171 memory: 6717 grad_norm: 3.1074 loss: 1.1920 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1920 2023/04/14 09:19:16 - mmengine - INFO - Epoch(train) [64][1060/1879] lr: 2.0000e-03 eta: 7:02:14 time: 0.3124 data_time: 0.0132 memory: 6717 grad_norm: 3.1847 loss: 1.2280 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2280 2023/04/14 09:19:24 - mmengine - INFO - Epoch(train) [64][1080/1879] lr: 2.0000e-03 eta: 7:02:07 time: 0.4084 data_time: 0.0161 memory: 6717 grad_norm: 3.1969 loss: 1.1955 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1955 2023/04/14 09:19:31 - mmengine - INFO - Epoch(train) [64][1100/1879] lr: 2.0000e-03 eta: 7:01:59 time: 0.3332 data_time: 0.0131 memory: 6717 grad_norm: 3.2398 loss: 1.2240 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2240 2023/04/14 09:19:39 - mmengine - INFO - Epoch(train) [64][1120/1879] lr: 2.0000e-03 eta: 7:01:52 time: 0.4007 data_time: 0.0176 memory: 6717 grad_norm: 3.1626 loss: 1.1832 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1832 2023/04/14 09:19:46 - mmengine - INFO - Epoch(train) [64][1140/1879] lr: 2.0000e-03 eta: 7:01:44 time: 0.3378 data_time: 0.0129 memory: 6717 grad_norm: 3.1869 loss: 1.3431 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3431 2023/04/14 09:19:54 - mmengine - INFO - Epoch(train) [64][1160/1879] lr: 2.0000e-03 eta: 7:01:37 time: 0.4313 data_time: 0.0159 memory: 6717 grad_norm: 3.2074 loss: 1.3277 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3277 2023/04/14 09:20:00 - mmengine - INFO - Epoch(train) [64][1180/1879] lr: 2.0000e-03 eta: 7:01:29 time: 0.2955 data_time: 0.0129 memory: 6717 grad_norm: 3.1821 loss: 1.0742 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0742 2023/04/14 09:20:09 - mmengine - INFO - Epoch(train) [64][1200/1879] lr: 2.0000e-03 eta: 7:01:22 time: 0.4297 data_time: 0.0148 memory: 6717 grad_norm: 3.1835 loss: 1.1401 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1401 2023/04/14 09:20:15 - mmengine - INFO - Epoch(train) [64][1220/1879] lr: 2.0000e-03 eta: 7:01:14 time: 0.3336 data_time: 0.0137 memory: 6717 grad_norm: 3.1358 loss: 1.0425 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0425 2023/04/14 09:20:23 - mmengine - INFO - Epoch(train) [64][1240/1879] lr: 2.0000e-03 eta: 7:01:07 time: 0.3941 data_time: 0.0148 memory: 6717 grad_norm: 3.1851 loss: 1.1273 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1273 2023/04/14 09:20:30 - mmengine - INFO - Epoch(train) [64][1260/1879] lr: 2.0000e-03 eta: 7:00:59 time: 0.3175 data_time: 0.0152 memory: 6717 grad_norm: 3.2424 loss: 1.2001 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.2001 2023/04/14 09:20:38 - mmengine - INFO - Epoch(train) [64][1280/1879] lr: 2.0000e-03 eta: 7:00:52 time: 0.4020 data_time: 0.0143 memory: 6717 grad_norm: 3.2168 loss: 1.0638 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0638 2023/04/14 09:20:44 - mmengine - INFO - Epoch(train) [64][1300/1879] lr: 2.0000e-03 eta: 7:00:44 time: 0.3212 data_time: 0.0138 memory: 6717 grad_norm: 3.1894 loss: 1.2885 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2885 2023/04/14 09:20:52 - mmengine - INFO - Epoch(train) [64][1320/1879] lr: 2.0000e-03 eta: 7:00:37 time: 0.4127 data_time: 0.0148 memory: 6717 grad_norm: 3.2618 loss: 1.2478 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2478 2023/04/14 09:20:59 - mmengine - INFO - Epoch(train) [64][1340/1879] lr: 2.0000e-03 eta: 7:00:29 time: 0.3133 data_time: 0.0492 memory: 6717 grad_norm: 3.2218 loss: 1.3257 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3257 2023/04/14 09:21:06 - mmengine - INFO - Epoch(train) [64][1360/1879] lr: 2.0000e-03 eta: 7:00:22 time: 0.3890 data_time: 0.0688 memory: 6717 grad_norm: 3.1555 loss: 1.3463 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3463 2023/04/14 09:21:14 - mmengine - INFO - Epoch(train) [64][1380/1879] lr: 2.0000e-03 eta: 7:00:15 time: 0.3818 data_time: 0.1378 memory: 6717 grad_norm: 3.1780 loss: 1.1791 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1791 2023/04/14 09:21:21 - mmengine - INFO - Epoch(train) [64][1400/1879] lr: 2.0000e-03 eta: 7:00:07 time: 0.3367 data_time: 0.0594 memory: 6717 grad_norm: 3.2082 loss: 1.1220 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1220 2023/04/14 09:21:29 - mmengine - INFO - Epoch(train) [64][1420/1879] lr: 2.0000e-03 eta: 7:00:00 time: 0.4036 data_time: 0.0152 memory: 6717 grad_norm: 3.1644 loss: 1.2939 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2939 2023/04/14 09:21:36 - mmengine - INFO - Epoch(train) [64][1440/1879] lr: 2.0000e-03 eta: 6:59:52 time: 0.3405 data_time: 0.0168 memory: 6717 grad_norm: 3.2769 loss: 1.1971 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1971 2023/04/14 09:21:44 - mmengine - INFO - Epoch(train) [64][1460/1879] lr: 2.0000e-03 eta: 6:59:46 time: 0.4215 data_time: 0.0136 memory: 6717 grad_norm: 3.2130 loss: 1.0734 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0734 2023/04/14 09:21:51 - mmengine - INFO - Epoch(train) [64][1480/1879] lr: 2.0000e-03 eta: 6:59:38 time: 0.3302 data_time: 0.0153 memory: 6717 grad_norm: 3.2793 loss: 1.2614 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2614 2023/04/14 09:21:58 - mmengine - INFO - Epoch(train) [64][1500/1879] lr: 2.0000e-03 eta: 6:59:31 time: 0.3859 data_time: 0.0167 memory: 6717 grad_norm: 3.2303 loss: 1.2501 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2501 2023/04/14 09:22:05 - mmengine - INFO - Epoch(train) [64][1520/1879] lr: 2.0000e-03 eta: 6:59:23 time: 0.3296 data_time: 0.0148 memory: 6717 grad_norm: 3.1055 loss: 1.0706 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0706 2023/04/14 09:22:13 - mmengine - INFO - Epoch(train) [64][1540/1879] lr: 2.0000e-03 eta: 6:59:16 time: 0.4086 data_time: 0.0151 memory: 6717 grad_norm: 3.2503 loss: 1.1344 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1344 2023/04/14 09:22:20 - mmengine - INFO - Epoch(train) [64][1560/1879] lr: 2.0000e-03 eta: 6:59:08 time: 0.3534 data_time: 0.0156 memory: 6717 grad_norm: 3.2309 loss: 1.0914 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.0914 2023/04/14 09:22:28 - mmengine - INFO - Epoch(train) [64][1580/1879] lr: 2.0000e-03 eta: 6:59:01 time: 0.3909 data_time: 0.0443 memory: 6717 grad_norm: 3.2087 loss: 1.1635 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1635 2023/04/14 09:22:34 - mmengine - INFO - Epoch(train) [64][1600/1879] lr: 2.0000e-03 eta: 6:58:53 time: 0.3149 data_time: 0.0463 memory: 6717 grad_norm: 3.1843 loss: 1.2147 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2147 2023/04/14 09:22:42 - mmengine - INFO - Epoch(train) [64][1620/1879] lr: 2.0000e-03 eta: 6:58:46 time: 0.3940 data_time: 0.0288 memory: 6717 grad_norm: 3.2183 loss: 1.1063 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1063 2023/04/14 09:22:43 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 09:22:50 - mmengine - INFO - Epoch(train) [64][1640/1879] lr: 2.0000e-03 eta: 6:58:38 time: 0.3728 data_time: 0.0194 memory: 6717 grad_norm: 3.2935 loss: 1.2892 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2892 2023/04/14 09:22:57 - mmengine - INFO - Epoch(train) [64][1660/1879] lr: 2.0000e-03 eta: 6:58:31 time: 0.3760 data_time: 0.0163 memory: 6717 grad_norm: 3.2209 loss: 1.3719 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3719 2023/04/14 09:23:04 - mmengine - INFO - Epoch(train) [64][1680/1879] lr: 2.0000e-03 eta: 6:58:23 time: 0.3449 data_time: 0.0124 memory: 6717 grad_norm: 3.1061 loss: 1.1510 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1510 2023/04/14 09:23:12 - mmengine - INFO - Epoch(train) [64][1700/1879] lr: 2.0000e-03 eta: 6:58:16 time: 0.3996 data_time: 0.0166 memory: 6717 grad_norm: 3.1442 loss: 1.1041 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1041 2023/04/14 09:23:19 - mmengine - INFO - Epoch(train) [64][1720/1879] lr: 2.0000e-03 eta: 6:58:09 time: 0.3388 data_time: 0.0124 memory: 6717 grad_norm: 3.2251 loss: 1.2235 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2235 2023/04/14 09:23:27 - mmengine - INFO - Epoch(train) [64][1740/1879] lr: 2.0000e-03 eta: 6:58:01 time: 0.3879 data_time: 0.0170 memory: 6717 grad_norm: 3.1640 loss: 1.2928 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2928 2023/04/14 09:23:33 - mmengine - INFO - Epoch(train) [64][1760/1879] lr: 2.0000e-03 eta: 6:57:53 time: 0.3124 data_time: 0.0174 memory: 6717 grad_norm: 3.1794 loss: 1.2336 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2336 2023/04/14 09:23:41 - mmengine - INFO - Epoch(train) [64][1780/1879] lr: 2.0000e-03 eta: 6:57:46 time: 0.4114 data_time: 0.0170 memory: 6717 grad_norm: 3.1847 loss: 1.2219 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2219 2023/04/14 09:23:47 - mmengine - INFO - Epoch(train) [64][1800/1879] lr: 2.0000e-03 eta: 6:57:38 time: 0.3145 data_time: 0.0128 memory: 6717 grad_norm: 3.1086 loss: 1.2685 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2685 2023/04/14 09:23:57 - mmengine - INFO - Epoch(train) [64][1820/1879] lr: 2.0000e-03 eta: 6:57:32 time: 0.4551 data_time: 0.0156 memory: 6717 grad_norm: 3.1828 loss: 1.3064 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3064 2023/04/14 09:24:03 - mmengine - INFO - Epoch(train) [64][1840/1879] lr: 2.0000e-03 eta: 6:57:24 time: 0.3024 data_time: 0.0140 memory: 6717 grad_norm: 3.3184 loss: 1.2911 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2911 2023/04/14 09:24:11 - mmengine - INFO - Epoch(train) [64][1860/1879] lr: 2.0000e-03 eta: 6:57:17 time: 0.4017 data_time: 0.0157 memory: 6717 grad_norm: 3.1662 loss: 1.3046 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.3046 2023/04/14 09:24:17 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 09:24:17 - mmengine - INFO - Epoch(train) [64][1879/1879] lr: 2.0000e-03 eta: 6:57:09 time: 0.3071 data_time: 0.0115 memory: 6717 grad_norm: 3.2853 loss: 1.2529 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.2529 2023/04/14 09:24:26 - mmengine - INFO - Epoch(val) [64][ 20/155] eta: 0:01:00 time: 0.4506 data_time: 0.4164 memory: 1391 2023/04/14 09:24:32 - mmengine - INFO - Epoch(val) [64][ 40/155] eta: 0:00:44 time: 0.3312 data_time: 0.2977 memory: 1391 2023/04/14 09:24:41 - mmengine - INFO - Epoch(val) [64][ 60/155] eta: 0:00:38 time: 0.4318 data_time: 0.3982 memory: 1391 2023/04/14 09:24:47 - mmengine - INFO - Epoch(val) [64][ 80/155] eta: 0:00:28 time: 0.3168 data_time: 0.2836 memory: 1391 2023/04/14 09:24:56 - mmengine - INFO - Epoch(val) [64][100/155] eta: 0:00:21 time: 0.4568 data_time: 0.4233 memory: 1391 2023/04/14 09:25:03 - mmengine - INFO - Epoch(val) [64][120/155] eta: 0:00:13 time: 0.3052 data_time: 0.2717 memory: 1391 2023/04/14 09:25:12 - mmengine - INFO - Epoch(val) [64][140/155] eta: 0:00:05 time: 0.4860 data_time: 0.4533 memory: 1391 2023/04/14 09:25:19 - mmengine - INFO - Epoch(val) [64][155/155] acc/top1: 0.6619 acc/top5: 0.8717 acc/mean1: 0.6618 data_time: 0.4173 time: 0.4497 2023/04/14 09:25:29 - mmengine - INFO - Epoch(train) [65][ 20/1879] lr: 2.0000e-03 eta: 6:57:03 time: 0.4942 data_time: 0.3069 memory: 6717 grad_norm: 3.2242 loss: 1.3456 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.3456 2023/04/14 09:25:36 - mmengine - INFO - Epoch(train) [65][ 40/1879] lr: 2.0000e-03 eta: 6:56:56 time: 0.3603 data_time: 0.1385 memory: 6717 grad_norm: 3.1648 loss: 0.9574 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9574 2023/04/14 09:25:44 - mmengine - INFO - Epoch(train) [65][ 60/1879] lr: 2.0000e-03 eta: 6:56:48 time: 0.3927 data_time: 0.1045 memory: 6717 grad_norm: 3.2390 loss: 1.2685 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2685 2023/04/14 09:25:51 - mmengine - INFO - Epoch(train) [65][ 80/1879] lr: 2.0000e-03 eta: 6:56:40 time: 0.3183 data_time: 0.0869 memory: 6717 grad_norm: 3.1208 loss: 1.2586 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2586 2023/04/14 09:25:59 - mmengine - INFO - Epoch(train) [65][ 100/1879] lr: 2.0000e-03 eta: 6:56:33 time: 0.4045 data_time: 0.0917 memory: 6717 grad_norm: 3.2187 loss: 1.3781 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3781 2023/04/14 09:26:06 - mmengine - INFO - Epoch(train) [65][ 120/1879] lr: 2.0000e-03 eta: 6:56:26 time: 0.3422 data_time: 0.0616 memory: 6717 grad_norm: 3.1306 loss: 1.2095 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2095 2023/04/14 09:26:14 - mmengine - INFO - Epoch(train) [65][ 140/1879] lr: 2.0000e-03 eta: 6:56:19 time: 0.4166 data_time: 0.0268 memory: 6717 grad_norm: 3.1538 loss: 1.2374 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2374 2023/04/14 09:26:20 - mmengine - INFO - Epoch(train) [65][ 160/1879] lr: 2.0000e-03 eta: 6:56:11 time: 0.3221 data_time: 0.0132 memory: 6717 grad_norm: 3.2035 loss: 1.1947 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1947 2023/04/14 09:26:29 - mmengine - INFO - Epoch(train) [65][ 180/1879] lr: 2.0000e-03 eta: 6:56:04 time: 0.4114 data_time: 0.0168 memory: 6717 grad_norm: 3.0964 loss: 1.2599 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2599 2023/04/14 09:26:35 - mmengine - INFO - Epoch(train) [65][ 200/1879] lr: 2.0000e-03 eta: 6:55:56 time: 0.3299 data_time: 0.0138 memory: 6717 grad_norm: 3.1489 loss: 1.1978 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1978 2023/04/14 09:26:43 - mmengine - INFO - Epoch(train) [65][ 220/1879] lr: 2.0000e-03 eta: 6:55:49 time: 0.3902 data_time: 0.0166 memory: 6717 grad_norm: 3.2177 loss: 1.0984 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0984 2023/04/14 09:26:50 - mmengine - INFO - Epoch(train) [65][ 240/1879] lr: 2.0000e-03 eta: 6:55:41 time: 0.3394 data_time: 0.0136 memory: 6717 grad_norm: 3.2096 loss: 1.2713 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2713 2023/04/14 09:26:58 - mmengine - INFO - Epoch(train) [65][ 260/1879] lr: 2.0000e-03 eta: 6:55:34 time: 0.4036 data_time: 0.0158 memory: 6717 grad_norm: 3.2156 loss: 1.1834 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1834 2023/04/14 09:27:05 - mmengine - INFO - Epoch(train) [65][ 280/1879] lr: 2.0000e-03 eta: 6:55:26 time: 0.3340 data_time: 0.0143 memory: 6717 grad_norm: 3.0869 loss: 1.1866 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1866 2023/04/14 09:27:13 - mmengine - INFO - Epoch(train) [65][ 300/1879] lr: 2.0000e-03 eta: 6:55:20 time: 0.4261 data_time: 0.0160 memory: 6717 grad_norm: 3.1990 loss: 1.1412 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1412 2023/04/14 09:27:20 - mmengine - INFO - Epoch(train) [65][ 320/1879] lr: 2.0000e-03 eta: 6:55:12 time: 0.3505 data_time: 0.0133 memory: 6717 grad_norm: 3.1105 loss: 1.1490 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1490 2023/04/14 09:27:28 - mmengine - INFO - Epoch(train) [65][ 340/1879] lr: 2.0000e-03 eta: 6:55:05 time: 0.4066 data_time: 0.0183 memory: 6717 grad_norm: 3.2048 loss: 1.2089 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.2089 2023/04/14 09:27:34 - mmengine - INFO - Epoch(train) [65][ 360/1879] lr: 2.0000e-03 eta: 6:54:57 time: 0.3132 data_time: 0.0132 memory: 6717 grad_norm: 3.1925 loss: 1.2931 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2931 2023/04/14 09:27:43 - mmengine - INFO - Epoch(train) [65][ 380/1879] lr: 2.0000e-03 eta: 6:54:50 time: 0.4157 data_time: 0.0173 memory: 6717 grad_norm: 3.1601 loss: 1.1058 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1058 2023/04/14 09:27:49 - mmengine - INFO - Epoch(train) [65][ 400/1879] lr: 2.0000e-03 eta: 6:54:42 time: 0.3060 data_time: 0.0139 memory: 6717 grad_norm: 3.1918 loss: 1.3008 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.3008 2023/04/14 09:27:57 - mmengine - INFO - Epoch(train) [65][ 420/1879] lr: 2.0000e-03 eta: 6:54:35 time: 0.4187 data_time: 0.0395 memory: 6717 grad_norm: 3.1084 loss: 1.2555 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2555 2023/04/14 09:28:04 - mmengine - INFO - Epoch(train) [65][ 440/1879] lr: 2.0000e-03 eta: 6:54:27 time: 0.3245 data_time: 0.0185 memory: 6717 grad_norm: 3.1576 loss: 1.0426 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0426 2023/04/14 09:28:12 - mmengine - INFO - Epoch(train) [65][ 460/1879] lr: 2.0000e-03 eta: 6:54:20 time: 0.4240 data_time: 0.0350 memory: 6717 grad_norm: 3.1364 loss: 1.1822 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1822 2023/04/14 09:28:19 - mmengine - INFO - Epoch(train) [65][ 480/1879] lr: 2.0000e-03 eta: 6:54:13 time: 0.3542 data_time: 0.0130 memory: 6717 grad_norm: 3.1057 loss: 1.2504 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2504 2023/04/14 09:28:28 - mmengine - INFO - Epoch(train) [65][ 500/1879] lr: 2.0000e-03 eta: 6:54:06 time: 0.4147 data_time: 0.0174 memory: 6717 grad_norm: 3.1782 loss: 1.2078 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2078 2023/04/14 09:28:34 - mmengine - INFO - Epoch(train) [65][ 520/1879] lr: 2.0000e-03 eta: 6:53:58 time: 0.3307 data_time: 0.0116 memory: 6717 grad_norm: 3.1276 loss: 1.1129 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1129 2023/04/14 09:28:43 - mmengine - INFO - Epoch(train) [65][ 540/1879] lr: 2.0000e-03 eta: 6:53:51 time: 0.4413 data_time: 0.0158 memory: 6717 grad_norm: 3.2458 loss: 1.2436 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.2436 2023/04/14 09:28:49 - mmengine - INFO - Epoch(train) [65][ 560/1879] lr: 2.0000e-03 eta: 6:53:43 time: 0.2846 data_time: 0.0133 memory: 6717 grad_norm: 3.1490 loss: 1.1081 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1081 2023/04/14 09:28:57 - mmengine - INFO - Epoch(train) [65][ 580/1879] lr: 2.0000e-03 eta: 6:53:36 time: 0.3947 data_time: 0.0181 memory: 6717 grad_norm: 3.1709 loss: 1.1796 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1796 2023/04/14 09:29:03 - mmengine - INFO - Epoch(train) [65][ 600/1879] lr: 2.0000e-03 eta: 6:53:28 time: 0.3167 data_time: 0.0124 memory: 6717 grad_norm: 3.2019 loss: 1.2616 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.2616 2023/04/14 09:29:12 - mmengine - INFO - Epoch(train) [65][ 620/1879] lr: 2.0000e-03 eta: 6:53:21 time: 0.4285 data_time: 0.0165 memory: 6717 grad_norm: 3.1033 loss: 1.0853 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0853 2023/04/14 09:29:18 - mmengine - INFO - Epoch(train) [65][ 640/1879] lr: 2.0000e-03 eta: 6:53:13 time: 0.3046 data_time: 0.0127 memory: 6717 grad_norm: 3.2282 loss: 1.1956 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1956 2023/04/14 09:29:26 - mmengine - INFO - Epoch(train) [65][ 660/1879] lr: 2.0000e-03 eta: 6:53:06 time: 0.4177 data_time: 0.0150 memory: 6717 grad_norm: 3.1499 loss: 1.1698 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1698 2023/04/14 09:29:33 - mmengine - INFO - Epoch(train) [65][ 680/1879] lr: 2.0000e-03 eta: 6:52:58 time: 0.3218 data_time: 0.0137 memory: 6717 grad_norm: 3.1926 loss: 1.2613 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2613 2023/04/14 09:29:41 - mmengine - INFO - Epoch(train) [65][ 700/1879] lr: 2.0000e-03 eta: 6:52:51 time: 0.3981 data_time: 0.0155 memory: 6717 grad_norm: 3.2167 loss: 1.2678 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2678 2023/04/14 09:29:47 - mmengine - INFO - Epoch(train) [65][ 720/1879] lr: 2.0000e-03 eta: 6:52:43 time: 0.3130 data_time: 0.0140 memory: 6717 grad_norm: 3.2240 loss: 1.3824 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3824 2023/04/14 09:29:55 - mmengine - INFO - Epoch(train) [65][ 740/1879] lr: 2.0000e-03 eta: 6:52:36 time: 0.4168 data_time: 0.0322 memory: 6717 grad_norm: 3.2097 loss: 1.3731 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3731 2023/04/14 09:29:56 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 09:30:02 - mmengine - INFO - Epoch(train) [65][ 760/1879] lr: 2.0000e-03 eta: 6:52:28 time: 0.3269 data_time: 0.0199 memory: 6717 grad_norm: 3.1262 loss: 1.0003 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0003 2023/04/14 09:30:11 - mmengine - INFO - Epoch(train) [65][ 780/1879] lr: 2.0000e-03 eta: 6:52:22 time: 0.4505 data_time: 0.0218 memory: 6717 grad_norm: 3.2023 loss: 1.0581 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0581 2023/04/14 09:30:18 - mmengine - INFO - Epoch(train) [65][ 800/1879] lr: 2.0000e-03 eta: 6:52:14 time: 0.3420 data_time: 0.0127 memory: 6717 grad_norm: 3.1775 loss: 1.0772 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0772 2023/04/14 09:30:26 - mmengine - INFO - Epoch(train) [65][ 820/1879] lr: 2.0000e-03 eta: 6:52:07 time: 0.4229 data_time: 0.0139 memory: 6717 grad_norm: 3.2480 loss: 1.0977 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0977 2023/04/14 09:30:32 - mmengine - INFO - Epoch(train) [65][ 840/1879] lr: 2.0000e-03 eta: 6:51:59 time: 0.3010 data_time: 0.0153 memory: 6717 grad_norm: 3.1591 loss: 1.2453 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2453 2023/04/14 09:30:40 - mmengine - INFO - Epoch(train) [65][ 860/1879] lr: 2.0000e-03 eta: 6:51:52 time: 0.4119 data_time: 0.0141 memory: 6717 grad_norm: 3.3034 loss: 1.2913 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2913 2023/04/14 09:30:47 - mmengine - INFO - Epoch(train) [65][ 880/1879] lr: 2.0000e-03 eta: 6:51:44 time: 0.3163 data_time: 0.0145 memory: 6717 grad_norm: 3.1814 loss: 1.1690 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1690 2023/04/14 09:30:55 - mmengine - INFO - Epoch(train) [65][ 900/1879] lr: 2.0000e-03 eta: 6:51:37 time: 0.3985 data_time: 0.0143 memory: 6717 grad_norm: 3.1692 loss: 1.0401 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0401 2023/04/14 09:31:01 - mmengine - INFO - Epoch(train) [65][ 920/1879] lr: 2.0000e-03 eta: 6:51:30 time: 0.3467 data_time: 0.0156 memory: 6717 grad_norm: 3.2469 loss: 1.0895 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0895 2023/04/14 09:31:10 - mmengine - INFO - Epoch(train) [65][ 940/1879] lr: 2.0000e-03 eta: 6:51:23 time: 0.4161 data_time: 0.0425 memory: 6717 grad_norm: 3.2045 loss: 1.1500 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1500 2023/04/14 09:31:17 - mmengine - INFO - Epoch(train) [65][ 960/1879] lr: 2.0000e-03 eta: 6:51:15 time: 0.3413 data_time: 0.0522 memory: 6717 grad_norm: 3.2439 loss: 1.4038 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4038 2023/04/14 09:31:25 - mmengine - INFO - Epoch(train) [65][ 980/1879] lr: 2.0000e-03 eta: 6:51:08 time: 0.4111 data_time: 0.0230 memory: 6717 grad_norm: 3.2382 loss: 1.3184 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3184 2023/04/14 09:31:32 - mmengine - INFO - Epoch(train) [65][1000/1879] lr: 2.0000e-03 eta: 6:51:00 time: 0.3564 data_time: 0.0134 memory: 6717 grad_norm: 3.1675 loss: 1.1851 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1851 2023/04/14 09:31:40 - mmengine - INFO - Epoch(train) [65][1020/1879] lr: 2.0000e-03 eta: 6:50:53 time: 0.4057 data_time: 0.0145 memory: 6717 grad_norm: 3.1482 loss: 1.1888 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1888 2023/04/14 09:31:47 - mmengine - INFO - Epoch(train) [65][1040/1879] lr: 2.0000e-03 eta: 6:50:45 time: 0.3221 data_time: 0.0148 memory: 6717 grad_norm: 3.1867 loss: 1.2943 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2943 2023/04/14 09:31:55 - mmengine - INFO - Epoch(train) [65][1060/1879] lr: 2.0000e-03 eta: 6:50:39 time: 0.4092 data_time: 0.0138 memory: 6717 grad_norm: 3.2353 loss: 1.1076 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1076 2023/04/14 09:32:02 - mmengine - INFO - Epoch(train) [65][1080/1879] lr: 2.0000e-03 eta: 6:50:31 time: 0.3513 data_time: 0.0153 memory: 6717 grad_norm: 3.1991 loss: 1.2138 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2138 2023/04/14 09:32:10 - mmengine - INFO - Epoch(train) [65][1100/1879] lr: 2.0000e-03 eta: 6:50:24 time: 0.4123 data_time: 0.0132 memory: 6717 grad_norm: 3.1964 loss: 1.3088 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3088 2023/04/14 09:32:16 - mmengine - INFO - Epoch(train) [65][1120/1879] lr: 2.0000e-03 eta: 6:50:16 time: 0.3009 data_time: 0.0158 memory: 6717 grad_norm: 3.2084 loss: 1.1527 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1527 2023/04/14 09:32:24 - mmengine - INFO - Epoch(train) [65][1140/1879] lr: 2.0000e-03 eta: 6:50:09 time: 0.4196 data_time: 0.0132 memory: 6717 grad_norm: 3.2182 loss: 1.2601 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2601 2023/04/14 09:32:31 - mmengine - INFO - Epoch(train) [65][1160/1879] lr: 2.0000e-03 eta: 6:50:01 time: 0.3191 data_time: 0.0160 memory: 6717 grad_norm: 3.1836 loss: 1.1293 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1293 2023/04/14 09:32:40 - mmengine - INFO - Epoch(train) [65][1180/1879] lr: 2.0000e-03 eta: 6:49:55 time: 0.4584 data_time: 0.0140 memory: 6717 grad_norm: 3.2012 loss: 1.3789 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3789 2023/04/14 09:32:47 - mmengine - INFO - Epoch(train) [65][1200/1879] lr: 2.0000e-03 eta: 6:49:47 time: 0.3245 data_time: 0.0143 memory: 6717 grad_norm: 3.1725 loss: 1.2629 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2629 2023/04/14 09:32:55 - mmengine - INFO - Epoch(train) [65][1220/1879] lr: 2.0000e-03 eta: 6:49:40 time: 0.4140 data_time: 0.0165 memory: 6717 grad_norm: 3.1814 loss: 1.2085 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2085 2023/04/14 09:33:01 - mmengine - INFO - Epoch(train) [65][1240/1879] lr: 2.0000e-03 eta: 6:49:32 time: 0.3134 data_time: 0.0153 memory: 6717 grad_norm: 3.2163 loss: 1.2773 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2773 2023/04/14 09:33:09 - mmengine - INFO - Epoch(train) [65][1260/1879] lr: 2.0000e-03 eta: 6:49:25 time: 0.3933 data_time: 0.0149 memory: 6717 grad_norm: 3.1323 loss: 1.2300 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2300 2023/04/14 09:33:16 - mmengine - INFO - Epoch(train) [65][1280/1879] lr: 2.0000e-03 eta: 6:49:17 time: 0.3335 data_time: 0.0163 memory: 6717 grad_norm: 3.2182 loss: 1.3783 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3783 2023/04/14 09:33:24 - mmengine - INFO - Epoch(train) [65][1300/1879] lr: 2.0000e-03 eta: 6:49:10 time: 0.3956 data_time: 0.0136 memory: 6717 grad_norm: 3.1783 loss: 1.0231 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0231 2023/04/14 09:33:30 - mmengine - INFO - Epoch(train) [65][1320/1879] lr: 2.0000e-03 eta: 6:49:02 time: 0.3124 data_time: 0.0167 memory: 6717 grad_norm: 3.2676 loss: 1.1325 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1325 2023/04/14 09:33:38 - mmengine - INFO - Epoch(train) [65][1340/1879] lr: 2.0000e-03 eta: 6:48:54 time: 0.3927 data_time: 0.0127 memory: 6717 grad_norm: 3.2813 loss: 0.9970 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9970 2023/04/14 09:33:44 - mmengine - INFO - Epoch(train) [65][1360/1879] lr: 2.0000e-03 eta: 6:48:46 time: 0.3099 data_time: 0.0162 memory: 6717 grad_norm: 3.1500 loss: 1.2568 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2568 2023/04/14 09:33:52 - mmengine - INFO - Epoch(train) [65][1380/1879] lr: 2.0000e-03 eta: 6:48:39 time: 0.3872 data_time: 0.0156 memory: 6717 grad_norm: 3.2500 loss: 1.2963 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2963 2023/04/14 09:33:58 - mmengine - INFO - Epoch(train) [65][1400/1879] lr: 2.0000e-03 eta: 6:48:31 time: 0.3343 data_time: 0.0165 memory: 6717 grad_norm: 3.2010 loss: 1.2505 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2505 2023/04/14 09:34:06 - mmengine - INFO - Epoch(train) [65][1420/1879] lr: 2.0000e-03 eta: 6:48:24 time: 0.3763 data_time: 0.0130 memory: 6717 grad_norm: 3.2247 loss: 1.1336 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1336 2023/04/14 09:34:14 - mmengine - INFO - Epoch(train) [65][1440/1879] lr: 2.0000e-03 eta: 6:48:17 time: 0.3981 data_time: 0.0164 memory: 6717 grad_norm: 3.2299 loss: 1.1569 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1569 2023/04/14 09:34:20 - mmengine - INFO - Epoch(train) [65][1460/1879] lr: 2.0000e-03 eta: 6:48:09 time: 0.3277 data_time: 0.0143 memory: 6717 grad_norm: 3.2140 loss: 1.1917 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1917 2023/04/14 09:34:28 - mmengine - INFO - Epoch(train) [65][1480/1879] lr: 2.0000e-03 eta: 6:48:02 time: 0.3919 data_time: 0.0149 memory: 6717 grad_norm: 3.2585 loss: 1.3292 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3292 2023/04/14 09:34:35 - mmengine - INFO - Epoch(train) [65][1500/1879] lr: 2.0000e-03 eta: 6:47:54 time: 0.3496 data_time: 0.0140 memory: 6717 grad_norm: 3.2267 loss: 1.3655 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3655 2023/04/14 09:34:42 - mmengine - INFO - Epoch(train) [65][1520/1879] lr: 2.0000e-03 eta: 6:47:47 time: 0.3464 data_time: 0.0155 memory: 6717 grad_norm: 3.1887 loss: 1.1842 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1842 2023/04/14 09:34:51 - mmengine - INFO - Epoch(train) [65][1540/1879] lr: 2.0000e-03 eta: 6:47:40 time: 0.4193 data_time: 0.0148 memory: 6717 grad_norm: 3.1203 loss: 0.9889 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9889 2023/04/14 09:34:57 - mmengine - INFO - Epoch(train) [65][1560/1879] lr: 2.0000e-03 eta: 6:47:32 time: 0.3179 data_time: 0.0153 memory: 6717 grad_norm: 3.1984 loss: 1.3668 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3668 2023/04/14 09:35:05 - mmengine - INFO - Epoch(train) [65][1580/1879] lr: 2.0000e-03 eta: 6:47:25 time: 0.4082 data_time: 0.0145 memory: 6717 grad_norm: 3.2646 loss: 1.2819 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2819 2023/04/14 09:35:12 - mmengine - INFO - Epoch(train) [65][1600/1879] lr: 2.0000e-03 eta: 6:47:17 time: 0.3275 data_time: 0.0161 memory: 6717 grad_norm: 3.2535 loss: 1.1712 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1712 2023/04/14 09:35:19 - mmengine - INFO - Epoch(train) [65][1620/1879] lr: 2.0000e-03 eta: 6:47:10 time: 0.3811 data_time: 0.0142 memory: 6717 grad_norm: 3.1811 loss: 1.2323 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2323 2023/04/14 09:35:26 - mmengine - INFO - Epoch(train) [65][1640/1879] lr: 2.0000e-03 eta: 6:47:02 time: 0.3578 data_time: 0.0166 memory: 6717 grad_norm: 3.1476 loss: 1.2631 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2631 2023/04/14 09:35:34 - mmengine - INFO - Epoch(train) [65][1660/1879] lr: 2.0000e-03 eta: 6:46:55 time: 0.3568 data_time: 0.0141 memory: 6717 grad_norm: 3.1386 loss: 1.0919 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0919 2023/04/14 09:35:42 - mmengine - INFO - Epoch(train) [65][1680/1879] lr: 2.0000e-03 eta: 6:46:48 time: 0.4086 data_time: 0.0154 memory: 6717 grad_norm: 3.2125 loss: 1.2122 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2122 2023/04/14 09:35:49 - mmengine - INFO - Epoch(train) [65][1700/1879] lr: 2.0000e-03 eta: 6:46:40 time: 0.3840 data_time: 0.0155 memory: 6717 grad_norm: 3.2109 loss: 0.9782 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9782 2023/04/14 09:35:56 - mmengine - INFO - Epoch(train) [65][1720/1879] lr: 2.0000e-03 eta: 6:46:33 time: 0.3521 data_time: 0.0145 memory: 6717 grad_norm: 3.1966 loss: 1.2425 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2425 2023/04/14 09:36:05 - mmengine - INFO - Epoch(train) [65][1740/1879] lr: 2.0000e-03 eta: 6:46:26 time: 0.4143 data_time: 0.0135 memory: 6717 grad_norm: 3.1291 loss: 1.3092 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3092 2023/04/14 09:36:05 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 09:36:11 - mmengine - INFO - Epoch(train) [65][1760/1879] lr: 2.0000e-03 eta: 6:46:18 time: 0.3060 data_time: 0.0149 memory: 6717 grad_norm: 3.1524 loss: 1.2161 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2161 2023/04/14 09:36:19 - mmengine - INFO - Epoch(train) [65][1780/1879] lr: 2.0000e-03 eta: 6:46:11 time: 0.4152 data_time: 0.0126 memory: 6717 grad_norm: 3.2574 loss: 1.2534 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2534 2023/04/14 09:36:26 - mmengine - INFO - Epoch(train) [65][1800/1879] lr: 2.0000e-03 eta: 6:46:03 time: 0.3298 data_time: 0.0141 memory: 6717 grad_norm: 3.1667 loss: 1.1347 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1347 2023/04/14 09:36:34 - mmengine - INFO - Epoch(train) [65][1820/1879] lr: 2.0000e-03 eta: 6:45:56 time: 0.3918 data_time: 0.0156 memory: 6717 grad_norm: 3.1934 loss: 1.0619 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0619 2023/04/14 09:36:40 - mmengine - INFO - Epoch(train) [65][1840/1879] lr: 2.0000e-03 eta: 6:45:48 time: 0.3259 data_time: 0.0132 memory: 6717 grad_norm: 3.2229 loss: 1.0963 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0963 2023/04/14 09:36:48 - mmengine - INFO - Epoch(train) [65][1860/1879] lr: 2.0000e-03 eta: 6:45:41 time: 0.4095 data_time: 0.0140 memory: 6717 grad_norm: 3.2488 loss: 1.1125 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1125 2023/04/14 09:36:54 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 09:36:54 - mmengine - INFO - Epoch(train) [65][1879/1879] lr: 2.0000e-03 eta: 6:45:33 time: 0.3187 data_time: 0.0118 memory: 6717 grad_norm: 3.2640 loss: 1.0683 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 1.0683 2023/04/14 09:37:04 - mmengine - INFO - Epoch(val) [65][ 20/155] eta: 0:01:01 time: 0.4573 data_time: 0.4240 memory: 1391 2023/04/14 09:37:10 - mmengine - INFO - Epoch(val) [65][ 40/155] eta: 0:00:43 time: 0.3054 data_time: 0.2719 memory: 1391 2023/04/14 09:37:19 - mmengine - INFO - Epoch(val) [65][ 60/155] eta: 0:00:38 time: 0.4509 data_time: 0.4176 memory: 1391 2023/04/14 09:37:25 - mmengine - INFO - Epoch(val) [65][ 80/155] eta: 0:00:28 time: 0.3177 data_time: 0.2840 memory: 1391 2023/04/14 09:37:34 - mmengine - INFO - Epoch(val) [65][100/155] eta: 0:00:21 time: 0.4519 data_time: 0.4183 memory: 1391 2023/04/14 09:37:40 - mmengine - INFO - Epoch(val) [65][120/155] eta: 0:00:13 time: 0.2998 data_time: 0.2663 memory: 1391 2023/04/14 09:37:50 - mmengine - INFO - Epoch(val) [65][140/155] eta: 0:00:05 time: 0.4828 data_time: 0.4499 memory: 1391 2023/04/14 09:37:57 - mmengine - INFO - Epoch(val) [65][155/155] acc/top1: 0.6629 acc/top5: 0.8730 acc/mean1: 0.6628 data_time: 0.4207 time: 0.4531 2023/04/14 09:38:07 - mmengine - INFO - Epoch(train) [66][ 20/1879] lr: 2.0000e-03 eta: 6:45:27 time: 0.4866 data_time: 0.2415 memory: 6717 grad_norm: 3.2268 loss: 1.1792 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1792 2023/04/14 09:38:14 - mmengine - INFO - Epoch(train) [66][ 40/1879] lr: 2.0000e-03 eta: 6:45:20 time: 0.3480 data_time: 0.0758 memory: 6717 grad_norm: 3.2101 loss: 1.1906 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1906 2023/04/14 09:38:22 - mmengine - INFO - Epoch(train) [66][ 60/1879] lr: 2.0000e-03 eta: 6:45:13 time: 0.4130 data_time: 0.0165 memory: 6717 grad_norm: 3.1805 loss: 1.2061 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2061 2023/04/14 09:38:28 - mmengine - INFO - Epoch(train) [66][ 80/1879] lr: 2.0000e-03 eta: 6:45:05 time: 0.3265 data_time: 0.0138 memory: 6717 grad_norm: 3.2401 loss: 1.2208 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2208 2023/04/14 09:38:37 - mmengine - INFO - Epoch(train) [66][ 100/1879] lr: 2.0000e-03 eta: 6:44:58 time: 0.4393 data_time: 0.0163 memory: 6717 grad_norm: 3.0684 loss: 1.1776 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1776 2023/04/14 09:38:44 - mmengine - INFO - Epoch(train) [66][ 120/1879] lr: 2.0000e-03 eta: 6:44:50 time: 0.3254 data_time: 0.0127 memory: 6717 grad_norm: 3.1976 loss: 1.2418 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2418 2023/04/14 09:38:52 - mmengine - INFO - Epoch(train) [66][ 140/1879] lr: 2.0000e-03 eta: 6:44:44 time: 0.4216 data_time: 0.0160 memory: 6717 grad_norm: 3.1993 loss: 1.3501 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3501 2023/04/14 09:38:59 - mmengine - INFO - Epoch(train) [66][ 160/1879] lr: 2.0000e-03 eta: 6:44:36 time: 0.3168 data_time: 0.0139 memory: 6717 grad_norm: 3.1969 loss: 1.2093 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2093 2023/04/14 09:39:07 - mmengine - INFO - Epoch(train) [66][ 180/1879] lr: 2.0000e-03 eta: 6:44:29 time: 0.4379 data_time: 0.0170 memory: 6717 grad_norm: 3.1731 loss: 1.2045 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2045 2023/04/14 09:39:14 - mmengine - INFO - Epoch(train) [66][ 200/1879] lr: 2.0000e-03 eta: 6:44:21 time: 0.3553 data_time: 0.0134 memory: 6717 grad_norm: 3.2371 loss: 1.2460 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2460 2023/04/14 09:39:23 - mmengine - INFO - Epoch(train) [66][ 220/1879] lr: 2.0000e-03 eta: 6:44:15 time: 0.4312 data_time: 0.0137 memory: 6717 grad_norm: 3.2184 loss: 1.2027 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2027 2023/04/14 09:39:30 - mmengine - INFO - Epoch(train) [66][ 240/1879] lr: 2.0000e-03 eta: 6:44:07 time: 0.3268 data_time: 0.0145 memory: 6717 grad_norm: 3.1890 loss: 1.1722 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1722 2023/04/14 09:39:38 - mmengine - INFO - Epoch(train) [66][ 260/1879] lr: 2.0000e-03 eta: 6:44:00 time: 0.4106 data_time: 0.0154 memory: 6717 grad_norm: 3.1667 loss: 1.2487 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2487 2023/04/14 09:39:45 - mmengine - INFO - Epoch(train) [66][ 280/1879] lr: 2.0000e-03 eta: 6:43:52 time: 0.3540 data_time: 0.0134 memory: 6717 grad_norm: 3.1957 loss: 1.1327 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1327 2023/04/14 09:39:53 - mmengine - INFO - Epoch(train) [66][ 300/1879] lr: 2.0000e-03 eta: 6:43:45 time: 0.4239 data_time: 0.0134 memory: 6717 grad_norm: 3.1633 loss: 1.1695 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1695 2023/04/14 09:40:00 - mmengine - INFO - Epoch(train) [66][ 320/1879] lr: 2.0000e-03 eta: 6:43:37 time: 0.3123 data_time: 0.0164 memory: 6717 grad_norm: 3.1915 loss: 1.1100 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1100 2023/04/14 09:40:08 - mmengine - INFO - Epoch(train) [66][ 340/1879] lr: 2.0000e-03 eta: 6:43:31 time: 0.4331 data_time: 0.0156 memory: 6717 grad_norm: 3.2095 loss: 1.0508 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0508 2023/04/14 09:40:14 - mmengine - INFO - Epoch(train) [66][ 360/1879] lr: 2.0000e-03 eta: 6:43:22 time: 0.2986 data_time: 0.0134 memory: 6717 grad_norm: 3.2140 loss: 1.3565 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3565 2023/04/14 09:40:23 - mmengine - INFO - Epoch(train) [66][ 380/1879] lr: 2.0000e-03 eta: 6:43:16 time: 0.4519 data_time: 0.0154 memory: 6717 grad_norm: 3.2529 loss: 1.1226 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1226 2023/04/14 09:40:30 - mmengine - INFO - Epoch(train) [66][ 400/1879] lr: 2.0000e-03 eta: 6:43:08 time: 0.3504 data_time: 0.0133 memory: 6717 grad_norm: 3.2101 loss: 1.2585 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2585 2023/04/14 09:40:38 - mmengine - INFO - Epoch(train) [66][ 420/1879] lr: 2.0000e-03 eta: 6:43:01 time: 0.4076 data_time: 0.0139 memory: 6717 grad_norm: 3.1889 loss: 1.3556 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3556 2023/04/14 09:40:45 - mmengine - INFO - Epoch(train) [66][ 440/1879] lr: 2.0000e-03 eta: 6:42:53 time: 0.3066 data_time: 0.0147 memory: 6717 grad_norm: 3.2289 loss: 1.2115 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2115 2023/04/14 09:40:53 - mmengine - INFO - Epoch(train) [66][ 460/1879] lr: 2.0000e-03 eta: 6:42:46 time: 0.4165 data_time: 0.0156 memory: 6717 grad_norm: 3.1801 loss: 1.1170 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1170 2023/04/14 09:41:00 - mmengine - INFO - Epoch(train) [66][ 480/1879] lr: 2.0000e-03 eta: 6:42:39 time: 0.3359 data_time: 0.0150 memory: 6717 grad_norm: 3.2941 loss: 1.2578 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2578 2023/04/14 09:41:08 - mmengine - INFO - Epoch(train) [66][ 500/1879] lr: 2.0000e-03 eta: 6:42:32 time: 0.4157 data_time: 0.0149 memory: 6717 grad_norm: 3.1422 loss: 1.0868 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0868 2023/04/14 09:41:15 - mmengine - INFO - Epoch(train) [66][ 520/1879] lr: 2.0000e-03 eta: 6:42:24 time: 0.3357 data_time: 0.0146 memory: 6717 grad_norm: 3.1986 loss: 1.2079 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.2079 2023/04/14 09:41:24 - mmengine - INFO - Epoch(train) [66][ 540/1879] lr: 2.0000e-03 eta: 6:42:18 time: 0.4781 data_time: 0.0134 memory: 6717 grad_norm: 3.0848 loss: 1.1527 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1527 2023/04/14 09:41:31 - mmengine - INFO - Epoch(train) [66][ 560/1879] lr: 2.0000e-03 eta: 6:42:10 time: 0.3349 data_time: 0.0143 memory: 6717 grad_norm: 3.2610 loss: 1.2472 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2472 2023/04/14 09:41:39 - mmengine - INFO - Epoch(train) [66][ 580/1879] lr: 2.0000e-03 eta: 6:42:03 time: 0.3851 data_time: 0.0139 memory: 6717 grad_norm: 3.1770 loss: 1.1376 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1376 2023/04/14 09:41:46 - mmengine - INFO - Epoch(train) [66][ 600/1879] lr: 2.0000e-03 eta: 6:41:55 time: 0.3653 data_time: 0.0160 memory: 6717 grad_norm: 3.2521 loss: 1.2398 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2398 2023/04/14 09:41:54 - mmengine - INFO - Epoch(train) [66][ 620/1879] lr: 2.0000e-03 eta: 6:41:48 time: 0.4090 data_time: 0.0135 memory: 6717 grad_norm: 3.1582 loss: 1.0106 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0106 2023/04/14 09:42:00 - mmengine - INFO - Epoch(train) [66][ 640/1879] lr: 2.0000e-03 eta: 6:41:40 time: 0.3010 data_time: 0.0146 memory: 6717 grad_norm: 3.2050 loss: 1.2590 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2590 2023/04/14 09:42:09 - mmengine - INFO - Epoch(train) [66][ 660/1879] lr: 2.0000e-03 eta: 6:41:33 time: 0.4276 data_time: 0.0154 memory: 6717 grad_norm: 3.2291 loss: 1.4091 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.4091 2023/04/14 09:42:15 - mmengine - INFO - Epoch(train) [66][ 680/1879] lr: 2.0000e-03 eta: 6:41:25 time: 0.2994 data_time: 0.0126 memory: 6717 grad_norm: 3.2210 loss: 1.2148 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.2148 2023/04/14 09:42:23 - mmengine - INFO - Epoch(train) [66][ 700/1879] lr: 2.0000e-03 eta: 6:41:18 time: 0.4003 data_time: 0.0141 memory: 6717 grad_norm: 3.1850 loss: 1.0756 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0756 2023/04/14 09:42:29 - mmengine - INFO - Epoch(train) [66][ 720/1879] lr: 2.0000e-03 eta: 6:41:10 time: 0.2979 data_time: 0.0137 memory: 6717 grad_norm: 3.2402 loss: 1.1537 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1537 2023/04/14 09:42:37 - mmengine - INFO - Epoch(train) [66][ 740/1879] lr: 2.0000e-03 eta: 6:41:03 time: 0.4293 data_time: 0.0155 memory: 6717 grad_norm: 3.2540 loss: 1.1221 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1221 2023/04/14 09:42:44 - mmengine - INFO - Epoch(train) [66][ 760/1879] lr: 2.0000e-03 eta: 6:40:55 time: 0.3193 data_time: 0.0141 memory: 6717 grad_norm: 3.3238 loss: 1.2484 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2484 2023/04/14 09:42:52 - mmengine - INFO - Epoch(train) [66][ 780/1879] lr: 2.0000e-03 eta: 6:40:48 time: 0.4206 data_time: 0.0146 memory: 6717 grad_norm: 3.2000 loss: 1.2034 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2034 2023/04/14 09:42:59 - mmengine - INFO - Epoch(train) [66][ 800/1879] lr: 2.0000e-03 eta: 6:40:40 time: 0.3245 data_time: 0.0150 memory: 6717 grad_norm: 3.1515 loss: 1.2916 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2916 2023/04/14 09:43:07 - mmengine - INFO - Epoch(train) [66][ 820/1879] lr: 2.0000e-03 eta: 6:40:33 time: 0.4189 data_time: 0.0157 memory: 6717 grad_norm: 3.2046 loss: 1.2873 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2873 2023/04/14 09:43:14 - mmengine - INFO - Epoch(train) [66][ 840/1879] lr: 2.0000e-03 eta: 6:40:26 time: 0.3665 data_time: 0.0142 memory: 6717 grad_norm: 3.2263 loss: 1.2695 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2695 2023/04/14 09:43:22 - mmengine - INFO - Epoch(train) [66][ 860/1879] lr: 2.0000e-03 eta: 6:40:19 time: 0.3742 data_time: 0.0145 memory: 6717 grad_norm: 3.2351 loss: 1.3050 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3050 2023/04/14 09:43:23 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 09:43:30 - mmengine - INFO - Epoch(train) [66][ 880/1879] lr: 2.0000e-03 eta: 6:40:12 time: 0.4090 data_time: 0.0143 memory: 6717 grad_norm: 3.1903 loss: 1.2717 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2717 2023/04/14 09:43:37 - mmengine - INFO - Epoch(train) [66][ 900/1879] lr: 2.0000e-03 eta: 6:40:04 time: 0.3284 data_time: 0.0164 memory: 6717 grad_norm: 3.2706 loss: 1.1683 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1683 2023/04/14 09:43:45 - mmengine - INFO - Epoch(train) [66][ 920/1879] lr: 2.0000e-03 eta: 6:39:57 time: 0.4120 data_time: 0.0154 memory: 6717 grad_norm: 3.1498 loss: 1.1913 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1913 2023/04/14 09:43:52 - mmengine - INFO - Epoch(train) [66][ 940/1879] lr: 2.0000e-03 eta: 6:39:49 time: 0.3428 data_time: 0.0130 memory: 6717 grad_norm: 3.2209 loss: 1.1052 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1052 2023/04/14 09:44:00 - mmengine - INFO - Epoch(train) [66][ 960/1879] lr: 2.0000e-03 eta: 6:39:42 time: 0.4324 data_time: 0.0146 memory: 6717 grad_norm: 3.2256 loss: 1.2991 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2991 2023/04/14 09:44:07 - mmengine - INFO - Epoch(train) [66][ 980/1879] lr: 2.0000e-03 eta: 6:39:35 time: 0.3372 data_time: 0.0142 memory: 6717 grad_norm: 3.2618 loss: 1.3277 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3277 2023/04/14 09:44:15 - mmengine - INFO - Epoch(train) [66][1000/1879] lr: 2.0000e-03 eta: 6:39:28 time: 0.4114 data_time: 0.0139 memory: 6717 grad_norm: 3.1778 loss: 1.2870 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2870 2023/04/14 09:44:22 - mmengine - INFO - Epoch(train) [66][1020/1879] lr: 2.0000e-03 eta: 6:39:20 time: 0.3255 data_time: 0.0162 memory: 6717 grad_norm: 3.1670 loss: 1.2272 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2272 2023/04/14 09:44:30 - mmengine - INFO - Epoch(train) [66][1040/1879] lr: 2.0000e-03 eta: 6:39:13 time: 0.3925 data_time: 0.0163 memory: 6717 grad_norm: 3.1753 loss: 1.0924 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0924 2023/04/14 09:44:36 - mmengine - INFO - Epoch(train) [66][1060/1879] lr: 2.0000e-03 eta: 6:39:05 time: 0.3024 data_time: 0.0131 memory: 6717 grad_norm: 3.1722 loss: 1.0521 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0521 2023/04/14 09:44:43 - mmengine - INFO - Epoch(train) [66][1080/1879] lr: 2.0000e-03 eta: 6:38:57 time: 0.3881 data_time: 0.0317 memory: 6717 grad_norm: 3.1715 loss: 1.1775 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1775 2023/04/14 09:44:50 - mmengine - INFO - Epoch(train) [66][1100/1879] lr: 2.0000e-03 eta: 6:38:50 time: 0.3379 data_time: 0.0507 memory: 6717 grad_norm: 3.1834 loss: 1.0838 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0838 2023/04/14 09:44:57 - mmengine - INFO - Epoch(train) [66][1120/1879] lr: 2.0000e-03 eta: 6:38:42 time: 0.3564 data_time: 0.0338 memory: 6717 grad_norm: 3.2091 loss: 1.2947 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2947 2023/04/14 09:45:05 - mmengine - INFO - Epoch(train) [66][1140/1879] lr: 2.0000e-03 eta: 6:38:35 time: 0.3934 data_time: 0.0262 memory: 6717 grad_norm: 3.1366 loss: 1.2004 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2004 2023/04/14 09:45:12 - mmengine - INFO - Epoch(train) [66][1160/1879] lr: 2.0000e-03 eta: 6:38:27 time: 0.3551 data_time: 0.1046 memory: 6717 grad_norm: 3.1853 loss: 1.1617 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1617 2023/04/14 09:45:20 - mmengine - INFO - Epoch(train) [66][1180/1879] lr: 2.0000e-03 eta: 6:38:20 time: 0.3861 data_time: 0.0941 memory: 6717 grad_norm: 3.2176 loss: 1.1359 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1359 2023/04/14 09:45:27 - mmengine - INFO - Epoch(train) [66][1200/1879] lr: 2.0000e-03 eta: 6:38:12 time: 0.3398 data_time: 0.1141 memory: 6717 grad_norm: 3.2442 loss: 1.2175 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2175 2023/04/14 09:45:35 - mmengine - INFO - Epoch(train) [66][1220/1879] lr: 2.0000e-03 eta: 6:38:05 time: 0.3942 data_time: 0.0839 memory: 6717 grad_norm: 3.2453 loss: 1.1156 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1156 2023/04/14 09:45:41 - mmengine - INFO - Epoch(train) [66][1240/1879] lr: 2.0000e-03 eta: 6:37:57 time: 0.3320 data_time: 0.0774 memory: 6717 grad_norm: 3.2297 loss: 1.3108 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3108 2023/04/14 09:45:49 - mmengine - INFO - Epoch(train) [66][1260/1879] lr: 2.0000e-03 eta: 6:37:50 time: 0.4024 data_time: 0.0661 memory: 6717 grad_norm: 3.2058 loss: 1.2276 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2276 2023/04/14 09:45:56 - mmengine - INFO - Epoch(train) [66][1280/1879] lr: 2.0000e-03 eta: 6:37:43 time: 0.3454 data_time: 0.0484 memory: 6717 grad_norm: 3.1454 loss: 1.1093 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1093 2023/04/14 09:46:05 - mmengine - INFO - Epoch(train) [66][1300/1879] lr: 2.0000e-03 eta: 6:37:36 time: 0.4269 data_time: 0.0630 memory: 6717 grad_norm: 3.2111 loss: 1.2382 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2382 2023/04/14 09:46:12 - mmengine - INFO - Epoch(train) [66][1320/1879] lr: 2.0000e-03 eta: 6:37:28 time: 0.3426 data_time: 0.0740 memory: 6717 grad_norm: 3.2669 loss: 1.2968 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2968 2023/04/14 09:46:20 - mmengine - INFO - Epoch(train) [66][1340/1879] lr: 2.0000e-03 eta: 6:37:21 time: 0.4198 data_time: 0.0688 memory: 6717 grad_norm: 3.2243 loss: 1.2433 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2433 2023/04/14 09:46:27 - mmengine - INFO - Epoch(train) [66][1360/1879] lr: 2.0000e-03 eta: 6:37:14 time: 0.3279 data_time: 0.0124 memory: 6717 grad_norm: 3.2096 loss: 1.2655 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2655 2023/04/14 09:46:35 - mmengine - INFO - Epoch(train) [66][1380/1879] lr: 2.0000e-03 eta: 6:37:07 time: 0.4221 data_time: 0.0162 memory: 6717 grad_norm: 3.2568 loss: 1.2558 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2558 2023/04/14 09:46:42 - mmengine - INFO - Epoch(train) [66][1400/1879] lr: 2.0000e-03 eta: 6:36:59 time: 0.3203 data_time: 0.0129 memory: 6717 grad_norm: 3.1377 loss: 1.2056 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2056 2023/04/14 09:46:49 - mmengine - INFO - Epoch(train) [66][1420/1879] lr: 2.0000e-03 eta: 6:36:52 time: 0.3881 data_time: 0.0145 memory: 6717 grad_norm: 3.1994 loss: 1.4004 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.4004 2023/04/14 09:46:56 - mmengine - INFO - Epoch(train) [66][1440/1879] lr: 2.0000e-03 eta: 6:36:44 time: 0.3208 data_time: 0.0156 memory: 6717 grad_norm: 3.2622 loss: 1.2347 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2347 2023/04/14 09:47:04 - mmengine - INFO - Epoch(train) [66][1460/1879] lr: 2.0000e-03 eta: 6:36:37 time: 0.4273 data_time: 0.0137 memory: 6717 grad_norm: 3.2188 loss: 1.2875 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2875 2023/04/14 09:47:11 - mmengine - INFO - Epoch(train) [66][1480/1879] lr: 2.0000e-03 eta: 6:36:29 time: 0.3210 data_time: 0.0145 memory: 6717 grad_norm: 3.2182 loss: 1.0999 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0999 2023/04/14 09:47:18 - mmengine - INFO - Epoch(train) [66][1500/1879] lr: 2.0000e-03 eta: 6:36:22 time: 0.3831 data_time: 0.0136 memory: 6717 grad_norm: 3.2581 loss: 1.1718 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1718 2023/04/14 09:47:25 - mmengine - INFO - Epoch(train) [66][1520/1879] lr: 2.0000e-03 eta: 6:36:14 time: 0.3241 data_time: 0.0144 memory: 6717 grad_norm: 3.1944 loss: 1.2909 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2909 2023/04/14 09:47:33 - mmengine - INFO - Epoch(train) [66][1540/1879] lr: 2.0000e-03 eta: 6:36:07 time: 0.4094 data_time: 0.0237 memory: 6717 grad_norm: 3.2093 loss: 1.2641 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2641 2023/04/14 09:47:40 - mmengine - INFO - Epoch(train) [66][1560/1879] lr: 2.0000e-03 eta: 6:35:59 time: 0.3424 data_time: 0.0124 memory: 6717 grad_norm: 3.2326 loss: 1.2280 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2280 2023/04/14 09:47:48 - mmengine - INFO - Epoch(train) [66][1580/1879] lr: 2.0000e-03 eta: 6:35:52 time: 0.4077 data_time: 0.0142 memory: 6717 grad_norm: 3.1480 loss: 1.2820 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2820 2023/04/14 09:47:54 - mmengine - INFO - Epoch(train) [66][1600/1879] lr: 2.0000e-03 eta: 6:35:44 time: 0.3096 data_time: 0.0147 memory: 6717 grad_norm: 3.2430 loss: 1.2393 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2393 2023/04/14 09:48:02 - mmengine - INFO - Epoch(train) [66][1620/1879] lr: 2.0000e-03 eta: 6:35:37 time: 0.3930 data_time: 0.0202 memory: 6717 grad_norm: 3.2202 loss: 1.1254 top1_acc: 0.4375 top5_acc: 0.5625 loss_cls: 1.1254 2023/04/14 09:48:09 - mmengine - INFO - Epoch(train) [66][1640/1879] lr: 2.0000e-03 eta: 6:35:29 time: 0.3316 data_time: 0.0134 memory: 6717 grad_norm: 3.2646 loss: 1.3071 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3071 2023/04/14 09:48:17 - mmengine - INFO - Epoch(train) [66][1660/1879] lr: 2.0000e-03 eta: 6:35:22 time: 0.4081 data_time: 0.1114 memory: 6717 grad_norm: 3.2372 loss: 1.1118 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1118 2023/04/14 09:48:24 - mmengine - INFO - Epoch(train) [66][1680/1879] lr: 2.0000e-03 eta: 6:35:14 time: 0.3345 data_time: 0.1229 memory: 6717 grad_norm: 3.1291 loss: 1.2073 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2073 2023/04/14 09:48:31 - mmengine - INFO - Epoch(train) [66][1700/1879] lr: 2.0000e-03 eta: 6:35:07 time: 0.3768 data_time: 0.1029 memory: 6717 grad_norm: 3.1800 loss: 1.2023 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2023 2023/04/14 09:48:38 - mmengine - INFO - Epoch(train) [66][1720/1879] lr: 2.0000e-03 eta: 6:35:00 time: 0.3622 data_time: 0.0868 memory: 6717 grad_norm: 3.2391 loss: 1.1072 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1072 2023/04/14 09:48:46 - mmengine - INFO - Epoch(train) [66][1740/1879] lr: 2.0000e-03 eta: 6:34:52 time: 0.3786 data_time: 0.1195 memory: 6717 grad_norm: 3.2072 loss: 1.1990 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1990 2023/04/14 09:48:53 - mmengine - INFO - Epoch(train) [66][1760/1879] lr: 2.0000e-03 eta: 6:34:45 time: 0.3622 data_time: 0.0746 memory: 6717 grad_norm: 3.1929 loss: 1.3688 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3688 2023/04/14 09:49:00 - mmengine - INFO - Epoch(train) [66][1780/1879] lr: 2.0000e-03 eta: 6:34:37 time: 0.3267 data_time: 0.0388 memory: 6717 grad_norm: 3.2516 loss: 1.2445 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2445 2023/04/14 09:49:08 - mmengine - INFO - Epoch(train) [66][1800/1879] lr: 2.0000e-03 eta: 6:34:30 time: 0.4006 data_time: 0.0498 memory: 6717 grad_norm: 3.1772 loss: 1.2198 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2198 2023/04/14 09:49:15 - mmengine - INFO - Epoch(train) [66][1820/1879] lr: 2.0000e-03 eta: 6:34:22 time: 0.3403 data_time: 0.0810 memory: 6717 grad_norm: 3.2016 loss: 1.3758 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3758 2023/04/14 09:49:22 - mmengine - INFO - Epoch(train) [66][1840/1879] lr: 2.0000e-03 eta: 6:34:14 time: 0.3500 data_time: 0.1319 memory: 6717 grad_norm: 3.2278 loss: 1.1477 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1477 2023/04/14 09:49:30 - mmengine - INFO - Epoch(train) [66][1860/1879] lr: 2.0000e-03 eta: 6:34:08 time: 0.4133 data_time: 0.1649 memory: 6717 grad_norm: 3.2403 loss: 1.1777 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1777 2023/04/14 09:49:31 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 09:49:36 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 09:49:36 - mmengine - INFO - Epoch(train) [66][1879/1879] lr: 2.0000e-03 eta: 6:34:00 time: 0.3112 data_time: 0.0954 memory: 6717 grad_norm: 3.4206 loss: 1.0512 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 1.0512 2023/04/14 09:49:36 - mmengine - INFO - Saving checkpoint at 66 epochs 2023/04/14 09:49:46 - mmengine - INFO - Epoch(val) [66][ 20/155] eta: 0:01:02 time: 0.4652 data_time: 0.4323 memory: 1391 2023/04/14 09:49:52 - mmengine - INFO - Epoch(val) [66][ 40/155] eta: 0:00:44 time: 0.3165 data_time: 0.2832 memory: 1391 2023/04/14 09:50:01 - mmengine - INFO - Epoch(val) [66][ 60/155] eta: 0:00:38 time: 0.4305 data_time: 0.3976 memory: 1391 2023/04/14 09:50:07 - mmengine - INFO - Epoch(val) [66][ 80/155] eta: 0:00:28 time: 0.3193 data_time: 0.2860 memory: 1391 2023/04/14 09:50:16 - mmengine - INFO - Epoch(val) [66][100/155] eta: 0:00:21 time: 0.4530 data_time: 0.4198 memory: 1391 2023/04/14 09:50:22 - mmengine - INFO - Epoch(val) [66][120/155] eta: 0:00:13 time: 0.2966 data_time: 0.2639 memory: 1391 2023/04/14 09:50:31 - mmengine - INFO - Epoch(val) [66][140/155] eta: 0:00:05 time: 0.4437 data_time: 0.4101 memory: 1391 2023/04/14 09:50:38 - mmengine - INFO - Epoch(val) [66][155/155] acc/top1: 0.6642 acc/top5: 0.8715 acc/mean1: 0.6642 data_time: 0.3716 time: 0.4039 2023/04/14 09:50:48 - mmengine - INFO - Epoch(train) [67][ 20/1879] lr: 2.0000e-03 eta: 6:33:54 time: 0.4972 data_time: 0.2954 memory: 6717 grad_norm: 3.2616 loss: 1.3203 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.3203 2023/04/14 09:50:54 - mmengine - INFO - Epoch(train) [67][ 40/1879] lr: 2.0000e-03 eta: 6:33:46 time: 0.3355 data_time: 0.1352 memory: 6717 grad_norm: 3.1474 loss: 1.2549 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.2549 2023/04/14 09:51:03 - mmengine - INFO - Epoch(train) [67][ 60/1879] lr: 2.0000e-03 eta: 6:33:40 time: 0.4443 data_time: 0.2843 memory: 6717 grad_norm: 3.1946 loss: 1.2930 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2930 2023/04/14 09:51:10 - mmengine - INFO - Epoch(train) [67][ 80/1879] lr: 2.0000e-03 eta: 6:33:32 time: 0.3331 data_time: 0.2028 memory: 6717 grad_norm: 3.1608 loss: 1.0956 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0956 2023/04/14 09:51:19 - mmengine - INFO - Epoch(train) [67][ 100/1879] lr: 2.0000e-03 eta: 6:33:25 time: 0.4342 data_time: 0.2831 memory: 6717 grad_norm: 3.2246 loss: 1.1956 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1956 2023/04/14 09:51:25 - mmengine - INFO - Epoch(train) [67][ 120/1879] lr: 2.0000e-03 eta: 6:33:17 time: 0.3337 data_time: 0.1921 memory: 6717 grad_norm: 3.1975 loss: 1.2014 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2014 2023/04/14 09:51:33 - mmengine - INFO - Epoch(train) [67][ 140/1879] lr: 2.0000e-03 eta: 6:33:10 time: 0.3933 data_time: 0.2220 memory: 6717 grad_norm: 3.1495 loss: 1.2189 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2189 2023/04/14 09:51:39 - mmengine - INFO - Epoch(train) [67][ 160/1879] lr: 2.0000e-03 eta: 6:33:02 time: 0.3087 data_time: 0.1062 memory: 6717 grad_norm: 3.2172 loss: 1.1695 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1695 2023/04/14 09:51:48 - mmengine - INFO - Epoch(train) [67][ 180/1879] lr: 2.0000e-03 eta: 6:32:55 time: 0.4096 data_time: 0.2080 memory: 6717 grad_norm: 3.1823 loss: 1.1573 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1573 2023/04/14 09:51:54 - mmengine - INFO - Epoch(train) [67][ 200/1879] lr: 2.0000e-03 eta: 6:32:47 time: 0.3115 data_time: 0.1520 memory: 6717 grad_norm: 3.2145 loss: 1.2533 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2533 2023/04/14 09:52:02 - mmengine - INFO - Epoch(train) [67][ 220/1879] lr: 2.0000e-03 eta: 6:32:40 time: 0.4196 data_time: 0.2391 memory: 6717 grad_norm: 3.2037 loss: 1.0949 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0949 2023/04/14 09:52:09 - mmengine - INFO - Epoch(train) [67][ 240/1879] lr: 2.0000e-03 eta: 6:32:32 time: 0.3235 data_time: 0.1640 memory: 6717 grad_norm: 3.2358 loss: 1.1764 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1764 2023/04/14 09:52:18 - mmengine - INFO - Epoch(train) [67][ 260/1879] lr: 2.0000e-03 eta: 6:32:26 time: 0.4389 data_time: 0.1979 memory: 6717 grad_norm: 3.3175 loss: 1.3341 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3341 2023/04/14 09:52:24 - mmengine - INFO - Epoch(train) [67][ 280/1879] lr: 2.0000e-03 eta: 6:32:17 time: 0.3021 data_time: 0.0993 memory: 6717 grad_norm: 3.2036 loss: 1.0350 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0350 2023/04/14 09:52:32 - mmengine - INFO - Epoch(train) [67][ 300/1879] lr: 2.0000e-03 eta: 6:32:10 time: 0.4043 data_time: 0.1289 memory: 6717 grad_norm: 3.2763 loss: 1.1790 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1790 2023/04/14 09:52:38 - mmengine - INFO - Epoch(train) [67][ 320/1879] lr: 2.0000e-03 eta: 6:32:02 time: 0.3140 data_time: 0.0139 memory: 6717 grad_norm: 3.2565 loss: 1.1786 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1786 2023/04/14 09:52:46 - mmengine - INFO - Epoch(train) [67][ 340/1879] lr: 2.0000e-03 eta: 6:31:55 time: 0.3980 data_time: 0.0521 memory: 6717 grad_norm: 3.2457 loss: 1.1580 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1580 2023/04/14 09:52:52 - mmengine - INFO - Epoch(train) [67][ 360/1879] lr: 2.0000e-03 eta: 6:31:47 time: 0.3166 data_time: 0.0709 memory: 6717 grad_norm: 3.3010 loss: 1.2108 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2108 2023/04/14 09:53:01 - mmengine - INFO - Epoch(train) [67][ 380/1879] lr: 2.0000e-03 eta: 6:31:41 time: 0.4510 data_time: 0.2161 memory: 6717 grad_norm: 3.1524 loss: 1.1158 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1158 2023/04/14 09:53:07 - mmengine - INFO - Epoch(train) [67][ 400/1879] lr: 2.0000e-03 eta: 6:31:33 time: 0.2913 data_time: 0.0538 memory: 6717 grad_norm: 3.2800 loss: 1.2375 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2375 2023/04/14 09:53:16 - mmengine - INFO - Epoch(train) [67][ 420/1879] lr: 2.0000e-03 eta: 6:31:26 time: 0.4214 data_time: 0.0556 memory: 6717 grad_norm: 3.1542 loss: 1.0478 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0478 2023/04/14 09:53:21 - mmengine - INFO - Epoch(train) [67][ 440/1879] lr: 2.0000e-03 eta: 6:31:17 time: 0.2912 data_time: 0.0128 memory: 6717 grad_norm: 3.2551 loss: 1.3153 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3153 2023/04/14 09:53:30 - mmengine - INFO - Epoch(train) [67][ 460/1879] lr: 2.0000e-03 eta: 6:31:11 time: 0.4175 data_time: 0.0763 memory: 6717 grad_norm: 3.2330 loss: 1.1812 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1812 2023/04/14 09:53:36 - mmengine - INFO - Epoch(train) [67][ 480/1879] lr: 2.0000e-03 eta: 6:31:03 time: 0.3286 data_time: 0.0604 memory: 6717 grad_norm: 3.2027 loss: 1.2845 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2845 2023/04/14 09:53:44 - mmengine - INFO - Epoch(train) [67][ 500/1879] lr: 2.0000e-03 eta: 6:30:56 time: 0.4027 data_time: 0.0180 memory: 6717 grad_norm: 3.2387 loss: 1.1110 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1110 2023/04/14 09:53:51 - mmengine - INFO - Epoch(train) [67][ 520/1879] lr: 2.0000e-03 eta: 6:30:48 time: 0.3285 data_time: 0.0416 memory: 6717 grad_norm: 3.2587 loss: 1.0421 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0421 2023/04/14 09:53:59 - mmengine - INFO - Epoch(train) [67][ 540/1879] lr: 2.0000e-03 eta: 6:30:41 time: 0.3927 data_time: 0.0682 memory: 6717 grad_norm: 3.2766 loss: 1.1748 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1748 2023/04/14 09:54:06 - mmengine - INFO - Epoch(train) [67][ 560/1879] lr: 2.0000e-03 eta: 6:30:33 time: 0.3435 data_time: 0.0324 memory: 6717 grad_norm: 3.1530 loss: 1.2407 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2407 2023/04/14 09:54:14 - mmengine - INFO - Epoch(train) [67][ 580/1879] lr: 2.0000e-03 eta: 6:30:26 time: 0.4074 data_time: 0.0661 memory: 6717 grad_norm: 3.1861 loss: 1.0078 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0078 2023/04/14 09:54:21 - mmengine - INFO - Epoch(train) [67][ 600/1879] lr: 2.0000e-03 eta: 6:30:18 time: 0.3377 data_time: 0.0827 memory: 6717 grad_norm: 3.2344 loss: 1.3261 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3261 2023/04/14 09:54:29 - mmengine - INFO - Epoch(train) [67][ 620/1879] lr: 2.0000e-03 eta: 6:30:11 time: 0.4045 data_time: 0.1281 memory: 6717 grad_norm: 3.2337 loss: 1.2689 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2689 2023/04/14 09:54:36 - mmengine - INFO - Epoch(train) [67][ 640/1879] lr: 2.0000e-03 eta: 6:30:04 time: 0.3750 data_time: 0.0521 memory: 6717 grad_norm: 3.2707 loss: 1.3068 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3068 2023/04/14 09:54:43 - mmengine - INFO - Epoch(train) [67][ 660/1879] lr: 2.0000e-03 eta: 6:29:56 time: 0.3349 data_time: 0.0421 memory: 6717 grad_norm: 3.2731 loss: 1.3311 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3311 2023/04/14 09:54:51 - mmengine - INFO - Epoch(train) [67][ 680/1879] lr: 2.0000e-03 eta: 6:29:49 time: 0.3976 data_time: 0.0630 memory: 6717 grad_norm: 3.2248 loss: 1.0341 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0341 2023/04/14 09:54:58 - mmengine - INFO - Epoch(train) [67][ 700/1879] lr: 2.0000e-03 eta: 6:29:41 time: 0.3417 data_time: 0.0353 memory: 6717 grad_norm: 3.2426 loss: 1.2246 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2246 2023/04/14 09:55:06 - mmengine - INFO - Epoch(train) [67][ 720/1879] lr: 2.0000e-03 eta: 6:29:34 time: 0.4204 data_time: 0.0129 memory: 6717 grad_norm: 3.2150 loss: 1.0771 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0771 2023/04/14 09:55:12 - mmengine - INFO - Epoch(train) [67][ 740/1879] lr: 2.0000e-03 eta: 6:29:26 time: 0.2906 data_time: 0.0156 memory: 6717 grad_norm: 3.2541 loss: 1.3375 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3375 2023/04/14 09:55:20 - mmengine - INFO - Epoch(train) [67][ 760/1879] lr: 2.0000e-03 eta: 6:29:19 time: 0.3889 data_time: 0.0528 memory: 6717 grad_norm: 3.1853 loss: 1.2564 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2564 2023/04/14 09:55:27 - mmengine - INFO - Epoch(train) [67][ 780/1879] lr: 2.0000e-03 eta: 6:29:12 time: 0.3788 data_time: 0.0386 memory: 6717 grad_norm: 3.2379 loss: 1.2311 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2311 2023/04/14 09:55:36 - mmengine - INFO - Epoch(train) [67][ 800/1879] lr: 2.0000e-03 eta: 6:29:05 time: 0.4460 data_time: 0.0122 memory: 6717 grad_norm: 3.2653 loss: 1.3043 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3043 2023/04/14 09:55:43 - mmengine - INFO - Epoch(train) [67][ 820/1879] lr: 2.0000e-03 eta: 6:28:57 time: 0.3294 data_time: 0.0163 memory: 6717 grad_norm: 3.3173 loss: 1.1633 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1633 2023/04/14 09:55:51 - mmengine - INFO - Epoch(train) [67][ 840/1879] lr: 2.0000e-03 eta: 6:28:50 time: 0.3944 data_time: 0.0126 memory: 6717 grad_norm: 3.2360 loss: 1.2123 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2123 2023/04/14 09:55:57 - mmengine - INFO - Epoch(train) [67][ 860/1879] lr: 2.0000e-03 eta: 6:28:42 time: 0.3058 data_time: 0.0157 memory: 6717 grad_norm: 3.3128 loss: 1.2379 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2379 2023/04/14 09:56:04 - mmengine - INFO - Epoch(train) [67][ 880/1879] lr: 2.0000e-03 eta: 6:28:35 time: 0.3809 data_time: 0.0769 memory: 6717 grad_norm: 3.1647 loss: 1.2827 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.2827 2023/04/14 09:56:12 - mmengine - INFO - Epoch(train) [67][ 900/1879] lr: 2.0000e-03 eta: 6:28:27 time: 0.3663 data_time: 0.1202 memory: 6717 grad_norm: 3.1559 loss: 1.0230 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0230 2023/04/14 09:56:19 - mmengine - INFO - Epoch(train) [67][ 920/1879] lr: 2.0000e-03 eta: 6:28:20 time: 0.3410 data_time: 0.0869 memory: 6717 grad_norm: 3.2106 loss: 1.1058 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1058 2023/04/14 09:56:26 - mmengine - INFO - Epoch(train) [67][ 940/1879] lr: 2.0000e-03 eta: 6:28:12 time: 0.3898 data_time: 0.1079 memory: 6717 grad_norm: 3.3224 loss: 1.2010 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2010 2023/04/14 09:56:34 - mmengine - INFO - Epoch(train) [67][ 960/1879] lr: 2.0000e-03 eta: 6:28:05 time: 0.3622 data_time: 0.0821 memory: 6717 grad_norm: 3.1364 loss: 1.2628 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2628 2023/04/14 09:56:41 - mmengine - INFO - Epoch(train) [67][ 980/1879] lr: 2.0000e-03 eta: 6:27:58 time: 0.3773 data_time: 0.0949 memory: 6717 grad_norm: 3.2280 loss: 1.2381 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2381 2023/04/14 09:56:44 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 09:56:48 - mmengine - INFO - Epoch(train) [67][1000/1879] lr: 2.0000e-03 eta: 6:27:50 time: 0.3632 data_time: 0.0563 memory: 6717 grad_norm: 3.2366 loss: 1.0748 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0748 2023/04/14 09:56:56 - mmengine - INFO - Epoch(train) [67][1020/1879] lr: 2.0000e-03 eta: 6:27:43 time: 0.3586 data_time: 0.0237 memory: 6717 grad_norm: 3.1857 loss: 1.0851 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0851 2023/04/14 09:57:03 - mmengine - INFO - Epoch(train) [67][1040/1879] lr: 2.0000e-03 eta: 6:27:35 time: 0.3686 data_time: 0.0129 memory: 6717 grad_norm: 3.2773 loss: 1.1622 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1622 2023/04/14 09:57:11 - mmengine - INFO - Epoch(train) [67][1060/1879] lr: 2.0000e-03 eta: 6:27:28 time: 0.3798 data_time: 0.0168 memory: 6717 grad_norm: 3.3408 loss: 1.2814 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2814 2023/04/14 09:57:17 - mmengine - INFO - Epoch(train) [67][1080/1879] lr: 2.0000e-03 eta: 6:27:20 time: 0.3400 data_time: 0.0132 memory: 6717 grad_norm: 3.2194 loss: 1.2946 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2946 2023/04/14 09:57:25 - mmengine - INFO - Epoch(train) [67][1100/1879] lr: 2.0000e-03 eta: 6:27:13 time: 0.3760 data_time: 0.0206 memory: 6717 grad_norm: 3.2195 loss: 1.0608 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0608 2023/04/14 09:57:33 - mmengine - INFO - Epoch(train) [67][1120/1879] lr: 2.0000e-03 eta: 6:27:06 time: 0.3864 data_time: 0.0129 memory: 6717 grad_norm: 3.1579 loss: 1.2109 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2109 2023/04/14 09:57:40 - mmengine - INFO - Epoch(train) [67][1140/1879] lr: 2.0000e-03 eta: 6:26:58 time: 0.3505 data_time: 0.0172 memory: 6717 grad_norm: 3.2619 loss: 1.2543 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2543 2023/04/14 09:57:47 - mmengine - INFO - Epoch(train) [67][1160/1879] lr: 2.0000e-03 eta: 6:26:51 time: 0.3878 data_time: 0.0132 memory: 6717 grad_norm: 3.2419 loss: 1.2473 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2473 2023/04/14 09:57:54 - mmengine - INFO - Epoch(train) [67][1180/1879] lr: 2.0000e-03 eta: 6:26:43 time: 0.3413 data_time: 0.0162 memory: 6717 grad_norm: 3.2906 loss: 1.1771 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1771 2023/04/14 09:58:02 - mmengine - INFO - Epoch(train) [67][1200/1879] lr: 2.0000e-03 eta: 6:26:36 time: 0.3648 data_time: 0.0127 memory: 6717 grad_norm: 3.1979 loss: 1.1201 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1201 2023/04/14 09:58:08 - mmengine - INFO - Epoch(train) [67][1220/1879] lr: 2.0000e-03 eta: 6:26:28 time: 0.3352 data_time: 0.0164 memory: 6717 grad_norm: 3.2144 loss: 1.2409 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2409 2023/04/14 09:58:16 - mmengine - INFO - Epoch(train) [67][1240/1879] lr: 2.0000e-03 eta: 6:26:21 time: 0.3973 data_time: 0.0127 memory: 6717 grad_norm: 3.0838 loss: 1.2155 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2155 2023/04/14 09:58:23 - mmengine - INFO - Epoch(train) [67][1260/1879] lr: 2.0000e-03 eta: 6:26:13 time: 0.3476 data_time: 0.0145 memory: 6717 grad_norm: 3.2836 loss: 1.2487 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2487 2023/04/14 09:58:31 - mmengine - INFO - Epoch(train) [67][1280/1879] lr: 2.0000e-03 eta: 6:26:06 time: 0.3793 data_time: 0.0140 memory: 6717 grad_norm: 3.1958 loss: 1.3516 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3516 2023/04/14 09:58:37 - mmengine - INFO - Epoch(train) [67][1300/1879] lr: 2.0000e-03 eta: 6:25:58 time: 0.3365 data_time: 0.0157 memory: 6717 grad_norm: 3.1487 loss: 1.0604 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0604 2023/04/14 09:58:45 - mmengine - INFO - Epoch(train) [67][1320/1879] lr: 2.0000e-03 eta: 6:25:51 time: 0.3593 data_time: 0.0138 memory: 6717 grad_norm: 3.2117 loss: 1.3091 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3091 2023/04/14 09:58:52 - mmengine - INFO - Epoch(train) [67][1340/1879] lr: 2.0000e-03 eta: 6:25:43 time: 0.3887 data_time: 0.0162 memory: 6717 grad_norm: 3.2925 loss: 1.2737 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2737 2023/04/14 09:59:00 - mmengine - INFO - Epoch(train) [67][1360/1879] lr: 2.0000e-03 eta: 6:25:36 time: 0.3577 data_time: 0.0127 memory: 6717 grad_norm: 3.2716 loss: 1.3947 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3947 2023/04/14 09:59:07 - mmengine - INFO - Epoch(train) [67][1380/1879] lr: 2.0000e-03 eta: 6:25:28 time: 0.3591 data_time: 0.0156 memory: 6717 grad_norm: 3.1765 loss: 1.2528 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2528 2023/04/14 09:59:15 - mmengine - INFO - Epoch(train) [67][1400/1879] lr: 2.0000e-03 eta: 6:25:21 time: 0.4079 data_time: 0.0141 memory: 6717 grad_norm: 3.1743 loss: 1.0645 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0645 2023/04/14 09:59:21 - mmengine - INFO - Epoch(train) [67][1420/1879] lr: 2.0000e-03 eta: 6:25:13 time: 0.3183 data_time: 0.0159 memory: 6717 grad_norm: 3.2582 loss: 1.2460 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2460 2023/04/14 09:59:30 - mmengine - INFO - Epoch(train) [67][1440/1879] lr: 2.0000e-03 eta: 6:25:07 time: 0.4386 data_time: 0.0159 memory: 6717 grad_norm: 3.2475 loss: 1.2074 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2074 2023/04/14 09:59:37 - mmengine - INFO - Epoch(train) [67][1460/1879] lr: 2.0000e-03 eta: 6:24:59 time: 0.3322 data_time: 0.0130 memory: 6717 grad_norm: 3.2255 loss: 1.2572 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2572 2023/04/14 09:59:44 - mmengine - INFO - Epoch(train) [67][1480/1879] lr: 2.0000e-03 eta: 6:24:52 time: 0.3805 data_time: 0.0134 memory: 6717 grad_norm: 3.1698 loss: 1.2910 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2910 2023/04/14 09:59:52 - mmengine - INFO - Epoch(train) [67][1500/1879] lr: 2.0000e-03 eta: 6:24:45 time: 0.4063 data_time: 0.0154 memory: 6717 grad_norm: 3.1608 loss: 1.1388 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1388 2023/04/14 10:00:00 - mmengine - INFO - Epoch(train) [67][1520/1879] lr: 2.0000e-03 eta: 6:24:37 time: 0.3534 data_time: 0.0127 memory: 6717 grad_norm: 3.2824 loss: 1.3383 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3383 2023/04/14 10:00:08 - mmengine - INFO - Epoch(train) [67][1540/1879] lr: 2.0000e-03 eta: 6:24:30 time: 0.4047 data_time: 0.0140 memory: 6717 grad_norm: 3.2250 loss: 1.2242 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2242 2023/04/14 10:00:15 - mmengine - INFO - Epoch(train) [67][1560/1879] lr: 2.0000e-03 eta: 6:24:22 time: 0.3459 data_time: 0.0160 memory: 6717 grad_norm: 3.2064 loss: 1.2853 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2853 2023/04/14 10:00:22 - mmengine - INFO - Epoch(train) [67][1580/1879] lr: 2.0000e-03 eta: 6:24:15 time: 0.3931 data_time: 0.0167 memory: 6717 grad_norm: 3.1907 loss: 1.1110 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1110 2023/04/14 10:00:30 - mmengine - INFO - Epoch(train) [67][1600/1879] lr: 2.0000e-03 eta: 6:24:08 time: 0.3668 data_time: 0.0147 memory: 6717 grad_norm: 3.1994 loss: 1.2937 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2937 2023/04/14 10:00:37 - mmengine - INFO - Epoch(train) [67][1620/1879] lr: 2.0000e-03 eta: 6:24:00 time: 0.3369 data_time: 0.0175 memory: 6717 grad_norm: 3.2463 loss: 1.3014 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3014 2023/04/14 10:00:45 - mmengine - INFO - Epoch(train) [67][1640/1879] lr: 2.0000e-03 eta: 6:23:53 time: 0.4222 data_time: 0.0136 memory: 6717 grad_norm: 3.3753 loss: 1.1495 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1495 2023/04/14 10:00:51 - mmengine - INFO - Epoch(train) [67][1660/1879] lr: 2.0000e-03 eta: 6:23:45 time: 0.3195 data_time: 0.0173 memory: 6717 grad_norm: 3.2585 loss: 1.2625 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2625 2023/04/14 10:01:00 - mmengine - INFO - Epoch(train) [67][1680/1879] lr: 2.0000e-03 eta: 6:23:38 time: 0.4127 data_time: 0.0133 memory: 6717 grad_norm: 3.3076 loss: 1.2683 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2683 2023/04/14 10:01:06 - mmengine - INFO - Epoch(train) [67][1700/1879] lr: 2.0000e-03 eta: 6:23:30 time: 0.3296 data_time: 0.0138 memory: 6717 grad_norm: 3.2866 loss: 1.2628 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2628 2023/04/14 10:01:14 - mmengine - INFO - Epoch(train) [67][1720/1879] lr: 2.0000e-03 eta: 6:23:23 time: 0.4116 data_time: 0.0140 memory: 6717 grad_norm: 3.3170 loss: 1.2180 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.2180 2023/04/14 10:01:21 - mmengine - INFO - Epoch(train) [67][1740/1879] lr: 2.0000e-03 eta: 6:23:15 time: 0.3043 data_time: 0.0139 memory: 6717 grad_norm: 3.1490 loss: 1.1354 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1354 2023/04/14 10:01:29 - mmengine - INFO - Epoch(train) [67][1760/1879] lr: 2.0000e-03 eta: 6:23:08 time: 0.4009 data_time: 0.0149 memory: 6717 grad_norm: 3.2432 loss: 1.4135 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4135 2023/04/14 10:01:36 - mmengine - INFO - Epoch(train) [67][1780/1879] lr: 2.0000e-03 eta: 6:23:01 time: 0.3526 data_time: 0.0147 memory: 6717 grad_norm: 3.1575 loss: 1.2005 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2005 2023/04/14 10:01:44 - mmengine - INFO - Epoch(train) [67][1800/1879] lr: 2.0000e-03 eta: 6:22:54 time: 0.3988 data_time: 0.0154 memory: 6717 grad_norm: 3.1276 loss: 1.1347 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1347 2023/04/14 10:01:50 - mmengine - INFO - Epoch(train) [67][1820/1879] lr: 2.0000e-03 eta: 6:22:45 time: 0.2971 data_time: 0.1030 memory: 6717 grad_norm: 3.2846 loss: 1.3303 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.3303 2023/04/14 10:01:58 - mmengine - INFO - Epoch(train) [67][1840/1879] lr: 2.0000e-03 eta: 6:22:39 time: 0.4287 data_time: 0.2573 memory: 6717 grad_norm: 3.3212 loss: 1.0997 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0997 2023/04/14 10:02:05 - mmengine - INFO - Epoch(train) [67][1860/1879] lr: 2.0000e-03 eta: 6:22:31 time: 0.3521 data_time: 0.1229 memory: 6717 grad_norm: 3.2548 loss: 1.2242 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2242 2023/04/14 10:02:11 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 10:02:11 - mmengine - INFO - Epoch(train) [67][1879/1879] lr: 2.0000e-03 eta: 6:22:24 time: 0.3191 data_time: 0.0829 memory: 6717 grad_norm: 3.2743 loss: 1.1297 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1297 2023/04/14 10:02:21 - mmengine - INFO - Epoch(val) [67][ 20/155] eta: 0:01:01 time: 0.4562 data_time: 0.4228 memory: 1391 2023/04/14 10:02:27 - mmengine - INFO - Epoch(val) [67][ 40/155] eta: 0:00:44 time: 0.3212 data_time: 0.2877 memory: 1391 2023/04/14 10:02:36 - mmengine - INFO - Epoch(val) [67][ 60/155] eta: 0:00:38 time: 0.4283 data_time: 0.3947 memory: 1391 2023/04/14 10:02:42 - mmengine - INFO - Epoch(val) [67][ 80/155] eta: 0:00:28 time: 0.3148 data_time: 0.2815 memory: 1391 2023/04/14 10:02:51 - mmengine - INFO - Epoch(val) [67][100/155] eta: 0:00:21 time: 0.4525 data_time: 0.4193 memory: 1391 2023/04/14 10:02:57 - mmengine - INFO - Epoch(val) [67][120/155] eta: 0:00:13 time: 0.2951 data_time: 0.2619 memory: 1391 2023/04/14 10:03:06 - mmengine - INFO - Epoch(val) [67][140/155] eta: 0:00:05 time: 0.4404 data_time: 0.4071 memory: 1391 2023/04/14 10:03:13 - mmengine - INFO - Epoch(val) [67][155/155] acc/top1: 0.6630 acc/top5: 0.8724 acc/mean1: 0.6630 data_time: 0.3746 time: 0.4074 2023/04/14 10:03:23 - mmengine - INFO - Epoch(train) [68][ 20/1879] lr: 2.0000e-03 eta: 6:22:17 time: 0.4784 data_time: 0.1808 memory: 6717 grad_norm: 3.1948 loss: 0.9048 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9048 2023/04/14 10:03:30 - mmengine - INFO - Epoch(train) [68][ 40/1879] lr: 2.0000e-03 eta: 6:22:10 time: 0.3320 data_time: 0.0209 memory: 6717 grad_norm: 3.2848 loss: 1.2535 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2535 2023/04/14 10:03:38 - mmengine - INFO - Epoch(train) [68][ 60/1879] lr: 2.0000e-03 eta: 6:22:02 time: 0.4054 data_time: 0.0157 memory: 6717 grad_norm: 3.2690 loss: 1.0804 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0804 2023/04/14 10:03:45 - mmengine - INFO - Epoch(train) [68][ 80/1879] lr: 2.0000e-03 eta: 6:21:55 time: 0.3524 data_time: 0.0136 memory: 6717 grad_norm: 3.2478 loss: 1.3572 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3572 2023/04/14 10:03:53 - mmengine - INFO - Epoch(train) [68][ 100/1879] lr: 2.0000e-03 eta: 6:21:48 time: 0.3905 data_time: 0.0681 memory: 6717 grad_norm: 3.2000 loss: 1.1721 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1721 2023/04/14 10:03:56 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 10:04:00 - mmengine - INFO - Epoch(train) [68][ 120/1879] lr: 2.0000e-03 eta: 6:21:40 time: 0.3711 data_time: 0.1511 memory: 6717 grad_norm: 3.1760 loss: 1.1857 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1857 2023/04/14 10:04:07 - mmengine - INFO - Epoch(train) [68][ 140/1879] lr: 2.0000e-03 eta: 6:21:33 time: 0.3707 data_time: 0.1180 memory: 6717 grad_norm: 3.1474 loss: 1.0301 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0301 2023/04/14 10:04:15 - mmengine - INFO - Epoch(train) [68][ 160/1879] lr: 2.0000e-03 eta: 6:21:26 time: 0.3827 data_time: 0.2291 memory: 6717 grad_norm: 3.1944 loss: 1.2081 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2081 2023/04/14 10:04:23 - mmengine - INFO - Epoch(train) [68][ 180/1879] lr: 2.0000e-03 eta: 6:21:18 time: 0.3766 data_time: 0.1196 memory: 6717 grad_norm: 3.1861 loss: 1.0041 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0041 2023/04/14 10:04:29 - mmengine - INFO - Epoch(train) [68][ 200/1879] lr: 2.0000e-03 eta: 6:21:11 time: 0.3454 data_time: 0.0784 memory: 6717 grad_norm: 3.2185 loss: 1.0134 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0134 2023/04/14 10:04:38 - mmengine - INFO - Epoch(train) [68][ 220/1879] lr: 2.0000e-03 eta: 6:21:04 time: 0.4420 data_time: 0.0484 memory: 6717 grad_norm: 3.2458 loss: 1.2380 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2380 2023/04/14 10:04:45 - mmengine - INFO - Epoch(train) [68][ 240/1879] lr: 2.0000e-03 eta: 6:20:56 time: 0.3260 data_time: 0.0124 memory: 6717 grad_norm: 3.2740 loss: 1.2142 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2142 2023/04/14 10:04:53 - mmengine - INFO - Epoch(train) [68][ 260/1879] lr: 2.0000e-03 eta: 6:20:49 time: 0.4079 data_time: 0.0144 memory: 6717 grad_norm: 3.2448 loss: 1.1784 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1784 2023/04/14 10:04:59 - mmengine - INFO - Epoch(train) [68][ 280/1879] lr: 2.0000e-03 eta: 6:20:41 time: 0.3041 data_time: 0.0138 memory: 6717 grad_norm: 3.1944 loss: 1.2372 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2372 2023/04/14 10:05:08 - mmengine - INFO - Epoch(train) [68][ 300/1879] lr: 2.0000e-03 eta: 6:20:34 time: 0.4439 data_time: 0.0144 memory: 6717 grad_norm: 3.1713 loss: 1.0572 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0572 2023/04/14 10:05:14 - mmengine - INFO - Epoch(train) [68][ 320/1879] lr: 2.0000e-03 eta: 6:20:26 time: 0.3171 data_time: 0.0142 memory: 6717 grad_norm: 3.2803 loss: 1.1614 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1614 2023/04/14 10:05:23 - mmengine - INFO - Epoch(train) [68][ 340/1879] lr: 2.0000e-03 eta: 6:20:19 time: 0.4112 data_time: 0.0152 memory: 6717 grad_norm: 3.3130 loss: 1.1430 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1430 2023/04/14 10:05:29 - mmengine - INFO - Epoch(train) [68][ 360/1879] lr: 2.0000e-03 eta: 6:20:11 time: 0.3013 data_time: 0.0169 memory: 6717 grad_norm: 3.1996 loss: 1.3056 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3056 2023/04/14 10:05:36 - mmengine - INFO - Epoch(train) [68][ 380/1879] lr: 2.0000e-03 eta: 6:20:04 time: 0.3778 data_time: 0.0318 memory: 6717 grad_norm: 3.2788 loss: 1.2802 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2802 2023/04/14 10:05:43 - mmengine - INFO - Epoch(train) [68][ 400/1879] lr: 2.0000e-03 eta: 6:19:57 time: 0.3569 data_time: 0.0357 memory: 6717 grad_norm: 3.2470 loss: 1.2259 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2259 2023/04/14 10:05:51 - mmengine - INFO - Epoch(train) [68][ 420/1879] lr: 2.0000e-03 eta: 6:19:49 time: 0.3743 data_time: 0.0126 memory: 6717 grad_norm: 3.2090 loss: 1.1085 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1085 2023/04/14 10:05:58 - mmengine - INFO - Epoch(train) [68][ 440/1879] lr: 2.0000e-03 eta: 6:19:42 time: 0.3775 data_time: 0.0162 memory: 6717 grad_norm: 3.1858 loss: 1.3057 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3057 2023/04/14 10:06:06 - mmengine - INFO - Epoch(train) [68][ 460/1879] lr: 2.0000e-03 eta: 6:19:34 time: 0.3642 data_time: 0.0131 memory: 6717 grad_norm: 3.2455 loss: 1.3630 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3630 2023/04/14 10:06:13 - mmengine - INFO - Epoch(train) [68][ 480/1879] lr: 2.0000e-03 eta: 6:19:27 time: 0.3602 data_time: 0.0164 memory: 6717 grad_norm: 3.1681 loss: 1.2352 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2352 2023/04/14 10:06:20 - mmengine - INFO - Epoch(train) [68][ 500/1879] lr: 2.0000e-03 eta: 6:19:20 time: 0.3752 data_time: 0.0138 memory: 6717 grad_norm: 3.2685 loss: 1.2866 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2866 2023/04/14 10:06:27 - mmengine - INFO - Epoch(train) [68][ 520/1879] lr: 2.0000e-03 eta: 6:19:12 time: 0.3490 data_time: 0.0166 memory: 6717 grad_norm: 3.1784 loss: 1.1319 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1319 2023/04/14 10:06:36 - mmengine - INFO - Epoch(train) [68][ 540/1879] lr: 2.0000e-03 eta: 6:19:05 time: 0.4322 data_time: 0.0121 memory: 6717 grad_norm: 3.3370 loss: 1.2827 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2827 2023/04/14 10:06:42 - mmengine - INFO - Epoch(train) [68][ 560/1879] lr: 2.0000e-03 eta: 6:18:57 time: 0.3083 data_time: 0.0221 memory: 6717 grad_norm: 3.2698 loss: 1.2004 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2004 2023/04/14 10:06:50 - mmengine - INFO - Epoch(train) [68][ 580/1879] lr: 2.0000e-03 eta: 6:18:50 time: 0.3875 data_time: 0.0267 memory: 6717 grad_norm: 3.2609 loss: 1.1704 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1704 2023/04/14 10:06:57 - mmengine - INFO - Epoch(train) [68][ 600/1879] lr: 2.0000e-03 eta: 6:18:42 time: 0.3388 data_time: 0.0523 memory: 6717 grad_norm: 3.1504 loss: 1.0749 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0749 2023/04/14 10:07:05 - mmengine - INFO - Epoch(train) [68][ 620/1879] lr: 2.0000e-03 eta: 6:18:35 time: 0.4060 data_time: 0.0575 memory: 6717 grad_norm: 3.3478 loss: 1.1025 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1025 2023/04/14 10:07:12 - mmengine - INFO - Epoch(train) [68][ 640/1879] lr: 2.0000e-03 eta: 6:18:28 time: 0.3502 data_time: 0.0208 memory: 6717 grad_norm: 3.2545 loss: 1.2497 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2497 2023/04/14 10:07:20 - mmengine - INFO - Epoch(train) [68][ 660/1879] lr: 2.0000e-03 eta: 6:18:20 time: 0.3885 data_time: 0.0133 memory: 6717 grad_norm: 3.2976 loss: 1.3921 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3921 2023/04/14 10:07:26 - mmengine - INFO - Epoch(train) [68][ 680/1879] lr: 2.0000e-03 eta: 6:18:13 time: 0.3329 data_time: 0.0170 memory: 6717 grad_norm: 3.3034 loss: 1.1925 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1925 2023/04/14 10:07:34 - mmengine - INFO - Epoch(train) [68][ 700/1879] lr: 2.0000e-03 eta: 6:18:05 time: 0.3889 data_time: 0.0126 memory: 6717 grad_norm: 3.2225 loss: 1.2633 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2633 2023/04/14 10:07:41 - mmengine - INFO - Epoch(train) [68][ 720/1879] lr: 2.0000e-03 eta: 6:17:58 time: 0.3655 data_time: 0.1723 memory: 6717 grad_norm: 3.2453 loss: 1.2499 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2499 2023/04/14 10:07:49 - mmengine - INFO - Epoch(train) [68][ 740/1879] lr: 2.0000e-03 eta: 6:17:51 time: 0.3702 data_time: 0.1529 memory: 6717 grad_norm: 3.1708 loss: 1.1550 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1550 2023/04/14 10:07:56 - mmengine - INFO - Epoch(train) [68][ 760/1879] lr: 2.0000e-03 eta: 6:17:43 time: 0.3483 data_time: 0.0544 memory: 6717 grad_norm: 3.1668 loss: 1.4338 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4338 2023/04/14 10:08:03 - mmengine - INFO - Epoch(train) [68][ 780/1879] lr: 2.0000e-03 eta: 6:17:36 time: 0.3872 data_time: 0.0310 memory: 6717 grad_norm: 3.1700 loss: 1.2484 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2484 2023/04/14 10:08:10 - mmengine - INFO - Epoch(train) [68][ 800/1879] lr: 2.0000e-03 eta: 6:17:28 time: 0.3363 data_time: 0.0547 memory: 6717 grad_norm: 3.1607 loss: 1.1955 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1955 2023/04/14 10:08:17 - mmengine - INFO - Epoch(train) [68][ 820/1879] lr: 2.0000e-03 eta: 6:17:20 time: 0.3526 data_time: 0.1008 memory: 6717 grad_norm: 3.2777 loss: 1.1641 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1641 2023/04/14 10:08:26 - mmengine - INFO - Epoch(train) [68][ 840/1879] lr: 2.0000e-03 eta: 6:17:13 time: 0.4180 data_time: 0.0276 memory: 6717 grad_norm: 3.1808 loss: 1.1635 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1635 2023/04/14 10:08:32 - mmengine - INFO - Epoch(train) [68][ 860/1879] lr: 2.0000e-03 eta: 6:17:06 time: 0.3305 data_time: 0.0129 memory: 6717 grad_norm: 3.2559 loss: 1.1278 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1278 2023/04/14 10:08:41 - mmengine - INFO - Epoch(train) [68][ 880/1879] lr: 2.0000e-03 eta: 6:16:59 time: 0.4329 data_time: 0.0165 memory: 6717 grad_norm: 3.3106 loss: 1.3175 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3175 2023/04/14 10:08:48 - mmengine - INFO - Epoch(train) [68][ 900/1879] lr: 2.0000e-03 eta: 6:16:51 time: 0.3343 data_time: 0.0138 memory: 6717 grad_norm: 3.2583 loss: 1.2426 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2426 2023/04/14 10:08:56 - mmengine - INFO - Epoch(train) [68][ 920/1879] lr: 2.0000e-03 eta: 6:16:44 time: 0.3989 data_time: 0.0140 memory: 6717 grad_norm: 3.2283 loss: 1.2455 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2455 2023/04/14 10:09:02 - mmengine - INFO - Epoch(train) [68][ 940/1879] lr: 2.0000e-03 eta: 6:16:36 time: 0.3251 data_time: 0.0143 memory: 6717 grad_norm: 3.2542 loss: 1.2159 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2159 2023/04/14 10:09:10 - mmengine - INFO - Epoch(train) [68][ 960/1879] lr: 2.0000e-03 eta: 6:16:29 time: 0.3753 data_time: 0.0151 memory: 6717 grad_norm: 3.2643 loss: 1.2622 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2622 2023/04/14 10:09:16 - mmengine - INFO - Epoch(train) [68][ 980/1879] lr: 2.0000e-03 eta: 6:16:21 time: 0.3220 data_time: 0.0157 memory: 6717 grad_norm: 3.3131 loss: 1.1634 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1634 2023/04/14 10:09:25 - mmengine - INFO - Epoch(train) [68][1000/1879] lr: 2.0000e-03 eta: 6:16:15 time: 0.4703 data_time: 0.0136 memory: 6717 grad_norm: 3.2867 loss: 1.2096 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2096 2023/04/14 10:09:32 - mmengine - INFO - Epoch(train) [68][1020/1879] lr: 2.0000e-03 eta: 6:16:07 time: 0.3235 data_time: 0.0156 memory: 6717 grad_norm: 3.2963 loss: 1.3744 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3744 2023/04/14 10:09:40 - mmengine - INFO - Epoch(train) [68][1040/1879] lr: 2.0000e-03 eta: 6:15:59 time: 0.3879 data_time: 0.0153 memory: 6717 grad_norm: 3.2935 loss: 1.2677 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2677 2023/04/14 10:09:47 - mmengine - INFO - Epoch(train) [68][1060/1879] lr: 2.0000e-03 eta: 6:15:52 time: 0.3471 data_time: 0.0133 memory: 6717 grad_norm: 3.2490 loss: 1.1663 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1663 2023/04/14 10:09:55 - mmengine - INFO - Epoch(train) [68][1080/1879] lr: 2.0000e-03 eta: 6:15:45 time: 0.4308 data_time: 0.0138 memory: 6717 grad_norm: 3.1923 loss: 1.1380 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1380 2023/04/14 10:10:01 - mmengine - INFO - Epoch(train) [68][1100/1879] lr: 2.0000e-03 eta: 6:15:37 time: 0.2777 data_time: 0.0150 memory: 6717 grad_norm: 3.2582 loss: 1.1605 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1605 2023/04/14 10:10:04 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 10:10:09 - mmengine - INFO - Epoch(train) [68][1120/1879] lr: 2.0000e-03 eta: 6:15:30 time: 0.4019 data_time: 0.0135 memory: 6717 grad_norm: 3.3049 loss: 1.2720 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2720 2023/04/14 10:10:15 - mmengine - INFO - Epoch(train) [68][1140/1879] lr: 2.0000e-03 eta: 6:15:22 time: 0.3155 data_time: 0.0147 memory: 6717 grad_norm: 3.2128 loss: 1.1741 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.1741 2023/04/14 10:10:24 - mmengine - INFO - Epoch(train) [68][1160/1879] lr: 2.0000e-03 eta: 6:15:15 time: 0.4166 data_time: 0.0144 memory: 6717 grad_norm: 3.2433 loss: 1.2594 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2594 2023/04/14 10:10:30 - mmengine - INFO - Epoch(train) [68][1180/1879] lr: 2.0000e-03 eta: 6:15:07 time: 0.3462 data_time: 0.0141 memory: 6717 grad_norm: 3.3046 loss: 1.2407 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2407 2023/04/14 10:10:39 - mmengine - INFO - Epoch(train) [68][1200/1879] lr: 2.0000e-03 eta: 6:15:00 time: 0.4099 data_time: 0.0153 memory: 6717 grad_norm: 3.3258 loss: 1.2893 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2893 2023/04/14 10:10:45 - mmengine - INFO - Epoch(train) [68][1220/1879] lr: 2.0000e-03 eta: 6:14:52 time: 0.3314 data_time: 0.0140 memory: 6717 grad_norm: 3.1981 loss: 1.2345 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2345 2023/04/14 10:10:53 - mmengine - INFO - Epoch(train) [68][1240/1879] lr: 2.0000e-03 eta: 6:14:45 time: 0.3943 data_time: 0.0134 memory: 6717 grad_norm: 3.1825 loss: 0.9914 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9914 2023/04/14 10:11:00 - mmengine - INFO - Epoch(train) [68][1260/1879] lr: 2.0000e-03 eta: 6:14:38 time: 0.3530 data_time: 0.0149 memory: 6717 grad_norm: 3.3361 loss: 1.4104 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4104 2023/04/14 10:11:09 - mmengine - INFO - Epoch(train) [68][1280/1879] lr: 2.0000e-03 eta: 6:14:31 time: 0.4369 data_time: 0.0130 memory: 6717 grad_norm: 3.2154 loss: 1.2332 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2332 2023/04/14 10:11:15 - mmengine - INFO - Epoch(train) [68][1300/1879] lr: 2.0000e-03 eta: 6:14:23 time: 0.3150 data_time: 0.0147 memory: 6717 grad_norm: 3.3433 loss: 1.3214 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3214 2023/04/14 10:11:23 - mmengine - INFO - Epoch(train) [68][1320/1879] lr: 2.0000e-03 eta: 6:14:16 time: 0.4098 data_time: 0.0157 memory: 6717 grad_norm: 3.3255 loss: 1.1839 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1839 2023/04/14 10:11:30 - mmengine - INFO - Epoch(train) [68][1340/1879] lr: 2.0000e-03 eta: 6:14:08 time: 0.3128 data_time: 0.0123 memory: 6717 grad_norm: 3.1887 loss: 1.1988 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1988 2023/04/14 10:11:39 - mmengine - INFO - Epoch(train) [68][1360/1879] lr: 2.0000e-03 eta: 6:14:01 time: 0.4460 data_time: 0.0154 memory: 6717 grad_norm: 3.2974 loss: 1.2069 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2069 2023/04/14 10:11:45 - mmengine - INFO - Epoch(train) [68][1380/1879] lr: 2.0000e-03 eta: 6:13:53 time: 0.3080 data_time: 0.0140 memory: 6717 grad_norm: 3.2072 loss: 1.0697 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.0697 2023/04/14 10:11:53 - mmengine - INFO - Epoch(train) [68][1400/1879] lr: 2.0000e-03 eta: 6:13:46 time: 0.4235 data_time: 0.0151 memory: 6717 grad_norm: 3.2808 loss: 1.4391 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.4391 2023/04/14 10:11:59 - mmengine - INFO - Epoch(train) [68][1420/1879] lr: 2.0000e-03 eta: 6:13:38 time: 0.2863 data_time: 0.0146 memory: 6717 grad_norm: 3.2619 loss: 1.2876 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2876 2023/04/14 10:12:07 - mmengine - INFO - Epoch(train) [68][1440/1879] lr: 2.0000e-03 eta: 6:13:31 time: 0.4089 data_time: 0.0135 memory: 6717 grad_norm: 3.2004 loss: 1.2661 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2661 2023/04/14 10:12:13 - mmengine - INFO - Epoch(train) [68][1460/1879] lr: 2.0000e-03 eta: 6:13:23 time: 0.3099 data_time: 0.0146 memory: 6717 grad_norm: 3.2144 loss: 1.1963 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1963 2023/04/14 10:12:22 - mmengine - INFO - Epoch(train) [68][1480/1879] lr: 2.0000e-03 eta: 6:13:17 time: 0.4457 data_time: 0.0132 memory: 6717 grad_norm: 3.2246 loss: 1.1980 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1980 2023/04/14 10:12:29 - mmengine - INFO - Epoch(train) [68][1500/1879] lr: 2.0000e-03 eta: 6:13:09 time: 0.3262 data_time: 0.0148 memory: 6717 grad_norm: 3.1975 loss: 1.3179 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3179 2023/04/14 10:12:37 - mmengine - INFO - Epoch(train) [68][1520/1879] lr: 2.0000e-03 eta: 6:13:01 time: 0.3899 data_time: 0.0133 memory: 6717 grad_norm: 3.2355 loss: 1.2666 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2666 2023/04/14 10:12:43 - mmengine - INFO - Epoch(train) [68][1540/1879] lr: 2.0000e-03 eta: 6:12:54 time: 0.3314 data_time: 0.0154 memory: 6717 grad_norm: 3.1828 loss: 1.0159 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0159 2023/04/14 10:12:52 - mmengine - INFO - Epoch(train) [68][1560/1879] lr: 2.0000e-03 eta: 6:12:47 time: 0.4241 data_time: 0.0149 memory: 6717 grad_norm: 3.2945 loss: 1.2351 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.2351 2023/04/14 10:12:58 - mmengine - INFO - Epoch(train) [68][1580/1879] lr: 2.0000e-03 eta: 6:12:39 time: 0.3143 data_time: 0.0150 memory: 6717 grad_norm: 3.2549 loss: 1.3967 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3967 2023/04/14 10:13:06 - mmengine - INFO - Epoch(train) [68][1600/1879] lr: 2.0000e-03 eta: 6:12:32 time: 0.4137 data_time: 0.0136 memory: 6717 grad_norm: 3.1343 loss: 1.1638 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1638 2023/04/14 10:13:13 - mmengine - INFO - Epoch(train) [68][1620/1879] lr: 2.0000e-03 eta: 6:12:24 time: 0.3543 data_time: 0.0153 memory: 6717 grad_norm: 3.3406 loss: 1.1341 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1341 2023/04/14 10:13:22 - mmengine - INFO - Epoch(train) [68][1640/1879] lr: 2.0000e-03 eta: 6:12:18 time: 0.4332 data_time: 0.0136 memory: 6717 grad_norm: 3.3594 loss: 1.1767 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1767 2023/04/14 10:13:28 - mmengine - INFO - Epoch(train) [68][1660/1879] lr: 2.0000e-03 eta: 6:12:10 time: 0.3108 data_time: 0.0148 memory: 6717 grad_norm: 3.1582 loss: 1.0691 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0691 2023/04/14 10:13:37 - mmengine - INFO - Epoch(train) [68][1680/1879] lr: 2.0000e-03 eta: 6:12:03 time: 0.4076 data_time: 0.0131 memory: 6717 grad_norm: 3.2258 loss: 1.1788 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1788 2023/04/14 10:13:43 - mmengine - INFO - Epoch(train) [68][1700/1879] lr: 2.0000e-03 eta: 6:11:55 time: 0.3451 data_time: 0.0147 memory: 6717 grad_norm: 3.2539 loss: 1.0688 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0688 2023/04/14 10:13:52 - mmengine - INFO - Epoch(train) [68][1720/1879] lr: 2.0000e-03 eta: 6:11:48 time: 0.4132 data_time: 0.0145 memory: 6717 grad_norm: 3.1614 loss: 1.3210 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3210 2023/04/14 10:13:58 - mmengine - INFO - Epoch(train) [68][1740/1879] lr: 2.0000e-03 eta: 6:11:40 time: 0.2942 data_time: 0.0152 memory: 6717 grad_norm: 3.1994 loss: 1.0380 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0380 2023/04/14 10:14:05 - mmengine - INFO - Epoch(train) [68][1760/1879] lr: 2.0000e-03 eta: 6:11:33 time: 0.3960 data_time: 0.0142 memory: 6717 grad_norm: 3.2889 loss: 1.2733 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.2733 2023/04/14 10:14:12 - mmengine - INFO - Epoch(train) [68][1780/1879] lr: 2.0000e-03 eta: 6:11:25 time: 0.3114 data_time: 0.0278 memory: 6717 grad_norm: 3.3243 loss: 1.4501 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.4501 2023/04/14 10:14:20 - mmengine - INFO - Epoch(train) [68][1800/1879] lr: 2.0000e-03 eta: 6:11:18 time: 0.4105 data_time: 0.0169 memory: 6717 grad_norm: 3.1807 loss: 1.1692 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1692 2023/04/14 10:14:27 - mmengine - INFO - Epoch(train) [68][1820/1879] lr: 2.0000e-03 eta: 6:11:10 time: 0.3356 data_time: 0.0154 memory: 6717 grad_norm: 3.2308 loss: 1.0343 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0343 2023/04/14 10:14:35 - mmengine - INFO - Epoch(train) [68][1840/1879] lr: 2.0000e-03 eta: 6:11:03 time: 0.4378 data_time: 0.0140 memory: 6717 grad_norm: 3.2571 loss: 1.0727 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0727 2023/04/14 10:14:42 - mmengine - INFO - Epoch(train) [68][1860/1879] lr: 2.0000e-03 eta: 6:10:55 time: 0.3266 data_time: 0.0195 memory: 6717 grad_norm: 3.2510 loss: 1.1576 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1576 2023/04/14 10:14:48 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 10:14:48 - mmengine - INFO - Epoch(train) [68][1879/1879] lr: 2.0000e-03 eta: 6:10:48 time: 0.3030 data_time: 0.0129 memory: 6717 grad_norm: 3.3951 loss: 1.4723 top1_acc: 0.2857 top5_acc: 0.5714 loss_cls: 1.4723 2023/04/14 10:14:57 - mmengine - INFO - Epoch(val) [68][ 20/155] eta: 0:01:02 time: 0.4616 data_time: 0.4285 memory: 1391 2023/04/14 10:15:04 - mmengine - INFO - Epoch(val) [68][ 40/155] eta: 0:00:44 time: 0.3198 data_time: 0.2874 memory: 1391 2023/04/14 10:15:12 - mmengine - INFO - Epoch(val) [68][ 60/155] eta: 0:00:38 time: 0.4239 data_time: 0.3902 memory: 1391 2023/04/14 10:15:18 - mmengine - INFO - Epoch(val) [68][ 80/155] eta: 0:00:28 time: 0.3151 data_time: 0.2822 memory: 1391 2023/04/14 10:15:27 - mmengine - INFO - Epoch(val) [68][100/155] eta: 0:00:21 time: 0.4300 data_time: 0.3969 memory: 1391 2023/04/14 10:15:34 - mmengine - INFO - Epoch(val) [68][120/155] eta: 0:00:13 time: 0.3366 data_time: 0.3034 memory: 1391 2023/04/14 10:15:43 - mmengine - INFO - Epoch(val) [68][140/155] eta: 0:00:05 time: 0.4869 data_time: 0.4549 memory: 1391 2023/04/14 10:15:50 - mmengine - INFO - Epoch(val) [68][155/155] acc/top1: 0.6626 acc/top5: 0.8710 acc/mean1: 0.6626 data_time: 0.4202 time: 0.4520 2023/04/14 10:16:01 - mmengine - INFO - Epoch(train) [69][ 20/1879] lr: 2.0000e-03 eta: 6:10:42 time: 0.5019 data_time: 0.1804 memory: 6717 grad_norm: 3.3240 loss: 1.3628 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3628 2023/04/14 10:16:07 - mmengine - INFO - Epoch(train) [69][ 40/1879] lr: 2.0000e-03 eta: 6:10:34 time: 0.3240 data_time: 0.0138 memory: 6717 grad_norm: 3.1424 loss: 1.1177 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1177 2023/04/14 10:16:15 - mmengine - INFO - Epoch(train) [69][ 60/1879] lr: 2.0000e-03 eta: 6:10:27 time: 0.4066 data_time: 0.0153 memory: 6717 grad_norm: 3.1147 loss: 1.2101 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2101 2023/04/14 10:16:22 - mmengine - INFO - Epoch(train) [69][ 80/1879] lr: 2.0000e-03 eta: 6:10:19 time: 0.3319 data_time: 0.0132 memory: 6717 grad_norm: 3.2336 loss: 1.1000 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1000 2023/04/14 10:16:30 - mmengine - INFO - Epoch(train) [69][ 100/1879] lr: 2.0000e-03 eta: 6:10:12 time: 0.3973 data_time: 0.0165 memory: 6717 grad_norm: 3.2948 loss: 1.1973 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1973 2023/04/14 10:16:36 - mmengine - INFO - Epoch(train) [69][ 120/1879] lr: 2.0000e-03 eta: 6:10:04 time: 0.3349 data_time: 0.0170 memory: 6717 grad_norm: 3.2824 loss: 1.2367 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2367 2023/04/14 10:16:45 - mmengine - INFO - Epoch(train) [69][ 140/1879] lr: 2.0000e-03 eta: 6:09:57 time: 0.4213 data_time: 0.0155 memory: 6717 grad_norm: 3.2609 loss: 1.2287 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2287 2023/04/14 10:16:52 - mmengine - INFO - Epoch(train) [69][ 160/1879] lr: 2.0000e-03 eta: 6:09:50 time: 0.3361 data_time: 0.0142 memory: 6717 grad_norm: 3.2054 loss: 1.2351 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2351 2023/04/14 10:17:00 - mmengine - INFO - Epoch(train) [69][ 180/1879] lr: 2.0000e-03 eta: 6:09:42 time: 0.4090 data_time: 0.0143 memory: 6717 grad_norm: 3.2315 loss: 1.1804 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1804 2023/04/14 10:17:06 - mmengine - INFO - Epoch(train) [69][ 200/1879] lr: 2.0000e-03 eta: 6:09:35 time: 0.3279 data_time: 0.0136 memory: 6717 grad_norm: 3.2796 loss: 1.2076 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2076 2023/04/14 10:17:14 - mmengine - INFO - Epoch(train) [69][ 220/1879] lr: 2.0000e-03 eta: 6:09:27 time: 0.3785 data_time: 0.0154 memory: 6717 grad_norm: 3.1976 loss: 1.0149 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0149 2023/04/14 10:17:17 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 10:17:21 - mmengine - INFO - Epoch(train) [69][ 240/1879] lr: 2.0000e-03 eta: 6:09:20 time: 0.3364 data_time: 0.0150 memory: 6717 grad_norm: 3.2346 loss: 1.1781 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1781 2023/04/14 10:17:28 - mmengine - INFO - Epoch(train) [69][ 260/1879] lr: 2.0000e-03 eta: 6:09:12 time: 0.3754 data_time: 0.0523 memory: 6717 grad_norm: 3.2337 loss: 1.0793 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0793 2023/04/14 10:17:36 - mmengine - INFO - Epoch(train) [69][ 280/1879] lr: 2.0000e-03 eta: 6:09:05 time: 0.3783 data_time: 0.1085 memory: 6717 grad_norm: 3.3218 loss: 1.2134 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2134 2023/04/14 10:17:44 - mmengine - INFO - Epoch(train) [69][ 300/1879] lr: 2.0000e-03 eta: 6:08:58 time: 0.3954 data_time: 0.0190 memory: 6717 grad_norm: 3.2099 loss: 1.2079 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2079 2023/04/14 10:17:50 - mmengine - INFO - Epoch(train) [69][ 320/1879] lr: 2.0000e-03 eta: 6:08:50 time: 0.3244 data_time: 0.0136 memory: 6717 grad_norm: 3.1162 loss: 1.1254 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1254 2023/04/14 10:17:58 - mmengine - INFO - Epoch(train) [69][ 340/1879] lr: 2.0000e-03 eta: 6:08:43 time: 0.4108 data_time: 0.0153 memory: 6717 grad_norm: 3.1905 loss: 1.2428 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2428 2023/04/14 10:18:05 - mmengine - INFO - Epoch(train) [69][ 360/1879] lr: 2.0000e-03 eta: 6:08:35 time: 0.3317 data_time: 0.0130 memory: 6717 grad_norm: 3.2764 loss: 1.3003 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3003 2023/04/14 10:18:13 - mmengine - INFO - Epoch(train) [69][ 380/1879] lr: 2.0000e-03 eta: 6:08:28 time: 0.3884 data_time: 0.0154 memory: 6717 grad_norm: 3.1601 loss: 1.1703 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1703 2023/04/14 10:18:20 - mmengine - INFO - Epoch(train) [69][ 400/1879] lr: 2.0000e-03 eta: 6:08:20 time: 0.3467 data_time: 0.0131 memory: 6717 grad_norm: 3.2497 loss: 1.2613 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2613 2023/04/14 10:18:28 - mmengine - INFO - Epoch(train) [69][ 420/1879] lr: 2.0000e-03 eta: 6:08:13 time: 0.4250 data_time: 0.0157 memory: 6717 grad_norm: 3.3205 loss: 1.1356 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1356 2023/04/14 10:18:34 - mmengine - INFO - Epoch(train) [69][ 440/1879] lr: 2.0000e-03 eta: 6:08:05 time: 0.3086 data_time: 0.0123 memory: 6717 grad_norm: 3.2316 loss: 1.1338 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1338 2023/04/14 10:18:42 - mmengine - INFO - Epoch(train) [69][ 460/1879] lr: 2.0000e-03 eta: 6:07:58 time: 0.4026 data_time: 0.0158 memory: 6717 grad_norm: 3.2594 loss: 1.1697 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1697 2023/04/14 10:18:49 - mmengine - INFO - Epoch(train) [69][ 480/1879] lr: 2.0000e-03 eta: 6:07:50 time: 0.3158 data_time: 0.0128 memory: 6717 grad_norm: 3.1380 loss: 1.1576 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1576 2023/04/14 10:18:56 - mmengine - INFO - Epoch(train) [69][ 500/1879] lr: 2.0000e-03 eta: 6:07:43 time: 0.3835 data_time: 0.0158 memory: 6717 grad_norm: 3.2462 loss: 1.1229 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1229 2023/04/14 10:19:03 - mmengine - INFO - Epoch(train) [69][ 520/1879] lr: 2.0000e-03 eta: 6:07:35 time: 0.3354 data_time: 0.0132 memory: 6717 grad_norm: 3.2314 loss: 1.0720 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0720 2023/04/14 10:19:11 - mmengine - INFO - Epoch(train) [69][ 540/1879] lr: 2.0000e-03 eta: 6:07:28 time: 0.3757 data_time: 0.0342 memory: 6717 grad_norm: 3.2854 loss: 1.1707 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1707 2023/04/14 10:19:18 - mmengine - INFO - Epoch(train) [69][ 560/1879] lr: 2.0000e-03 eta: 6:07:21 time: 0.3801 data_time: 0.1903 memory: 6717 grad_norm: 3.2051 loss: 1.1592 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1592 2023/04/14 10:19:25 - mmengine - INFO - Epoch(train) [69][ 580/1879] lr: 2.0000e-03 eta: 6:07:13 time: 0.3382 data_time: 0.1678 memory: 6717 grad_norm: 3.2469 loss: 1.2355 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2355 2023/04/14 10:19:33 - mmengine - INFO - Epoch(train) [69][ 600/1879] lr: 2.0000e-03 eta: 6:07:06 time: 0.3861 data_time: 0.2109 memory: 6717 grad_norm: 3.2771 loss: 1.1133 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1133 2023/04/14 10:19:40 - mmengine - INFO - Epoch(train) [69][ 620/1879] lr: 2.0000e-03 eta: 6:06:58 time: 0.3529 data_time: 0.1443 memory: 6717 grad_norm: 3.2149 loss: 1.2036 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2036 2023/04/14 10:19:47 - mmengine - INFO - Epoch(train) [69][ 640/1879] lr: 2.0000e-03 eta: 6:06:51 time: 0.3695 data_time: 0.0632 memory: 6717 grad_norm: 3.3368 loss: 1.2409 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2409 2023/04/14 10:19:54 - mmengine - INFO - Epoch(train) [69][ 660/1879] lr: 2.0000e-03 eta: 6:06:43 time: 0.3642 data_time: 0.0376 memory: 6717 grad_norm: 3.1334 loss: 1.3463 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3463 2023/04/14 10:20:02 - mmengine - INFO - Epoch(train) [69][ 680/1879] lr: 2.0000e-03 eta: 6:06:36 time: 0.3847 data_time: 0.0140 memory: 6717 grad_norm: 3.2159 loss: 1.2200 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2200 2023/04/14 10:20:10 - mmengine - INFO - Epoch(train) [69][ 700/1879] lr: 2.0000e-03 eta: 6:06:29 time: 0.3740 data_time: 0.0144 memory: 6717 grad_norm: 3.2745 loss: 1.2713 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2713 2023/04/14 10:20:16 - mmengine - INFO - Epoch(train) [69][ 720/1879] lr: 2.0000e-03 eta: 6:06:21 time: 0.3159 data_time: 0.0142 memory: 6717 grad_norm: 3.1995 loss: 1.0221 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0221 2023/04/14 10:20:25 - mmengine - INFO - Epoch(train) [69][ 740/1879] lr: 2.0000e-03 eta: 6:06:14 time: 0.4497 data_time: 0.0131 memory: 6717 grad_norm: 3.2060 loss: 1.1926 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1926 2023/04/14 10:20:32 - mmengine - INFO - Epoch(train) [69][ 760/1879] lr: 2.0000e-03 eta: 6:06:07 time: 0.3405 data_time: 0.0146 memory: 6717 grad_norm: 3.2456 loss: 1.2619 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.2619 2023/04/14 10:20:40 - mmengine - INFO - Epoch(train) [69][ 780/1879] lr: 2.0000e-03 eta: 6:05:59 time: 0.3972 data_time: 0.0131 memory: 6717 grad_norm: 3.2809 loss: 1.1569 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1569 2023/04/14 10:20:47 - mmengine - INFO - Epoch(train) [69][ 800/1879] lr: 2.0000e-03 eta: 6:05:52 time: 0.3401 data_time: 0.0444 memory: 6717 grad_norm: 3.2119 loss: 1.1209 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1209 2023/04/14 10:20:54 - mmengine - INFO - Epoch(train) [69][ 820/1879] lr: 2.0000e-03 eta: 6:05:45 time: 0.3867 data_time: 0.0272 memory: 6717 grad_norm: 3.2292 loss: 1.2595 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2595 2023/04/14 10:21:01 - mmengine - INFO - Epoch(train) [69][ 840/1879] lr: 2.0000e-03 eta: 6:05:37 time: 0.3539 data_time: 0.0968 memory: 6717 grad_norm: 3.3270 loss: 1.2849 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2849 2023/04/14 10:21:09 - mmengine - INFO - Epoch(train) [69][ 860/1879] lr: 2.0000e-03 eta: 6:05:30 time: 0.3918 data_time: 0.1022 memory: 6717 grad_norm: 3.2693 loss: 1.1348 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1348 2023/04/14 10:21:16 - mmengine - INFO - Epoch(train) [69][ 880/1879] lr: 2.0000e-03 eta: 6:05:22 time: 0.3597 data_time: 0.1801 memory: 6717 grad_norm: 3.1803 loss: 1.0545 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0545 2023/04/14 10:21:23 - mmengine - INFO - Epoch(train) [69][ 900/1879] lr: 2.0000e-03 eta: 6:05:15 time: 0.3323 data_time: 0.1647 memory: 6717 grad_norm: 3.2997 loss: 1.2419 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2419 2023/04/14 10:21:31 - mmengine - INFO - Epoch(train) [69][ 920/1879] lr: 2.0000e-03 eta: 6:05:07 time: 0.4074 data_time: 0.2676 memory: 6717 grad_norm: 3.2465 loss: 1.2723 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2723 2023/04/14 10:21:38 - mmengine - INFO - Epoch(train) [69][ 940/1879] lr: 2.0000e-03 eta: 6:05:00 time: 0.3246 data_time: 0.1417 memory: 6717 grad_norm: 3.2273 loss: 1.2775 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2775 2023/04/14 10:21:46 - mmengine - INFO - Epoch(train) [69][ 960/1879] lr: 2.0000e-03 eta: 6:04:53 time: 0.4327 data_time: 0.1762 memory: 6717 grad_norm: 3.2842 loss: 1.2609 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2609 2023/04/14 10:21:53 - mmengine - INFO - Epoch(train) [69][ 980/1879] lr: 2.0000e-03 eta: 6:04:45 time: 0.3203 data_time: 0.0364 memory: 6717 grad_norm: 3.2125 loss: 1.2670 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2670 2023/04/14 10:22:01 - mmengine - INFO - Epoch(train) [69][1000/1879] lr: 2.0000e-03 eta: 6:04:38 time: 0.4298 data_time: 0.0156 memory: 6717 grad_norm: 3.3232 loss: 1.1683 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1683 2023/04/14 10:22:08 - mmengine - INFO - Epoch(train) [69][1020/1879] lr: 2.0000e-03 eta: 6:04:30 time: 0.3328 data_time: 0.0131 memory: 6717 grad_norm: 3.2927 loss: 1.2301 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2301 2023/04/14 10:22:16 - mmengine - INFO - Epoch(train) [69][1040/1879] lr: 2.0000e-03 eta: 6:04:23 time: 0.3982 data_time: 0.0141 memory: 6717 grad_norm: 3.2552 loss: 1.0551 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0551 2023/04/14 10:22:22 - mmengine - INFO - Epoch(train) [69][1060/1879] lr: 2.0000e-03 eta: 6:04:15 time: 0.3216 data_time: 0.0144 memory: 6717 grad_norm: 3.2921 loss: 1.0806 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0806 2023/04/14 10:22:30 - mmengine - INFO - Epoch(train) [69][1080/1879] lr: 2.0000e-03 eta: 6:04:08 time: 0.3830 data_time: 0.0144 memory: 6717 grad_norm: 3.3124 loss: 1.1259 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1259 2023/04/14 10:22:36 - mmengine - INFO - Epoch(train) [69][1100/1879] lr: 2.0000e-03 eta: 6:04:00 time: 0.3091 data_time: 0.0145 memory: 6717 grad_norm: 3.2674 loss: 1.1533 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1533 2023/04/14 10:22:44 - mmengine - INFO - Epoch(train) [69][1120/1879] lr: 2.0000e-03 eta: 6:03:53 time: 0.4081 data_time: 0.0665 memory: 6717 grad_norm: 3.1634 loss: 1.1135 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1135 2023/04/14 10:22:52 - mmengine - INFO - Epoch(train) [69][1140/1879] lr: 2.0000e-03 eta: 6:03:46 time: 0.3526 data_time: 0.0193 memory: 6717 grad_norm: 3.2390 loss: 1.0875 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0875 2023/04/14 10:22:59 - mmengine - INFO - Epoch(train) [69][1160/1879] lr: 2.0000e-03 eta: 6:03:38 time: 0.3493 data_time: 0.0558 memory: 6717 grad_norm: 3.1948 loss: 1.1941 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1941 2023/04/14 10:23:07 - mmengine - INFO - Epoch(train) [69][1180/1879] lr: 2.0000e-03 eta: 6:03:31 time: 0.4030 data_time: 0.0278 memory: 6717 grad_norm: 3.1711 loss: 1.2685 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2685 2023/04/14 10:23:14 - mmengine - INFO - Epoch(train) [69][1200/1879] lr: 2.0000e-03 eta: 6:03:24 time: 0.3768 data_time: 0.0163 memory: 6717 grad_norm: 3.1942 loss: 1.0697 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0697 2023/04/14 10:23:21 - mmengine - INFO - Epoch(train) [69][1220/1879] lr: 2.0000e-03 eta: 6:03:16 time: 0.3532 data_time: 0.0141 memory: 6717 grad_norm: 3.2239 loss: 1.2688 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.2688 2023/04/14 10:23:25 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 10:23:29 - mmengine - INFO - Epoch(train) [69][1240/1879] lr: 2.0000e-03 eta: 6:03:09 time: 0.4011 data_time: 0.0161 memory: 6717 grad_norm: 3.2547 loss: 1.1874 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1874 2023/04/14 10:23:36 - mmengine - INFO - Epoch(train) [69][1260/1879] lr: 2.0000e-03 eta: 6:03:01 time: 0.3458 data_time: 0.0127 memory: 6717 grad_norm: 3.2430 loss: 1.0872 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0872 2023/04/14 10:23:44 - mmengine - INFO - Epoch(train) [69][1280/1879] lr: 2.0000e-03 eta: 6:02:54 time: 0.3808 data_time: 0.0143 memory: 6717 grad_norm: 3.3118 loss: 1.3550 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.3550 2023/04/14 10:23:51 - mmengine - INFO - Epoch(train) [69][1300/1879] lr: 2.0000e-03 eta: 6:02:46 time: 0.3486 data_time: 0.0133 memory: 6717 grad_norm: 3.3257 loss: 1.0826 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0826 2023/04/14 10:23:59 - mmengine - INFO - Epoch(train) [69][1320/1879] lr: 2.0000e-03 eta: 6:02:39 time: 0.4075 data_time: 0.0156 memory: 6717 grad_norm: 3.2784 loss: 1.2176 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2176 2023/04/14 10:24:05 - mmengine - INFO - Epoch(train) [69][1340/1879] lr: 2.0000e-03 eta: 6:02:31 time: 0.3231 data_time: 0.0135 memory: 6717 grad_norm: 3.2502 loss: 1.1846 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1846 2023/04/14 10:24:13 - mmengine - INFO - Epoch(train) [69][1360/1879] lr: 2.0000e-03 eta: 6:02:24 time: 0.3850 data_time: 0.0140 memory: 6717 grad_norm: 3.3378 loss: 1.3016 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3016 2023/04/14 10:24:19 - mmengine - INFO - Epoch(train) [69][1380/1879] lr: 2.0000e-03 eta: 6:02:16 time: 0.3159 data_time: 0.0148 memory: 6717 grad_norm: 3.2063 loss: 1.1466 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1466 2023/04/14 10:24:28 - mmengine - INFO - Epoch(train) [69][1400/1879] lr: 2.0000e-03 eta: 6:02:09 time: 0.4064 data_time: 0.0134 memory: 6717 grad_norm: 3.2524 loss: 1.3348 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3348 2023/04/14 10:24:34 - mmengine - INFO - Epoch(train) [69][1420/1879] lr: 2.0000e-03 eta: 6:02:01 time: 0.3163 data_time: 0.0151 memory: 6717 grad_norm: 3.2100 loss: 1.2993 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2993 2023/04/14 10:24:42 - mmengine - INFO - Epoch(train) [69][1440/1879] lr: 2.0000e-03 eta: 6:01:54 time: 0.4143 data_time: 0.0141 memory: 6717 grad_norm: 3.2839 loss: 1.1812 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1812 2023/04/14 10:24:49 - mmengine - INFO - Epoch(train) [69][1460/1879] lr: 2.0000e-03 eta: 6:01:47 time: 0.3481 data_time: 0.0749 memory: 6717 grad_norm: 3.2535 loss: 1.0018 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0018 2023/04/14 10:24:58 - mmengine - INFO - Epoch(train) [69][1480/1879] lr: 2.0000e-03 eta: 6:01:40 time: 0.4363 data_time: 0.0312 memory: 6717 grad_norm: 3.2873 loss: 1.2603 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2603 2023/04/14 10:25:04 - mmengine - INFO - Epoch(train) [69][1500/1879] lr: 2.0000e-03 eta: 6:01:32 time: 0.3283 data_time: 0.0164 memory: 6717 grad_norm: 3.2503 loss: 1.2147 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2147 2023/04/14 10:25:13 - mmengine - INFO - Epoch(train) [69][1520/1879] lr: 2.0000e-03 eta: 6:01:25 time: 0.4067 data_time: 0.0128 memory: 6717 grad_norm: 3.2838 loss: 1.3504 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.3504 2023/04/14 10:25:19 - mmengine - INFO - Epoch(train) [69][1540/1879] lr: 2.0000e-03 eta: 6:01:17 time: 0.3331 data_time: 0.0157 memory: 6717 grad_norm: 3.1633 loss: 1.1579 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1579 2023/04/14 10:25:28 - mmengine - INFO - Epoch(train) [69][1560/1879] lr: 2.0000e-03 eta: 6:01:10 time: 0.4285 data_time: 0.0140 memory: 6717 grad_norm: 3.3408 loss: 1.2721 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2721 2023/04/14 10:25:34 - mmengine - INFO - Epoch(train) [69][1580/1879] lr: 2.0000e-03 eta: 6:01:03 time: 0.3252 data_time: 0.0148 memory: 6717 grad_norm: 3.2507 loss: 1.1179 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1179 2023/04/14 10:25:42 - mmengine - INFO - Epoch(train) [69][1600/1879] lr: 2.0000e-03 eta: 6:00:55 time: 0.3705 data_time: 0.0138 memory: 6717 grad_norm: 3.2694 loss: 1.1256 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1256 2023/04/14 10:25:48 - mmengine - INFO - Epoch(train) [69][1620/1879] lr: 2.0000e-03 eta: 6:00:48 time: 0.3293 data_time: 0.0160 memory: 6717 grad_norm: 3.1629 loss: 1.2640 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2640 2023/04/14 10:25:57 - mmengine - INFO - Epoch(train) [69][1640/1879] lr: 2.0000e-03 eta: 6:00:41 time: 0.4423 data_time: 0.0117 memory: 6717 grad_norm: 3.3137 loss: 1.1222 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1222 2023/04/14 10:26:04 - mmengine - INFO - Epoch(train) [69][1660/1879] lr: 2.0000e-03 eta: 6:00:33 time: 0.3311 data_time: 0.0143 memory: 6717 grad_norm: 3.2735 loss: 1.1326 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1326 2023/04/14 10:26:12 - mmengine - INFO - Epoch(train) [69][1680/1879] lr: 2.0000e-03 eta: 6:00:26 time: 0.4172 data_time: 0.0144 memory: 6717 grad_norm: 3.4372 loss: 1.2724 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2724 2023/04/14 10:26:18 - mmengine - INFO - Epoch(train) [69][1700/1879] lr: 2.0000e-03 eta: 6:00:18 time: 0.3141 data_time: 0.0149 memory: 6717 grad_norm: 3.0920 loss: 1.1307 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1307 2023/04/14 10:26:27 - mmengine - INFO - Epoch(train) [69][1720/1879] lr: 2.0000e-03 eta: 6:00:11 time: 0.4119 data_time: 0.0162 memory: 6717 grad_norm: 3.2654 loss: 1.1764 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1764 2023/04/14 10:26:34 - mmengine - INFO - Epoch(train) [69][1740/1879] lr: 2.0000e-03 eta: 6:00:04 time: 0.3457 data_time: 0.0171 memory: 6717 grad_norm: 3.1835 loss: 1.1145 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1145 2023/04/14 10:26:41 - mmengine - INFO - Epoch(train) [69][1760/1879] lr: 2.0000e-03 eta: 5:59:56 time: 0.3525 data_time: 0.0188 memory: 6717 grad_norm: 3.2945 loss: 1.0391 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0391 2023/04/14 10:26:47 - mmengine - INFO - Epoch(train) [69][1780/1879] lr: 2.0000e-03 eta: 5:59:48 time: 0.3407 data_time: 0.0146 memory: 6717 grad_norm: 3.2323 loss: 1.2708 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2708 2023/04/14 10:26:56 - mmengine - INFO - Epoch(train) [69][1800/1879] lr: 2.0000e-03 eta: 5:59:41 time: 0.4336 data_time: 0.0128 memory: 6717 grad_norm: 3.2560 loss: 1.1594 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1594 2023/04/14 10:27:03 - mmengine - INFO - Epoch(train) [69][1820/1879] lr: 2.0000e-03 eta: 5:59:34 time: 0.3303 data_time: 0.0161 memory: 6717 grad_norm: 3.2647 loss: 1.0989 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0989 2023/04/14 10:27:11 - mmengine - INFO - Epoch(train) [69][1840/1879] lr: 2.0000e-03 eta: 5:59:27 time: 0.4060 data_time: 0.0137 memory: 6717 grad_norm: 3.2849 loss: 1.1802 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1802 2023/04/14 10:27:18 - mmengine - INFO - Epoch(train) [69][1860/1879] lr: 2.0000e-03 eta: 5:59:19 time: 0.3464 data_time: 0.0149 memory: 6717 grad_norm: 3.3229 loss: 1.3791 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3791 2023/04/14 10:27:24 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 10:27:24 - mmengine - INFO - Epoch(train) [69][1879/1879] lr: 2.0000e-03 eta: 5:59:12 time: 0.3337 data_time: 0.0120 memory: 6717 grad_norm: 3.2746 loss: 1.1671 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 1.1671 2023/04/14 10:27:24 - mmengine - INFO - Saving checkpoint at 69 epochs 2023/04/14 10:27:34 - mmengine - INFO - Epoch(val) [69][ 20/155] eta: 0:00:58 time: 0.4334 data_time: 0.3996 memory: 1391 2023/04/14 10:27:41 - mmengine - INFO - Epoch(val) [69][ 40/155] eta: 0:00:44 time: 0.3460 data_time: 0.3126 memory: 1391 2023/04/14 10:27:48 - mmengine - INFO - Epoch(val) [69][ 60/155] eta: 0:00:36 time: 0.3669 data_time: 0.3325 memory: 1391 2023/04/14 10:27:55 - mmengine - INFO - Epoch(val) [69][ 80/155] eta: 0:00:28 time: 0.3742 data_time: 0.3403 memory: 1391 2023/04/14 10:28:04 - mmengine - INFO - Epoch(val) [69][100/155] eta: 0:00:21 time: 0.4236 data_time: 0.3896 memory: 1391 2023/04/14 10:28:10 - mmengine - INFO - Epoch(val) [69][120/155] eta: 0:00:13 time: 0.3187 data_time: 0.2854 memory: 1391 2023/04/14 10:28:18 - mmengine - INFO - Epoch(val) [69][140/155] eta: 0:00:05 time: 0.3691 data_time: 0.3349 memory: 1391 2023/04/14 10:28:26 - mmengine - INFO - Epoch(val) [69][155/155] acc/top1: 0.6648 acc/top5: 0.8728 acc/mean1: 0.6648 data_time: 0.2922 time: 0.3256 2023/04/14 10:28:26 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_63.pth is removed 2023/04/14 10:28:27 - mmengine - INFO - The best checkpoint with 0.6648 acc/top1 at 69 epoch is saved to best_acc_top1_epoch_69.pth. 2023/04/14 10:28:37 - mmengine - INFO - Epoch(train) [70][ 20/1879] lr: 2.0000e-03 eta: 5:59:05 time: 0.4900 data_time: 0.3510 memory: 6717 grad_norm: 3.2176 loss: 1.2131 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2131 2023/04/14 10:28:44 - mmengine - INFO - Epoch(train) [70][ 40/1879] lr: 2.0000e-03 eta: 5:58:58 time: 0.3490 data_time: 0.2167 memory: 6717 grad_norm: 3.1374 loss: 1.0810 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0810 2023/04/14 10:28:52 - mmengine - INFO - Epoch(train) [70][ 60/1879] lr: 2.0000e-03 eta: 5:58:51 time: 0.4348 data_time: 0.2906 memory: 6717 grad_norm: 3.2518 loss: 1.4330 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.4330 2023/04/14 10:28:59 - mmengine - INFO - Epoch(train) [70][ 80/1879] lr: 2.0000e-03 eta: 5:58:43 time: 0.3267 data_time: 0.1876 memory: 6717 grad_norm: 3.2549 loss: 1.2868 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2868 2023/04/14 10:29:07 - mmengine - INFO - Epoch(train) [70][ 100/1879] lr: 2.0000e-03 eta: 5:58:36 time: 0.4126 data_time: 0.2694 memory: 6717 grad_norm: 3.3640 loss: 1.2621 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2621 2023/04/14 10:29:13 - mmengine - INFO - Epoch(train) [70][ 120/1879] lr: 2.0000e-03 eta: 5:58:28 time: 0.3080 data_time: 0.1705 memory: 6717 grad_norm: 3.2802 loss: 1.2638 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2638 2023/04/14 10:29:22 - mmengine - INFO - Epoch(train) [70][ 140/1879] lr: 2.0000e-03 eta: 5:58:21 time: 0.4219 data_time: 0.2835 memory: 6717 grad_norm: 3.2616 loss: 1.2574 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2574 2023/04/14 10:29:28 - mmengine - INFO - Epoch(train) [70][ 160/1879] lr: 2.0000e-03 eta: 5:58:13 time: 0.2981 data_time: 0.1607 memory: 6717 grad_norm: 3.2718 loss: 1.1751 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1751 2023/04/14 10:29:35 - mmengine - INFO - Epoch(train) [70][ 180/1879] lr: 2.0000e-03 eta: 5:58:06 time: 0.3931 data_time: 0.2201 memory: 6717 grad_norm: 3.2545 loss: 1.1435 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1435 2023/04/14 10:29:42 - mmengine - INFO - Epoch(train) [70][ 200/1879] lr: 2.0000e-03 eta: 5:57:58 time: 0.3282 data_time: 0.1325 memory: 6717 grad_norm: 3.2572 loss: 1.2643 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.2643 2023/04/14 10:29:50 - mmengine - INFO - Epoch(train) [70][ 220/1879] lr: 2.0000e-03 eta: 5:57:51 time: 0.3991 data_time: 0.2379 memory: 6717 grad_norm: 3.2670 loss: 1.0551 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0551 2023/04/14 10:29:58 - mmengine - INFO - Epoch(train) [70][ 240/1879] lr: 2.0000e-03 eta: 5:57:44 time: 0.3771 data_time: 0.1281 memory: 6717 grad_norm: 3.2393 loss: 1.0597 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0597 2023/04/14 10:30:06 - mmengine - INFO - Epoch(train) [70][ 260/1879] lr: 2.0000e-03 eta: 5:57:37 time: 0.3976 data_time: 0.2100 memory: 6717 grad_norm: 3.2360 loss: 1.2994 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2994 2023/04/14 10:30:12 - mmengine - INFO - Epoch(train) [70][ 280/1879] lr: 2.0000e-03 eta: 5:57:29 time: 0.3312 data_time: 0.1374 memory: 6717 grad_norm: 3.2612 loss: 0.9706 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9706 2023/04/14 10:30:21 - mmengine - INFO - Epoch(train) [70][ 300/1879] lr: 2.0000e-03 eta: 5:57:22 time: 0.4217 data_time: 0.2590 memory: 6717 grad_norm: 3.2097 loss: 1.2458 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2458 2023/04/14 10:30:27 - mmengine - INFO - Epoch(train) [70][ 320/1879] lr: 2.0000e-03 eta: 5:57:14 time: 0.3041 data_time: 0.1608 memory: 6717 grad_norm: 3.2941 loss: 1.1109 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1109 2023/04/14 10:30:35 - mmengine - INFO - Epoch(train) [70][ 340/1879] lr: 2.0000e-03 eta: 5:57:07 time: 0.4000 data_time: 0.2378 memory: 6717 grad_norm: 3.1443 loss: 1.0062 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0062 2023/04/14 10:30:38 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 10:30:41 - mmengine - INFO - Epoch(train) [70][ 360/1879] lr: 2.0000e-03 eta: 5:56:59 time: 0.3301 data_time: 0.1645 memory: 6717 grad_norm: 3.3388 loss: 1.1305 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1305 2023/04/14 10:30:50 - mmengine - INFO - Epoch(train) [70][ 380/1879] lr: 2.0000e-03 eta: 5:56:52 time: 0.4320 data_time: 0.2847 memory: 6717 grad_norm: 3.2941 loss: 1.2120 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2120 2023/04/14 10:30:57 - mmengine - INFO - Epoch(train) [70][ 400/1879] lr: 2.0000e-03 eta: 5:56:45 time: 0.3464 data_time: 0.2059 memory: 6717 grad_norm: 3.3385 loss: 1.3627 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.3627 2023/04/14 10:31:05 - mmengine - INFO - Epoch(train) [70][ 420/1879] lr: 2.0000e-03 eta: 5:56:38 time: 0.4170 data_time: 0.2774 memory: 6717 grad_norm: 3.1827 loss: 1.1388 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1388 2023/04/14 10:31:12 - mmengine - INFO - Epoch(train) [70][ 440/1879] lr: 2.0000e-03 eta: 5:56:30 time: 0.3390 data_time: 0.1977 memory: 6717 grad_norm: 3.2080 loss: 0.9537 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9537 2023/04/14 10:31:20 - mmengine - INFO - Epoch(train) [70][ 460/1879] lr: 2.0000e-03 eta: 5:56:23 time: 0.4227 data_time: 0.2798 memory: 6717 grad_norm: 3.3572 loss: 1.1954 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1954 2023/04/14 10:31:27 - mmengine - INFO - Epoch(train) [70][ 480/1879] lr: 2.0000e-03 eta: 5:56:15 time: 0.3400 data_time: 0.2001 memory: 6717 grad_norm: 3.2676 loss: 1.1380 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1380 2023/04/14 10:31:35 - mmengine - INFO - Epoch(train) [70][ 500/1879] lr: 2.0000e-03 eta: 5:56:08 time: 0.4023 data_time: 0.2600 memory: 6717 grad_norm: 3.2698 loss: 1.2074 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2074 2023/04/14 10:31:42 - mmengine - INFO - Epoch(train) [70][ 520/1879] lr: 2.0000e-03 eta: 5:56:01 time: 0.3364 data_time: 0.1959 memory: 6717 grad_norm: 3.2881 loss: 1.0485 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0485 2023/04/14 10:31:50 - mmengine - INFO - Epoch(train) [70][ 540/1879] lr: 2.0000e-03 eta: 5:55:53 time: 0.3970 data_time: 0.2567 memory: 6717 grad_norm: 3.1561 loss: 1.2161 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2161 2023/04/14 10:31:57 - mmengine - INFO - Epoch(train) [70][ 560/1879] lr: 2.0000e-03 eta: 5:55:46 time: 0.3257 data_time: 0.1853 memory: 6717 grad_norm: 3.2420 loss: 1.1001 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1001 2023/04/14 10:32:05 - mmengine - INFO - Epoch(train) [70][ 580/1879] lr: 2.0000e-03 eta: 5:55:39 time: 0.4189 data_time: 0.2770 memory: 6717 grad_norm: 3.2657 loss: 1.3564 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3564 2023/04/14 10:32:11 - mmengine - INFO - Epoch(train) [70][ 600/1879] lr: 2.0000e-03 eta: 5:55:31 time: 0.3244 data_time: 0.1817 memory: 6717 grad_norm: 3.2775 loss: 1.2252 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2252 2023/04/14 10:32:20 - mmengine - INFO - Epoch(train) [70][ 620/1879] lr: 2.0000e-03 eta: 5:55:24 time: 0.4200 data_time: 0.2785 memory: 6717 grad_norm: 3.2197 loss: 1.1307 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1307 2023/04/14 10:32:26 - mmengine - INFO - Epoch(train) [70][ 640/1879] lr: 2.0000e-03 eta: 5:55:16 time: 0.3192 data_time: 0.1845 memory: 6717 grad_norm: 3.2260 loss: 1.1556 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1556 2023/04/14 10:32:34 - mmengine - INFO - Epoch(train) [70][ 660/1879] lr: 2.0000e-03 eta: 5:55:09 time: 0.3733 data_time: 0.1608 memory: 6717 grad_norm: 3.3502 loss: 1.3324 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3324 2023/04/14 10:32:41 - mmengine - INFO - Epoch(train) [70][ 680/1879] lr: 2.0000e-03 eta: 5:55:01 time: 0.3489 data_time: 0.1890 memory: 6717 grad_norm: 3.2059 loss: 1.1446 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1446 2023/04/14 10:32:49 - mmengine - INFO - Epoch(train) [70][ 700/1879] lr: 2.0000e-03 eta: 5:54:54 time: 0.3967 data_time: 0.0778 memory: 6717 grad_norm: 3.2251 loss: 1.0625 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0625 2023/04/14 10:32:55 - mmengine - INFO - Epoch(train) [70][ 720/1879] lr: 2.0000e-03 eta: 5:54:46 time: 0.3408 data_time: 0.0572 memory: 6717 grad_norm: 3.2552 loss: 1.1200 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1200 2023/04/14 10:33:03 - mmengine - INFO - Epoch(train) [70][ 740/1879] lr: 2.0000e-03 eta: 5:54:39 time: 0.3885 data_time: 0.0266 memory: 6717 grad_norm: 3.3262 loss: 1.2204 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2204 2023/04/14 10:33:10 - mmengine - INFO - Epoch(train) [70][ 760/1879] lr: 2.0000e-03 eta: 5:54:31 time: 0.3389 data_time: 0.0477 memory: 6717 grad_norm: 3.2351 loss: 1.1210 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1210 2023/04/14 10:33:17 - mmengine - INFO - Epoch(train) [70][ 780/1879] lr: 2.0000e-03 eta: 5:54:24 time: 0.3407 data_time: 0.0734 memory: 6717 grad_norm: 3.2707 loss: 1.2028 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2028 2023/04/14 10:33:24 - mmengine - INFO - Epoch(train) [70][ 800/1879] lr: 2.0000e-03 eta: 5:54:16 time: 0.3650 data_time: 0.0895 memory: 6717 grad_norm: 3.2824 loss: 1.1612 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1612 2023/04/14 10:33:31 - mmengine - INFO - Epoch(train) [70][ 820/1879] lr: 2.0000e-03 eta: 5:54:09 time: 0.3625 data_time: 0.1383 memory: 6717 grad_norm: 3.2566 loss: 1.2433 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2433 2023/04/14 10:33:39 - mmengine - INFO - Epoch(train) [70][ 840/1879] lr: 2.0000e-03 eta: 5:54:01 time: 0.3715 data_time: 0.2118 memory: 6717 grad_norm: 3.2835 loss: 1.2776 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2776 2023/04/14 10:33:46 - mmengine - INFO - Epoch(train) [70][ 860/1879] lr: 2.0000e-03 eta: 5:53:54 time: 0.3549 data_time: 0.1917 memory: 6717 grad_norm: 3.2787 loss: 1.3291 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3291 2023/04/14 10:33:54 - mmengine - INFO - Epoch(train) [70][ 880/1879] lr: 2.0000e-03 eta: 5:53:47 time: 0.3872 data_time: 0.0940 memory: 6717 grad_norm: 3.2679 loss: 1.1689 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1689 2023/04/14 10:34:02 - mmengine - INFO - Epoch(train) [70][ 900/1879] lr: 2.0000e-03 eta: 5:53:39 time: 0.3987 data_time: 0.0173 memory: 6717 grad_norm: 3.3016 loss: 1.1994 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1994 2023/04/14 10:34:08 - mmengine - INFO - Epoch(train) [70][ 920/1879] lr: 2.0000e-03 eta: 5:53:32 time: 0.3393 data_time: 0.0146 memory: 6717 grad_norm: 3.3006 loss: 1.2488 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.2488 2023/04/14 10:34:16 - mmengine - INFO - Epoch(train) [70][ 940/1879] lr: 2.0000e-03 eta: 5:53:25 time: 0.3804 data_time: 0.0495 memory: 6717 grad_norm: 3.3529 loss: 1.0888 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0888 2023/04/14 10:34:23 - mmengine - INFO - Epoch(train) [70][ 960/1879] lr: 2.0000e-03 eta: 5:53:17 time: 0.3448 data_time: 0.0199 memory: 6717 grad_norm: 3.2285 loss: 1.1717 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1717 2023/04/14 10:34:31 - mmengine - INFO - Epoch(train) [70][ 980/1879] lr: 2.0000e-03 eta: 5:53:10 time: 0.3938 data_time: 0.0143 memory: 6717 grad_norm: 3.2940 loss: 1.3925 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3925 2023/04/14 10:34:39 - mmengine - INFO - Epoch(train) [70][1000/1879] lr: 2.0000e-03 eta: 5:53:03 time: 0.4025 data_time: 0.0446 memory: 6717 grad_norm: 3.2182 loss: 1.2029 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2029 2023/04/14 10:34:45 - mmengine - INFO - Epoch(train) [70][1020/1879] lr: 2.0000e-03 eta: 5:52:55 time: 0.3308 data_time: 0.0322 memory: 6717 grad_norm: 3.3147 loss: 1.1692 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1692 2023/04/14 10:34:53 - mmengine - INFO - Epoch(train) [70][1040/1879] lr: 2.0000e-03 eta: 5:52:48 time: 0.3792 data_time: 0.0426 memory: 6717 grad_norm: 3.3254 loss: 1.0896 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0896 2023/04/14 10:35:00 - mmengine - INFO - Epoch(train) [70][1060/1879] lr: 2.0000e-03 eta: 5:52:40 time: 0.3402 data_time: 0.0718 memory: 6717 grad_norm: 3.2383 loss: 1.1559 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.1559 2023/04/14 10:35:08 - mmengine - INFO - Epoch(train) [70][1080/1879] lr: 2.0000e-03 eta: 5:52:33 time: 0.4040 data_time: 0.0783 memory: 6717 grad_norm: 3.2009 loss: 1.0994 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0994 2023/04/14 10:35:15 - mmengine - INFO - Epoch(train) [70][1100/1879] lr: 2.0000e-03 eta: 5:52:25 time: 0.3390 data_time: 0.1501 memory: 6717 grad_norm: 3.2601 loss: 1.2629 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2629 2023/04/14 10:35:22 - mmengine - INFO - Epoch(train) [70][1120/1879] lr: 2.0000e-03 eta: 5:52:18 time: 0.3838 data_time: 0.2417 memory: 6717 grad_norm: 3.2050 loss: 1.2100 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2100 2023/04/14 10:35:28 - mmengine - INFO - Epoch(train) [70][1140/1879] lr: 2.0000e-03 eta: 5:52:10 time: 0.3031 data_time: 0.1633 memory: 6717 grad_norm: 3.2825 loss: 1.2009 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2009 2023/04/14 10:35:37 - mmengine - INFO - Epoch(train) [70][1160/1879] lr: 2.0000e-03 eta: 5:52:03 time: 0.4248 data_time: 0.2821 memory: 6717 grad_norm: 3.2242 loss: 1.1316 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1316 2023/04/14 10:35:43 - mmengine - INFO - Epoch(train) [70][1180/1879] lr: 2.0000e-03 eta: 5:51:55 time: 0.3134 data_time: 0.1609 memory: 6717 grad_norm: 3.3155 loss: 1.4143 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.4143 2023/04/14 10:35:52 - mmengine - INFO - Epoch(train) [70][1200/1879] lr: 2.0000e-03 eta: 5:51:48 time: 0.4276 data_time: 0.2695 memory: 6717 grad_norm: 3.2968 loss: 1.0984 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0984 2023/04/14 10:35:59 - mmengine - INFO - Epoch(train) [70][1220/1879] lr: 2.0000e-03 eta: 5:51:41 time: 0.3558 data_time: 0.1154 memory: 6717 grad_norm: 3.2617 loss: 1.2108 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2108 2023/04/14 10:36:07 - mmengine - INFO - Epoch(train) [70][1240/1879] lr: 2.0000e-03 eta: 5:51:33 time: 0.3800 data_time: 0.1868 memory: 6717 grad_norm: 3.2274 loss: 1.0928 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0928 2023/04/14 10:36:14 - mmengine - INFO - Epoch(train) [70][1260/1879] lr: 2.0000e-03 eta: 5:51:26 time: 0.3619 data_time: 0.0635 memory: 6717 grad_norm: 3.2257 loss: 1.2051 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2051 2023/04/14 10:36:20 - mmengine - INFO - Epoch(train) [70][1280/1879] lr: 2.0000e-03 eta: 5:51:18 time: 0.3227 data_time: 0.0208 memory: 6717 grad_norm: 3.2978 loss: 1.0053 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0053 2023/04/14 10:36:28 - mmengine - INFO - Epoch(train) [70][1300/1879] lr: 2.0000e-03 eta: 5:51:11 time: 0.3861 data_time: 0.0187 memory: 6717 grad_norm: 3.2755 loss: 1.2197 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2197 2023/04/14 10:36:35 - mmengine - INFO - Epoch(train) [70][1320/1879] lr: 2.0000e-03 eta: 5:51:03 time: 0.3473 data_time: 0.0179 memory: 6717 grad_norm: 3.2898 loss: 1.1058 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1058 2023/04/14 10:36:43 - mmengine - INFO - Epoch(train) [70][1340/1879] lr: 2.0000e-03 eta: 5:50:56 time: 0.4194 data_time: 0.0136 memory: 6717 grad_norm: 3.3234 loss: 1.1137 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1137 2023/04/14 10:36:46 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 10:36:50 - mmengine - INFO - Epoch(train) [70][1360/1879] lr: 2.0000e-03 eta: 5:50:48 time: 0.3116 data_time: 0.0143 memory: 6717 grad_norm: 3.2679 loss: 1.1608 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1608 2023/04/14 10:36:57 - mmengine - INFO - Epoch(train) [70][1380/1879] lr: 2.0000e-03 eta: 5:50:41 time: 0.3693 data_time: 0.0145 memory: 6717 grad_norm: 3.3876 loss: 1.2728 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2728 2023/04/14 10:37:05 - mmengine - INFO - Epoch(train) [70][1400/1879] lr: 2.0000e-03 eta: 5:50:34 time: 0.3804 data_time: 0.0141 memory: 6717 grad_norm: 3.2801 loss: 1.1859 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1859 2023/04/14 10:37:11 - mmengine - INFO - Epoch(train) [70][1420/1879] lr: 2.0000e-03 eta: 5:50:26 time: 0.3397 data_time: 0.0146 memory: 6717 grad_norm: 3.3116 loss: 1.2865 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2865 2023/04/14 10:37:20 - mmengine - INFO - Epoch(train) [70][1440/1879] lr: 2.0000e-03 eta: 5:50:19 time: 0.4138 data_time: 0.0149 memory: 6717 grad_norm: 3.2134 loss: 1.2502 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2502 2023/04/14 10:37:26 - mmengine - INFO - Epoch(train) [70][1460/1879] lr: 2.0000e-03 eta: 5:50:11 time: 0.3251 data_time: 0.0135 memory: 6717 grad_norm: 3.2456 loss: 1.0198 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0198 2023/04/14 10:37:34 - mmengine - INFO - Epoch(train) [70][1480/1879] lr: 2.0000e-03 eta: 5:50:04 time: 0.3924 data_time: 0.0147 memory: 6717 grad_norm: 3.2969 loss: 1.2148 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2148 2023/04/14 10:37:41 - mmengine - INFO - Epoch(train) [70][1500/1879] lr: 2.0000e-03 eta: 5:49:56 time: 0.3580 data_time: 0.0140 memory: 6717 grad_norm: 3.3339 loss: 1.2468 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2468 2023/04/14 10:37:49 - mmengine - INFO - Epoch(train) [70][1520/1879] lr: 2.0000e-03 eta: 5:49:49 time: 0.3840 data_time: 0.0163 memory: 6717 grad_norm: 3.3550 loss: 1.0900 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0900 2023/04/14 10:37:56 - mmengine - INFO - Epoch(train) [70][1540/1879] lr: 2.0000e-03 eta: 5:49:42 time: 0.3535 data_time: 0.0135 memory: 6717 grad_norm: 3.2751 loss: 1.1635 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1635 2023/04/14 10:38:04 - mmengine - INFO - Epoch(train) [70][1560/1879] lr: 2.0000e-03 eta: 5:49:35 time: 0.4239 data_time: 0.0144 memory: 6717 grad_norm: 3.2758 loss: 1.1487 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1487 2023/04/14 10:38:12 - mmengine - INFO - Epoch(train) [70][1580/1879] lr: 2.0000e-03 eta: 5:49:27 time: 0.3561 data_time: 0.0142 memory: 6717 grad_norm: 3.2238 loss: 1.1617 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1617 2023/04/14 10:38:19 - mmengine - INFO - Epoch(train) [70][1600/1879] lr: 2.0000e-03 eta: 5:49:19 time: 0.3503 data_time: 0.0130 memory: 6717 grad_norm: 3.2460 loss: 1.2221 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2221 2023/04/14 10:38:26 - mmengine - INFO - Epoch(train) [70][1620/1879] lr: 2.0000e-03 eta: 5:49:12 time: 0.3793 data_time: 0.0148 memory: 6717 grad_norm: 3.3131 loss: 1.2760 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2760 2023/04/14 10:38:33 - mmengine - INFO - Epoch(train) [70][1640/1879] lr: 2.0000e-03 eta: 5:49:05 time: 0.3424 data_time: 0.0146 memory: 6717 grad_norm: 3.2909 loss: 1.1475 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1475 2023/04/14 10:38:41 - mmengine - INFO - Epoch(train) [70][1660/1879] lr: 2.0000e-03 eta: 5:48:58 time: 0.4196 data_time: 0.0161 memory: 6717 grad_norm: 3.3139 loss: 1.1389 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1389 2023/04/14 10:38:48 - mmengine - INFO - Epoch(train) [70][1680/1879] lr: 2.0000e-03 eta: 5:48:50 time: 0.3275 data_time: 0.0145 memory: 6717 grad_norm: 3.2758 loss: 1.2262 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2262 2023/04/14 10:38:56 - mmengine - INFO - Epoch(train) [70][1700/1879] lr: 2.0000e-03 eta: 5:48:43 time: 0.3951 data_time: 0.0135 memory: 6717 grad_norm: 3.3054 loss: 1.3655 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3655 2023/04/14 10:39:03 - mmengine - INFO - Epoch(train) [70][1720/1879] lr: 2.0000e-03 eta: 5:48:35 time: 0.3458 data_time: 0.0155 memory: 6717 grad_norm: 3.3683 loss: 1.3371 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3371 2023/04/14 10:39:12 - mmengine - INFO - Epoch(train) [70][1740/1879] lr: 2.0000e-03 eta: 5:48:28 time: 0.4412 data_time: 0.0140 memory: 6717 grad_norm: 3.2752 loss: 1.2639 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2639 2023/04/14 10:39:18 - mmengine - INFO - Epoch(train) [70][1760/1879] lr: 2.0000e-03 eta: 5:48:20 time: 0.3066 data_time: 0.0147 memory: 6717 grad_norm: 3.1865 loss: 1.2209 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2209 2023/04/14 10:39:26 - mmengine - INFO - Epoch(train) [70][1780/1879] lr: 2.0000e-03 eta: 5:48:13 time: 0.4003 data_time: 0.0151 memory: 6717 grad_norm: 3.2340 loss: 1.0811 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0811 2023/04/14 10:39:33 - mmengine - INFO - Epoch(train) [70][1800/1879] lr: 2.0000e-03 eta: 5:48:06 time: 0.3471 data_time: 0.0145 memory: 6717 grad_norm: 3.3330 loss: 1.1838 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1838 2023/04/14 10:39:40 - mmengine - INFO - Epoch(train) [70][1820/1879] lr: 2.0000e-03 eta: 5:47:58 time: 0.3852 data_time: 0.0133 memory: 6717 grad_norm: 3.2359 loss: 1.2528 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2528 2023/04/14 10:39:47 - mmengine - INFO - Epoch(train) [70][1840/1879] lr: 2.0000e-03 eta: 5:47:50 time: 0.3160 data_time: 0.0156 memory: 6717 grad_norm: 3.2386 loss: 1.1409 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1409 2023/04/14 10:39:55 - mmengine - INFO - Epoch(train) [70][1860/1879] lr: 2.0000e-03 eta: 5:47:43 time: 0.4021 data_time: 0.0150 memory: 6717 grad_norm: 3.2825 loss: 1.2410 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2410 2023/04/14 10:40:01 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 10:40:01 - mmengine - INFO - Epoch(train) [70][1879/1879] lr: 2.0000e-03 eta: 5:47:36 time: 0.2955 data_time: 0.0138 memory: 6717 grad_norm: 3.3188 loss: 1.1698 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 1.1698 2023/04/14 10:40:10 - mmengine - INFO - Epoch(val) [70][ 20/155] eta: 0:01:01 time: 0.4526 data_time: 0.4195 memory: 1391 2023/04/14 10:40:16 - mmengine - INFO - Epoch(val) [70][ 40/155] eta: 0:00:44 time: 0.3200 data_time: 0.2870 memory: 1391 2023/04/14 10:40:25 - mmengine - INFO - Epoch(val) [70][ 60/155] eta: 0:00:38 time: 0.4355 data_time: 0.4026 memory: 1391 2023/04/14 10:40:31 - mmengine - INFO - Epoch(val) [70][ 80/155] eta: 0:00:28 time: 0.3208 data_time: 0.2879 memory: 1391 2023/04/14 10:40:40 - mmengine - INFO - Epoch(val) [70][100/155] eta: 0:00:21 time: 0.4539 data_time: 0.4207 memory: 1391 2023/04/14 10:40:46 - mmengine - INFO - Epoch(val) [70][120/155] eta: 0:00:13 time: 0.3014 data_time: 0.2680 memory: 1391 2023/04/14 10:40:56 - mmengine - INFO - Epoch(val) [70][140/155] eta: 0:00:05 time: 0.4824 data_time: 0.4495 memory: 1391 2023/04/14 10:41:06 - mmengine - INFO - Epoch(val) [70][155/155] acc/top1: 0.6635 acc/top5: 0.8724 acc/mean1: 0.6634 data_time: 0.4223 time: 0.4544 2023/04/14 10:41:16 - mmengine - INFO - Epoch(train) [71][ 20/1879] lr: 2.0000e-03 eta: 5:47:29 time: 0.5039 data_time: 0.2518 memory: 6717 grad_norm: 3.1873 loss: 0.9980 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9980 2023/04/14 10:41:23 - mmengine - INFO - Epoch(train) [71][ 40/1879] lr: 2.0000e-03 eta: 5:47:22 time: 0.3448 data_time: 0.0638 memory: 6717 grad_norm: 3.2455 loss: 1.2297 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2297 2023/04/14 10:41:31 - mmengine - INFO - Epoch(train) [71][ 60/1879] lr: 2.0000e-03 eta: 5:47:15 time: 0.3879 data_time: 0.0725 memory: 6717 grad_norm: 3.2454 loss: 1.2124 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2124 2023/04/14 10:41:37 - mmengine - INFO - Epoch(train) [71][ 80/1879] lr: 2.0000e-03 eta: 5:47:07 time: 0.3428 data_time: 0.0779 memory: 6717 grad_norm: 3.1997 loss: 1.0745 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0745 2023/04/14 10:41:45 - mmengine - INFO - Epoch(train) [71][ 100/1879] lr: 2.0000e-03 eta: 5:47:00 time: 0.3806 data_time: 0.0186 memory: 6717 grad_norm: 3.3370 loss: 1.3790 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.3790 2023/04/14 10:41:52 - mmengine - INFO - Epoch(train) [71][ 120/1879] lr: 2.0000e-03 eta: 5:46:52 time: 0.3450 data_time: 0.0129 memory: 6717 grad_norm: 3.2483 loss: 1.0808 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0808 2023/04/14 10:42:00 - mmengine - INFO - Epoch(train) [71][ 140/1879] lr: 2.0000e-03 eta: 5:46:45 time: 0.4210 data_time: 0.0159 memory: 6717 grad_norm: 3.2955 loss: 1.1136 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1136 2023/04/14 10:42:07 - mmengine - INFO - Epoch(train) [71][ 160/1879] lr: 2.0000e-03 eta: 5:46:37 time: 0.3300 data_time: 0.0138 memory: 6717 grad_norm: 3.3495 loss: 1.0814 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0814 2023/04/14 10:42:15 - mmengine - INFO - Epoch(train) [71][ 180/1879] lr: 2.0000e-03 eta: 5:46:30 time: 0.4256 data_time: 0.0160 memory: 6717 grad_norm: 3.2879 loss: 1.1788 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1788 2023/04/14 10:42:22 - mmengine - INFO - Epoch(train) [71][ 200/1879] lr: 2.0000e-03 eta: 5:46:23 time: 0.3273 data_time: 0.0128 memory: 6717 grad_norm: 3.3377 loss: 1.1731 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1731 2023/04/14 10:42:30 - mmengine - INFO - Epoch(train) [71][ 220/1879] lr: 2.0000e-03 eta: 5:46:15 time: 0.3994 data_time: 0.0207 memory: 6717 grad_norm: 3.2520 loss: 1.0734 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0734 2023/04/14 10:42:37 - mmengine - INFO - Epoch(train) [71][ 240/1879] lr: 2.0000e-03 eta: 5:46:08 time: 0.3360 data_time: 0.0125 memory: 6717 grad_norm: 3.3395 loss: 1.1391 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1391 2023/04/14 10:42:45 - mmengine - INFO - Epoch(train) [71][ 260/1879] lr: 2.0000e-03 eta: 5:46:01 time: 0.4116 data_time: 0.0157 memory: 6717 grad_norm: 3.2841 loss: 1.2705 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2705 2023/04/14 10:42:51 - mmengine - INFO - Epoch(train) [71][ 280/1879] lr: 2.0000e-03 eta: 5:45:53 time: 0.3105 data_time: 0.0142 memory: 6717 grad_norm: 3.2641 loss: 1.1005 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1005 2023/04/14 10:42:59 - mmengine - INFO - Epoch(train) [71][ 300/1879] lr: 2.0000e-03 eta: 5:45:46 time: 0.4047 data_time: 0.1237 memory: 6717 grad_norm: 3.2752 loss: 1.2054 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2054 2023/04/14 10:43:07 - mmengine - INFO - Epoch(train) [71][ 320/1879] lr: 2.0000e-03 eta: 5:45:38 time: 0.3779 data_time: 0.0795 memory: 6717 grad_norm: 3.1876 loss: 1.0517 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0517 2023/04/14 10:43:14 - mmengine - INFO - Epoch(train) [71][ 340/1879] lr: 2.0000e-03 eta: 5:45:31 time: 0.3486 data_time: 0.0254 memory: 6717 grad_norm: 3.2809 loss: 1.1846 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1846 2023/04/14 10:43:21 - mmengine - INFO - Epoch(train) [71][ 360/1879] lr: 2.0000e-03 eta: 5:45:24 time: 0.3831 data_time: 0.0146 memory: 6717 grad_norm: 3.2842 loss: 1.2865 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2865 2023/04/14 10:43:28 - mmengine - INFO - Epoch(train) [71][ 380/1879] lr: 2.0000e-03 eta: 5:45:16 time: 0.3176 data_time: 0.0422 memory: 6717 grad_norm: 3.3385 loss: 1.3875 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3875 2023/04/14 10:43:36 - mmengine - INFO - Epoch(train) [71][ 400/1879] lr: 2.0000e-03 eta: 5:45:08 time: 0.3832 data_time: 0.0376 memory: 6717 grad_norm: 3.2918 loss: 1.2725 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2725 2023/04/14 10:43:42 - mmengine - INFO - Epoch(train) [71][ 420/1879] lr: 2.0000e-03 eta: 5:45:01 time: 0.3471 data_time: 0.0694 memory: 6717 grad_norm: 3.2202 loss: 1.2993 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.2993 2023/04/14 10:43:50 - mmengine - INFO - Epoch(train) [71][ 440/1879] lr: 2.0000e-03 eta: 5:44:53 time: 0.3630 data_time: 0.0223 memory: 6717 grad_norm: 3.2177 loss: 1.2075 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.2075 2023/04/14 10:43:57 - mmengine - INFO - Epoch(train) [71][ 460/1879] lr: 2.0000e-03 eta: 5:44:46 time: 0.3733 data_time: 0.0802 memory: 6717 grad_norm: 3.2262 loss: 1.0255 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0255 2023/04/14 10:44:01 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 10:44:05 - mmengine - INFO - Epoch(train) [71][ 480/1879] lr: 2.0000e-03 eta: 5:44:39 time: 0.3719 data_time: 0.0962 memory: 6717 grad_norm: 3.3021 loss: 1.2682 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2682 2023/04/14 10:44:13 - mmengine - INFO - Epoch(train) [71][ 500/1879] lr: 2.0000e-03 eta: 5:44:31 time: 0.3961 data_time: 0.1011 memory: 6717 grad_norm: 3.2824 loss: 1.0317 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0317 2023/04/14 10:44:19 - mmengine - INFO - Epoch(train) [71][ 520/1879] lr: 2.0000e-03 eta: 5:44:24 time: 0.3208 data_time: 0.0974 memory: 6717 grad_norm: 3.3136 loss: 1.3350 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3350 2023/04/14 10:44:27 - mmengine - INFO - Epoch(train) [71][ 540/1879] lr: 2.0000e-03 eta: 5:44:17 time: 0.4234 data_time: 0.1109 memory: 6717 grad_norm: 3.3284 loss: 1.1933 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1933 2023/04/14 10:44:34 - mmengine - INFO - Epoch(train) [71][ 560/1879] lr: 2.0000e-03 eta: 5:44:09 time: 0.3333 data_time: 0.0446 memory: 6717 grad_norm: 3.2368 loss: 1.1987 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1987 2023/04/14 10:44:42 - mmengine - INFO - Epoch(train) [71][ 580/1879] lr: 2.0000e-03 eta: 5:44:02 time: 0.3997 data_time: 0.0135 memory: 6717 grad_norm: 3.3215 loss: 1.1195 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1195 2023/04/14 10:44:49 - mmengine - INFO - Epoch(train) [71][ 600/1879] lr: 2.0000e-03 eta: 5:43:54 time: 0.3504 data_time: 0.0146 memory: 6717 grad_norm: 3.2977 loss: 1.3042 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3042 2023/04/14 10:44:57 - mmengine - INFO - Epoch(train) [71][ 620/1879] lr: 2.0000e-03 eta: 5:43:47 time: 0.4076 data_time: 0.0141 memory: 6717 grad_norm: 3.2426 loss: 1.3201 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.3201 2023/04/14 10:45:04 - mmengine - INFO - Epoch(train) [71][ 640/1879] lr: 2.0000e-03 eta: 5:43:39 time: 0.3216 data_time: 0.0165 memory: 6717 grad_norm: 3.2592 loss: 1.1938 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1938 2023/04/14 10:45:11 - mmengine - INFO - Epoch(train) [71][ 660/1879] lr: 2.0000e-03 eta: 5:43:32 time: 0.3621 data_time: 0.0130 memory: 6717 grad_norm: 3.2564 loss: 1.0752 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0752 2023/04/14 10:45:18 - mmengine - INFO - Epoch(train) [71][ 680/1879] lr: 2.0000e-03 eta: 5:43:24 time: 0.3719 data_time: 0.0278 memory: 6717 grad_norm: 3.2992 loss: 1.2077 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2077 2023/04/14 10:45:26 - mmengine - INFO - Epoch(train) [71][ 700/1879] lr: 2.0000e-03 eta: 5:43:17 time: 0.3964 data_time: 0.0130 memory: 6717 grad_norm: 3.2175 loss: 1.2152 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2152 2023/04/14 10:45:33 - mmengine - INFO - Epoch(train) [71][ 720/1879] lr: 2.0000e-03 eta: 5:43:10 time: 0.3359 data_time: 0.0156 memory: 6717 grad_norm: 3.2949 loss: 1.0924 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0924 2023/04/14 10:45:41 - mmengine - INFO - Epoch(train) [71][ 740/1879] lr: 2.0000e-03 eta: 5:43:02 time: 0.3757 data_time: 0.0142 memory: 6717 grad_norm: 3.2181 loss: 1.1034 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1034 2023/04/14 10:45:48 - mmengine - INFO - Epoch(train) [71][ 760/1879] lr: 2.0000e-03 eta: 5:42:55 time: 0.3593 data_time: 0.0158 memory: 6717 grad_norm: 3.2685 loss: 1.0313 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0313 2023/04/14 10:45:55 - mmengine - INFO - Epoch(train) [71][ 780/1879] lr: 2.0000e-03 eta: 5:42:47 time: 0.3789 data_time: 0.0132 memory: 6717 grad_norm: 3.3173 loss: 1.1972 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1972 2023/04/14 10:46:02 - mmengine - INFO - Epoch(train) [71][ 800/1879] lr: 2.0000e-03 eta: 5:42:40 time: 0.3457 data_time: 0.0154 memory: 6717 grad_norm: 3.2566 loss: 1.1641 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1641 2023/04/14 10:46:10 - mmengine - INFO - Epoch(train) [71][ 820/1879] lr: 2.0000e-03 eta: 5:42:33 time: 0.3857 data_time: 0.0151 memory: 6717 grad_norm: 3.2621 loss: 1.1472 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.1472 2023/04/14 10:46:17 - mmengine - INFO - Epoch(train) [71][ 840/1879] lr: 2.0000e-03 eta: 5:42:25 time: 0.3363 data_time: 0.0158 memory: 6717 grad_norm: 3.3100 loss: 1.2326 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2326 2023/04/14 10:46:24 - mmengine - INFO - Epoch(train) [71][ 860/1879] lr: 2.0000e-03 eta: 5:42:17 time: 0.3582 data_time: 0.0136 memory: 6717 grad_norm: 3.2773 loss: 1.2605 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2605 2023/04/14 10:46:31 - mmengine - INFO - Epoch(train) [71][ 880/1879] lr: 2.0000e-03 eta: 5:42:10 time: 0.3635 data_time: 0.0702 memory: 6717 grad_norm: 3.2910 loss: 1.2906 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2906 2023/04/14 10:46:38 - mmengine - INFO - Epoch(train) [71][ 900/1879] lr: 2.0000e-03 eta: 5:42:02 time: 0.3605 data_time: 0.0515 memory: 6717 grad_norm: 3.2389 loss: 1.2103 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2103 2023/04/14 10:46:47 - mmengine - INFO - Epoch(train) [71][ 920/1879] lr: 2.0000e-03 eta: 5:41:55 time: 0.4091 data_time: 0.0517 memory: 6717 grad_norm: 3.3315 loss: 1.1295 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1295 2023/04/14 10:46:53 - mmengine - INFO - Epoch(train) [71][ 940/1879] lr: 2.0000e-03 eta: 5:41:48 time: 0.3389 data_time: 0.0486 memory: 6717 grad_norm: 3.2989 loss: 1.1921 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1921 2023/04/14 10:47:02 - mmengine - INFO - Epoch(train) [71][ 960/1879] lr: 2.0000e-03 eta: 5:41:41 time: 0.4262 data_time: 0.0702 memory: 6717 grad_norm: 3.2849 loss: 1.3291 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3291 2023/04/14 10:47:08 - mmengine - INFO - Epoch(train) [71][ 980/1879] lr: 2.0000e-03 eta: 5:41:33 time: 0.3288 data_time: 0.0515 memory: 6717 grad_norm: 3.2247 loss: 1.1017 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1017 2023/04/14 10:47:17 - mmengine - INFO - Epoch(train) [71][1000/1879] lr: 2.0000e-03 eta: 5:41:26 time: 0.4029 data_time: 0.1208 memory: 6717 grad_norm: 3.3503 loss: 1.1891 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1891 2023/04/14 10:47:23 - mmengine - INFO - Epoch(train) [71][1020/1879] lr: 2.0000e-03 eta: 5:41:18 time: 0.3175 data_time: 0.0262 memory: 6717 grad_norm: 3.3553 loss: 1.3635 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.3635 2023/04/14 10:47:32 - mmengine - INFO - Epoch(train) [71][1040/1879] lr: 2.0000e-03 eta: 5:41:11 time: 0.4439 data_time: 0.0168 memory: 6717 grad_norm: 3.1874 loss: 1.2300 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2300 2023/04/14 10:47:38 - mmengine - INFO - Epoch(train) [71][1060/1879] lr: 2.0000e-03 eta: 5:41:03 time: 0.3116 data_time: 0.0123 memory: 6717 grad_norm: 3.2536 loss: 1.2030 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2030 2023/04/14 10:47:46 - mmengine - INFO - Epoch(train) [71][1080/1879] lr: 2.0000e-03 eta: 5:40:56 time: 0.4168 data_time: 0.0157 memory: 6717 grad_norm: 3.3784 loss: 1.1786 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1786 2023/04/14 10:47:53 - mmengine - INFO - Epoch(train) [71][1100/1879] lr: 2.0000e-03 eta: 5:40:49 time: 0.3196 data_time: 0.0132 memory: 6717 grad_norm: 3.3368 loss: 1.1678 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1678 2023/04/14 10:48:00 - mmengine - INFO - Epoch(train) [71][1120/1879] lr: 2.0000e-03 eta: 5:40:41 time: 0.3807 data_time: 0.0154 memory: 6717 grad_norm: 3.2433 loss: 1.1560 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1560 2023/04/14 10:48:08 - mmengine - INFO - Epoch(train) [71][1140/1879] lr: 2.0000e-03 eta: 5:40:34 time: 0.3885 data_time: 0.0131 memory: 6717 grad_norm: 3.3018 loss: 1.0780 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0780 2023/04/14 10:48:15 - mmengine - INFO - Epoch(train) [71][1160/1879] lr: 2.0000e-03 eta: 5:40:26 time: 0.3442 data_time: 0.0154 memory: 6717 grad_norm: 3.2804 loss: 1.2584 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2584 2023/04/14 10:48:23 - mmengine - INFO - Epoch(train) [71][1180/1879] lr: 2.0000e-03 eta: 5:40:19 time: 0.4107 data_time: 0.0136 memory: 6717 grad_norm: 3.3303 loss: 1.2886 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2886 2023/04/14 10:48:30 - mmengine - INFO - Epoch(train) [71][1200/1879] lr: 2.0000e-03 eta: 5:40:12 time: 0.3308 data_time: 0.0156 memory: 6717 grad_norm: 3.1929 loss: 1.1401 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1401 2023/04/14 10:48:37 - mmengine - INFO - Epoch(train) [71][1220/1879] lr: 2.0000e-03 eta: 5:40:04 time: 0.3731 data_time: 0.0143 memory: 6717 grad_norm: 3.2495 loss: 1.1234 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1234 2023/04/14 10:48:44 - mmengine - INFO - Epoch(train) [71][1240/1879] lr: 2.0000e-03 eta: 5:39:57 time: 0.3555 data_time: 0.0259 memory: 6717 grad_norm: 3.2615 loss: 1.2238 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2238 2023/04/14 10:48:52 - mmengine - INFO - Epoch(train) [71][1260/1879] lr: 2.0000e-03 eta: 5:39:49 time: 0.3514 data_time: 0.0470 memory: 6717 grad_norm: 3.2345 loss: 1.1428 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1428 2023/04/14 10:48:59 - mmengine - INFO - Epoch(train) [71][1280/1879] lr: 2.0000e-03 eta: 5:39:42 time: 0.3620 data_time: 0.1246 memory: 6717 grad_norm: 3.3247 loss: 1.0307 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0307 2023/04/14 10:49:06 - mmengine - INFO - Epoch(train) [71][1300/1879] lr: 2.0000e-03 eta: 5:39:34 time: 0.3666 data_time: 0.0311 memory: 6717 grad_norm: 3.2695 loss: 1.0880 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0880 2023/04/14 10:49:14 - mmengine - INFO - Epoch(train) [71][1320/1879] lr: 2.0000e-03 eta: 5:39:27 time: 0.3793 data_time: 0.0153 memory: 6717 grad_norm: 3.2093 loss: 1.2030 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2030 2023/04/14 10:49:20 - mmengine - INFO - Epoch(train) [71][1340/1879] lr: 2.0000e-03 eta: 5:39:19 time: 0.3232 data_time: 0.0132 memory: 6717 grad_norm: 3.3592 loss: 1.2862 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2862 2023/04/14 10:49:28 - mmengine - INFO - Epoch(train) [71][1360/1879] lr: 2.0000e-03 eta: 5:39:12 time: 0.3918 data_time: 0.0160 memory: 6717 grad_norm: 3.2677 loss: 1.2817 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2817 2023/04/14 10:49:34 - mmengine - INFO - Epoch(train) [71][1380/1879] lr: 2.0000e-03 eta: 5:39:04 time: 0.3204 data_time: 0.0147 memory: 6717 grad_norm: 3.3111 loss: 1.0845 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0845 2023/04/14 10:49:43 - mmengine - INFO - Epoch(train) [71][1400/1879] lr: 2.0000e-03 eta: 5:38:57 time: 0.4146 data_time: 0.0158 memory: 6717 grad_norm: 3.2301 loss: 1.1222 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1222 2023/04/14 10:49:50 - mmengine - INFO - Epoch(train) [71][1420/1879] lr: 2.0000e-03 eta: 5:38:50 time: 0.3502 data_time: 0.0137 memory: 6717 grad_norm: 3.2761 loss: 1.0228 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0228 2023/04/14 10:49:58 - mmengine - INFO - Epoch(train) [71][1440/1879] lr: 2.0000e-03 eta: 5:38:43 time: 0.4122 data_time: 0.0165 memory: 6717 grad_norm: 3.3180 loss: 1.1580 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1580 2023/04/14 10:50:05 - mmengine - INFO - Epoch(train) [71][1460/1879] lr: 2.0000e-03 eta: 5:38:35 time: 0.3307 data_time: 0.0156 memory: 6717 grad_norm: 3.2819 loss: 1.2024 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2024 2023/04/14 10:50:08 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 10:50:12 - mmengine - INFO - Epoch(train) [71][1480/1879] lr: 2.0000e-03 eta: 5:38:27 time: 0.3665 data_time: 0.0154 memory: 6717 grad_norm: 3.2542 loss: 1.3830 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.3830 2023/04/14 10:50:19 - mmengine - INFO - Epoch(train) [71][1500/1879] lr: 2.0000e-03 eta: 5:38:20 time: 0.3303 data_time: 0.0153 memory: 6717 grad_norm: 3.1882 loss: 1.1363 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1363 2023/04/14 10:50:26 - mmengine - INFO - Epoch(train) [71][1520/1879] lr: 2.0000e-03 eta: 5:38:12 time: 0.3833 data_time: 0.0157 memory: 6717 grad_norm: 3.3229 loss: 1.1456 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1456 2023/04/14 10:50:33 - mmengine - INFO - Epoch(train) [71][1540/1879] lr: 2.0000e-03 eta: 5:38:05 time: 0.3338 data_time: 0.0138 memory: 6717 grad_norm: 3.3186 loss: 1.1657 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1657 2023/04/14 10:50:41 - mmengine - INFO - Epoch(train) [71][1560/1879] lr: 2.0000e-03 eta: 5:37:57 time: 0.3972 data_time: 0.0255 memory: 6717 grad_norm: 3.3605 loss: 1.1513 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1513 2023/04/14 10:50:47 - mmengine - INFO - Epoch(train) [71][1580/1879] lr: 2.0000e-03 eta: 5:37:50 time: 0.3295 data_time: 0.0202 memory: 6717 grad_norm: 3.3098 loss: 1.3120 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3120 2023/04/14 10:50:56 - mmengine - INFO - Epoch(train) [71][1600/1879] lr: 2.0000e-03 eta: 5:37:43 time: 0.4099 data_time: 0.0472 memory: 6717 grad_norm: 3.2342 loss: 1.1542 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1542 2023/04/14 10:51:02 - mmengine - INFO - Epoch(train) [71][1620/1879] lr: 2.0000e-03 eta: 5:37:35 time: 0.3070 data_time: 0.0306 memory: 6717 grad_norm: 3.2460 loss: 1.1178 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1178 2023/04/14 10:51:10 - mmengine - INFO - Epoch(train) [71][1640/1879] lr: 2.0000e-03 eta: 5:37:28 time: 0.4351 data_time: 0.0423 memory: 6717 grad_norm: 3.3954 loss: 1.2265 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2265 2023/04/14 10:51:17 - mmengine - INFO - Epoch(train) [71][1660/1879] lr: 2.0000e-03 eta: 5:37:20 time: 0.3326 data_time: 0.0559 memory: 6717 grad_norm: 3.2417 loss: 1.1536 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.1536 2023/04/14 10:51:26 - mmengine - INFO - Epoch(train) [71][1680/1879] lr: 2.0000e-03 eta: 5:37:13 time: 0.4212 data_time: 0.0810 memory: 6717 grad_norm: 3.3830 loss: 1.2091 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2091 2023/04/14 10:51:32 - mmengine - INFO - Epoch(train) [71][1700/1879] lr: 2.0000e-03 eta: 5:37:05 time: 0.3130 data_time: 0.0871 memory: 6717 grad_norm: 3.2955 loss: 1.1588 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1588 2023/04/14 10:51:40 - mmengine - INFO - Epoch(train) [71][1720/1879] lr: 2.0000e-03 eta: 5:36:58 time: 0.3887 data_time: 0.1703 memory: 6717 grad_norm: 3.2548 loss: 1.1716 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1716 2023/04/14 10:51:46 - mmengine - INFO - Epoch(train) [71][1740/1879] lr: 2.0000e-03 eta: 5:36:50 time: 0.3255 data_time: 0.1366 memory: 6717 grad_norm: 3.1851 loss: 1.2715 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2715 2023/04/14 10:51:54 - mmengine - INFO - Epoch(train) [71][1760/1879] lr: 2.0000e-03 eta: 5:36:43 time: 0.4048 data_time: 0.0553 memory: 6717 grad_norm: 3.3192 loss: 1.1578 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1578 2023/04/14 10:52:01 - mmengine - INFO - Epoch(train) [71][1780/1879] lr: 2.0000e-03 eta: 5:36:36 time: 0.3627 data_time: 0.0145 memory: 6717 grad_norm: 3.2842 loss: 1.2343 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2343 2023/04/14 10:52:10 - mmengine - INFO - Epoch(train) [71][1800/1879] lr: 2.0000e-03 eta: 5:36:29 time: 0.4286 data_time: 0.0132 memory: 6717 grad_norm: 3.3060 loss: 1.3348 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3348 2023/04/14 10:52:16 - mmengine - INFO - Epoch(train) [71][1820/1879] lr: 2.0000e-03 eta: 5:36:21 time: 0.3012 data_time: 0.0162 memory: 6717 grad_norm: 3.2318 loss: 0.9891 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9891 2023/04/14 10:52:25 - mmengine - INFO - Epoch(train) [71][1840/1879] lr: 2.0000e-03 eta: 5:36:14 time: 0.4213 data_time: 0.0199 memory: 6717 grad_norm: 3.3263 loss: 1.2167 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2167 2023/04/14 10:52:30 - mmengine - INFO - Epoch(train) [71][1860/1879] lr: 2.0000e-03 eta: 5:36:06 time: 0.2983 data_time: 0.0138 memory: 6717 grad_norm: 3.2842 loss: 1.1915 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1915 2023/04/14 10:52:37 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 10:52:37 - mmengine - INFO - Epoch(train) [71][1879/1879] lr: 2.0000e-03 eta: 5:35:59 time: 0.3460 data_time: 0.0131 memory: 6717 grad_norm: 3.2852 loss: 1.1258 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 1.1258 2023/04/14 10:52:46 - mmengine - INFO - Epoch(val) [71][ 20/155] eta: 0:01:02 time: 0.4607 data_time: 0.4272 memory: 1391 2023/04/14 10:52:53 - mmengine - INFO - Epoch(val) [71][ 40/155] eta: 0:00:44 time: 0.3069 data_time: 0.2743 memory: 1391 2023/04/14 10:53:01 - mmengine - INFO - Epoch(val) [71][ 60/155] eta: 0:00:38 time: 0.4363 data_time: 0.4030 memory: 1391 2023/04/14 10:53:08 - mmengine - INFO - Epoch(val) [71][ 80/155] eta: 0:00:28 time: 0.3150 data_time: 0.2823 memory: 1391 2023/04/14 10:53:16 - mmengine - INFO - Epoch(val) [71][100/155] eta: 0:00:21 time: 0.4269 data_time: 0.3942 memory: 1391 2023/04/14 10:53:23 - mmengine - INFO - Epoch(val) [71][120/155] eta: 0:00:13 time: 0.3359 data_time: 0.3027 memory: 1391 2023/04/14 10:53:33 - mmengine - INFO - Epoch(val) [71][140/155] eta: 0:00:05 time: 0.4847 data_time: 0.4516 memory: 1391 2023/04/14 10:53:40 - mmengine - INFO - Epoch(val) [71][155/155] acc/top1: 0.6626 acc/top5: 0.8727 acc/mean1: 0.6625 data_time: 0.4219 time: 0.4542 2023/04/14 10:53:49 - mmengine - INFO - Epoch(train) [72][ 20/1879] lr: 2.0000e-03 eta: 5:35:52 time: 0.4800 data_time: 0.3197 memory: 6717 grad_norm: 3.1868 loss: 1.0631 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0631 2023/04/14 10:53:56 - mmengine - INFO - Epoch(train) [72][ 40/1879] lr: 2.0000e-03 eta: 5:35:45 time: 0.3401 data_time: 0.1357 memory: 6717 grad_norm: 3.3831 loss: 1.1614 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1614 2023/04/14 10:54:04 - mmengine - INFO - Epoch(train) [72][ 60/1879] lr: 2.0000e-03 eta: 5:35:37 time: 0.3988 data_time: 0.0995 memory: 6717 grad_norm: 3.2690 loss: 1.0579 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0579 2023/04/14 10:54:11 - mmengine - INFO - Epoch(train) [72][ 80/1879] lr: 2.0000e-03 eta: 5:35:30 time: 0.3696 data_time: 0.0152 memory: 6717 grad_norm: 3.3481 loss: 1.1998 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1998 2023/04/14 10:54:19 - mmengine - INFO - Epoch(train) [72][ 100/1879] lr: 2.0000e-03 eta: 5:35:23 time: 0.3805 data_time: 0.0137 memory: 6717 grad_norm: 3.2992 loss: 1.2999 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2999 2023/04/14 10:54:25 - mmengine - INFO - Epoch(train) [72][ 120/1879] lr: 2.0000e-03 eta: 5:35:15 time: 0.3116 data_time: 0.0156 memory: 6717 grad_norm: 3.1789 loss: 0.9217 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9217 2023/04/14 10:54:34 - mmengine - INFO - Epoch(train) [72][ 140/1879] lr: 2.0000e-03 eta: 5:35:08 time: 0.4297 data_time: 0.0135 memory: 6717 grad_norm: 3.2695 loss: 1.0773 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0773 2023/04/14 10:54:40 - mmengine - INFO - Epoch(train) [72][ 160/1879] lr: 2.0000e-03 eta: 5:35:00 time: 0.3223 data_time: 0.0143 memory: 6717 grad_norm: 3.3061 loss: 1.2470 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2470 2023/04/14 10:54:49 - mmengine - INFO - Epoch(train) [72][ 180/1879] lr: 2.0000e-03 eta: 5:34:53 time: 0.4354 data_time: 0.0140 memory: 6717 grad_norm: 3.2776 loss: 1.0218 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0218 2023/04/14 10:54:55 - mmengine - INFO - Epoch(train) [72][ 200/1879] lr: 2.0000e-03 eta: 5:34:45 time: 0.3184 data_time: 0.0138 memory: 6717 grad_norm: 3.3088 loss: 1.1999 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1999 2023/04/14 10:55:04 - mmengine - INFO - Epoch(train) [72][ 220/1879] lr: 2.0000e-03 eta: 5:34:39 time: 0.4233 data_time: 0.0144 memory: 6717 grad_norm: 3.2311 loss: 1.2351 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2351 2023/04/14 10:55:10 - mmengine - INFO - Epoch(train) [72][ 240/1879] lr: 2.0000e-03 eta: 5:34:31 time: 0.2987 data_time: 0.0132 memory: 6717 grad_norm: 3.4202 loss: 1.1831 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1831 2023/04/14 10:55:18 - mmengine - INFO - Epoch(train) [72][ 260/1879] lr: 2.0000e-03 eta: 5:34:23 time: 0.4052 data_time: 0.0149 memory: 6717 grad_norm: 3.2801 loss: 1.1030 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1030 2023/04/14 10:55:24 - mmengine - INFO - Epoch(train) [72][ 280/1879] lr: 2.0000e-03 eta: 5:34:16 time: 0.3128 data_time: 0.0131 memory: 6717 grad_norm: 3.2844 loss: 1.1925 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1925 2023/04/14 10:55:33 - mmengine - INFO - Epoch(train) [72][ 300/1879] lr: 2.0000e-03 eta: 5:34:09 time: 0.4334 data_time: 0.0147 memory: 6717 grad_norm: 3.3240 loss: 1.1511 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1511 2023/04/14 10:55:40 - mmengine - INFO - Epoch(train) [72][ 320/1879] lr: 2.0000e-03 eta: 5:34:01 time: 0.3362 data_time: 0.0134 memory: 6717 grad_norm: 3.3653 loss: 1.3060 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3060 2023/04/14 10:55:48 - mmengine - INFO - Epoch(train) [72][ 340/1879] lr: 2.0000e-03 eta: 5:33:54 time: 0.4053 data_time: 0.0145 memory: 6717 grad_norm: 3.3378 loss: 1.1680 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1680 2023/04/14 10:55:54 - mmengine - INFO - Epoch(train) [72][ 360/1879] lr: 2.0000e-03 eta: 5:33:46 time: 0.3230 data_time: 0.0136 memory: 6717 grad_norm: 3.3009 loss: 1.3145 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3145 2023/04/14 10:56:03 - mmengine - INFO - Epoch(train) [72][ 380/1879] lr: 2.0000e-03 eta: 5:33:39 time: 0.4590 data_time: 0.0138 memory: 6717 grad_norm: 3.3904 loss: 1.2970 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2970 2023/04/14 10:56:10 - mmengine - INFO - Epoch(train) [72][ 400/1879] lr: 2.0000e-03 eta: 5:33:32 time: 0.3075 data_time: 0.0175 memory: 6717 grad_norm: 3.3045 loss: 1.2158 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2158 2023/04/14 10:56:17 - mmengine - INFO - Epoch(train) [72][ 420/1879] lr: 2.0000e-03 eta: 5:33:24 time: 0.3869 data_time: 0.0145 memory: 6717 grad_norm: 3.2543 loss: 1.0524 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0524 2023/04/14 10:56:24 - mmengine - INFO - Epoch(train) [72][ 440/1879] lr: 2.0000e-03 eta: 5:33:17 time: 0.3400 data_time: 0.0146 memory: 6717 grad_norm: 3.3035 loss: 1.2742 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2742 2023/04/14 10:56:33 - mmengine - INFO - Epoch(train) [72][ 460/1879] lr: 2.0000e-03 eta: 5:33:10 time: 0.4254 data_time: 0.0135 memory: 6717 grad_norm: 3.2825 loss: 1.1363 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1363 2023/04/14 10:56:40 - mmengine - INFO - Epoch(train) [72][ 480/1879] lr: 2.0000e-03 eta: 5:33:02 time: 0.3428 data_time: 0.0135 memory: 6717 grad_norm: 3.3069 loss: 1.1138 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1138 2023/04/14 10:56:48 - mmengine - INFO - Epoch(train) [72][ 500/1879] lr: 2.0000e-03 eta: 5:32:55 time: 0.4019 data_time: 0.0143 memory: 6717 grad_norm: 3.3221 loss: 1.2168 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2168 2023/04/14 10:56:54 - mmengine - INFO - Epoch(train) [72][ 520/1879] lr: 2.0000e-03 eta: 5:32:47 time: 0.3152 data_time: 0.0141 memory: 6717 grad_norm: 3.2716 loss: 1.3400 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3400 2023/04/14 10:57:02 - mmengine - INFO - Epoch(train) [72][ 540/1879] lr: 2.0000e-03 eta: 5:32:40 time: 0.3945 data_time: 0.0143 memory: 6717 grad_norm: 3.2398 loss: 1.0657 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0657 2023/04/14 10:57:08 - mmengine - INFO - Epoch(train) [72][ 560/1879] lr: 2.0000e-03 eta: 5:32:32 time: 0.3231 data_time: 0.0146 memory: 6717 grad_norm: 3.2452 loss: 1.2641 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2641 2023/04/14 10:57:16 - mmengine - INFO - Epoch(train) [72][ 580/1879] lr: 2.0000e-03 eta: 5:32:25 time: 0.3908 data_time: 0.0140 memory: 6717 grad_norm: 3.2505 loss: 1.2245 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2245 2023/04/14 10:57:19 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 10:57:23 - mmengine - INFO - Epoch(train) [72][ 600/1879] lr: 2.0000e-03 eta: 5:32:17 time: 0.3443 data_time: 0.0340 memory: 6717 grad_norm: 3.3007 loss: 1.1216 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1216 2023/04/14 10:57:31 - mmengine - INFO - Epoch(train) [72][ 620/1879] lr: 2.0000e-03 eta: 5:32:10 time: 0.3818 data_time: 0.0810 memory: 6717 grad_norm: 3.2762 loss: 1.2558 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2558 2023/04/14 10:57:38 - mmengine - INFO - Epoch(train) [72][ 640/1879] lr: 2.0000e-03 eta: 5:32:03 time: 0.3921 data_time: 0.0577 memory: 6717 grad_norm: 3.2049 loss: 1.2418 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2418 2023/04/14 10:57:45 - mmengine - INFO - Epoch(train) [72][ 660/1879] lr: 2.0000e-03 eta: 5:31:55 time: 0.3124 data_time: 0.0488 memory: 6717 grad_norm: 3.2991 loss: 1.1403 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.1403 2023/04/14 10:57:53 - mmengine - INFO - Epoch(train) [72][ 680/1879] lr: 2.0000e-03 eta: 5:31:48 time: 0.4104 data_time: 0.1005 memory: 6717 grad_norm: 3.2276 loss: 1.2256 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2256 2023/04/14 10:58:00 - mmengine - INFO - Epoch(train) [72][ 700/1879] lr: 2.0000e-03 eta: 5:31:40 time: 0.3462 data_time: 0.0129 memory: 6717 grad_norm: 3.2390 loss: 1.1265 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1265 2023/04/14 10:58:08 - mmengine - INFO - Epoch(train) [72][ 720/1879] lr: 2.0000e-03 eta: 5:31:33 time: 0.3998 data_time: 0.0151 memory: 6717 grad_norm: 3.4432 loss: 1.1423 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1423 2023/04/14 10:58:15 - mmengine - INFO - Epoch(train) [72][ 740/1879] lr: 2.0000e-03 eta: 5:31:26 time: 0.3727 data_time: 0.0133 memory: 6717 grad_norm: 3.2643 loss: 1.2154 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2154 2023/04/14 10:58:21 - mmengine - INFO - Epoch(train) [72][ 760/1879] lr: 2.0000e-03 eta: 5:31:18 time: 0.3049 data_time: 0.0160 memory: 6717 grad_norm: 3.2742 loss: 1.1625 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1625 2023/04/14 10:58:29 - mmengine - INFO - Epoch(train) [72][ 780/1879] lr: 2.0000e-03 eta: 5:31:11 time: 0.3969 data_time: 0.0146 memory: 6717 grad_norm: 3.3572 loss: 1.2230 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2230 2023/04/14 10:58:36 - mmengine - INFO - Epoch(train) [72][ 800/1879] lr: 2.0000e-03 eta: 5:31:03 time: 0.3212 data_time: 0.0153 memory: 6717 grad_norm: 3.2795 loss: 1.0979 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0979 2023/04/14 10:58:44 - mmengine - INFO - Epoch(train) [72][ 820/1879] lr: 2.0000e-03 eta: 5:30:56 time: 0.4029 data_time: 0.0218 memory: 6717 grad_norm: 3.3096 loss: 1.1693 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1693 2023/04/14 10:58:50 - mmengine - INFO - Epoch(train) [72][ 840/1879] lr: 2.0000e-03 eta: 5:30:48 time: 0.3330 data_time: 0.0143 memory: 6717 grad_norm: 3.3306 loss: 1.2822 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2822 2023/04/14 10:58:59 - mmengine - INFO - Epoch(train) [72][ 860/1879] lr: 2.0000e-03 eta: 5:30:41 time: 0.4069 data_time: 0.0147 memory: 6717 grad_norm: 3.2729 loss: 1.2711 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2711 2023/04/14 10:59:05 - mmengine - INFO - Epoch(train) [72][ 880/1879] lr: 2.0000e-03 eta: 5:30:33 time: 0.3240 data_time: 0.0143 memory: 6717 grad_norm: 3.3258 loss: 1.2069 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2069 2023/04/14 10:59:14 - mmengine - INFO - Epoch(train) [72][ 900/1879] lr: 2.0000e-03 eta: 5:30:26 time: 0.4662 data_time: 0.0142 memory: 6717 grad_norm: 3.2575 loss: 1.1588 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1588 2023/04/14 10:59:20 - mmengine - INFO - Epoch(train) [72][ 920/1879] lr: 2.0000e-03 eta: 5:30:18 time: 0.3016 data_time: 0.0144 memory: 6717 grad_norm: 3.2869 loss: 1.3322 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3322 2023/04/14 10:59:29 - mmengine - INFO - Epoch(train) [72][ 940/1879] lr: 2.0000e-03 eta: 5:30:11 time: 0.4014 data_time: 0.0139 memory: 6717 grad_norm: 3.3100 loss: 1.0118 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0118 2023/04/14 10:59:35 - mmengine - INFO - Epoch(train) [72][ 960/1879] lr: 2.0000e-03 eta: 5:30:04 time: 0.3377 data_time: 0.0150 memory: 6717 grad_norm: 3.3581 loss: 1.1568 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1568 2023/04/14 10:59:44 - mmengine - INFO - Epoch(train) [72][ 980/1879] lr: 2.0000e-03 eta: 5:29:57 time: 0.4341 data_time: 0.0136 memory: 6717 grad_norm: 3.3054 loss: 1.2108 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2108 2023/04/14 10:59:51 - mmengine - INFO - Epoch(train) [72][1000/1879] lr: 2.0000e-03 eta: 5:29:49 time: 0.3606 data_time: 0.0128 memory: 6717 grad_norm: 3.2818 loss: 1.1779 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1779 2023/04/14 10:59:59 - mmengine - INFO - Epoch(train) [72][1020/1879] lr: 2.0000e-03 eta: 5:29:42 time: 0.3958 data_time: 0.0139 memory: 6717 grad_norm: 3.2806 loss: 1.2462 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2462 2023/04/14 11:00:06 - mmengine - INFO - Epoch(train) [72][1040/1879] lr: 2.0000e-03 eta: 5:29:34 time: 0.3234 data_time: 0.0146 memory: 6717 grad_norm: 3.2955 loss: 1.2567 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2567 2023/04/14 11:00:14 - mmengine - INFO - Epoch(train) [72][1060/1879] lr: 2.0000e-03 eta: 5:29:27 time: 0.4324 data_time: 0.0130 memory: 6717 grad_norm: 3.3248 loss: 1.2166 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2166 2023/04/14 11:00:21 - mmengine - INFO - Epoch(train) [72][1080/1879] lr: 2.0000e-03 eta: 5:29:20 time: 0.3299 data_time: 0.0143 memory: 6717 grad_norm: 3.2325 loss: 1.0991 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0991 2023/04/14 11:00:29 - mmengine - INFO - Epoch(train) [72][1100/1879] lr: 2.0000e-03 eta: 5:29:13 time: 0.4260 data_time: 0.0139 memory: 6717 grad_norm: 3.3249 loss: 1.1629 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1629 2023/04/14 11:00:36 - mmengine - INFO - Epoch(train) [72][1120/1879] lr: 2.0000e-03 eta: 5:29:05 time: 0.3154 data_time: 0.0136 memory: 6717 grad_norm: 3.2850 loss: 1.3341 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.3341 2023/04/14 11:00:44 - mmengine - INFO - Epoch(train) [72][1140/1879] lr: 2.0000e-03 eta: 5:28:58 time: 0.4003 data_time: 0.0143 memory: 6717 grad_norm: 3.3034 loss: 1.2857 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2857 2023/04/14 11:00:50 - mmengine - INFO - Epoch(train) [72][1160/1879] lr: 2.0000e-03 eta: 5:28:50 time: 0.3134 data_time: 0.0147 memory: 6717 grad_norm: 3.2594 loss: 1.2447 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2447 2023/04/14 11:00:59 - mmengine - INFO - Epoch(train) [72][1180/1879] lr: 2.0000e-03 eta: 5:28:43 time: 0.4619 data_time: 0.0138 memory: 6717 grad_norm: 3.3710 loss: 1.1776 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1776 2023/04/14 11:01:06 - mmengine - INFO - Epoch(train) [72][1200/1879] lr: 2.0000e-03 eta: 5:28:35 time: 0.3186 data_time: 0.0151 memory: 6717 grad_norm: 3.3069 loss: 1.3012 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3012 2023/04/14 11:01:14 - mmengine - INFO - Epoch(train) [72][1220/1879] lr: 2.0000e-03 eta: 5:28:28 time: 0.4128 data_time: 0.0142 memory: 6717 grad_norm: 3.2612 loss: 1.1252 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.1252 2023/04/14 11:01:20 - mmengine - INFO - Epoch(train) [72][1240/1879] lr: 2.0000e-03 eta: 5:28:21 time: 0.3267 data_time: 0.0159 memory: 6717 grad_norm: 3.3256 loss: 1.0110 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0110 2023/04/14 11:01:28 - mmengine - INFO - Epoch(train) [72][1260/1879] lr: 2.0000e-03 eta: 5:28:14 time: 0.3987 data_time: 0.0131 memory: 6717 grad_norm: 3.2655 loss: 1.3070 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3070 2023/04/14 11:01:34 - mmengine - INFO - Epoch(train) [72][1280/1879] lr: 2.0000e-03 eta: 5:28:06 time: 0.3077 data_time: 0.0152 memory: 6717 grad_norm: 3.4086 loss: 1.2507 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2507 2023/04/14 11:01:43 - mmengine - INFO - Epoch(train) [72][1300/1879] lr: 2.0000e-03 eta: 5:27:58 time: 0.4053 data_time: 0.0202 memory: 6717 grad_norm: 3.4074 loss: 1.1533 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1533 2023/04/14 11:01:49 - mmengine - INFO - Epoch(train) [72][1320/1879] lr: 2.0000e-03 eta: 5:27:51 time: 0.3258 data_time: 0.0152 memory: 6717 grad_norm: 3.1643 loss: 1.0556 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0556 2023/04/14 11:01:58 - mmengine - INFO - Epoch(train) [72][1340/1879] lr: 2.0000e-03 eta: 5:27:44 time: 0.4201 data_time: 0.0159 memory: 6717 grad_norm: 3.2940 loss: 1.1912 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1912 2023/04/14 11:02:04 - mmengine - INFO - Epoch(train) [72][1360/1879] lr: 2.0000e-03 eta: 5:27:36 time: 0.3261 data_time: 0.0142 memory: 6717 grad_norm: 3.2539 loss: 1.0802 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0802 2023/04/14 11:02:12 - mmengine - INFO - Epoch(train) [72][1380/1879] lr: 2.0000e-03 eta: 5:27:29 time: 0.4039 data_time: 0.0167 memory: 6717 grad_norm: 3.3027 loss: 1.1374 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1374 2023/04/14 11:02:19 - mmengine - INFO - Epoch(train) [72][1400/1879] lr: 2.0000e-03 eta: 5:27:21 time: 0.3515 data_time: 0.0127 memory: 6717 grad_norm: 3.3467 loss: 1.1323 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1323 2023/04/14 11:02:28 - mmengine - INFO - Epoch(train) [72][1420/1879] lr: 2.0000e-03 eta: 5:27:14 time: 0.4350 data_time: 0.0152 memory: 6717 grad_norm: 3.3463 loss: 1.1939 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1939 2023/04/14 11:02:34 - mmengine - INFO - Epoch(train) [72][1440/1879] lr: 2.0000e-03 eta: 5:27:07 time: 0.3175 data_time: 0.0141 memory: 6717 grad_norm: 3.3402 loss: 1.3306 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3306 2023/04/14 11:02:43 - mmengine - INFO - Epoch(train) [72][1460/1879] lr: 2.0000e-03 eta: 5:27:00 time: 0.4220 data_time: 0.0143 memory: 6717 grad_norm: 3.2632 loss: 1.2405 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2405 2023/04/14 11:02:49 - mmengine - INFO - Epoch(train) [72][1480/1879] lr: 2.0000e-03 eta: 5:26:52 time: 0.3139 data_time: 0.0142 memory: 6717 grad_norm: 3.2614 loss: 1.2603 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.2603 2023/04/14 11:02:57 - mmengine - INFO - Epoch(train) [72][1500/1879] lr: 2.0000e-03 eta: 5:26:45 time: 0.4225 data_time: 0.0167 memory: 6717 grad_norm: 3.3886 loss: 1.2232 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2232 2023/04/14 11:03:04 - mmengine - INFO - Epoch(train) [72][1520/1879] lr: 2.0000e-03 eta: 5:26:37 time: 0.3164 data_time: 0.0132 memory: 6717 grad_norm: 3.3221 loss: 1.1467 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1467 2023/04/14 11:03:12 - mmengine - INFO - Epoch(train) [72][1540/1879] lr: 2.0000e-03 eta: 5:26:30 time: 0.4163 data_time: 0.0168 memory: 6717 grad_norm: 3.2170 loss: 1.2099 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2099 2023/04/14 11:03:19 - mmengine - INFO - Epoch(train) [72][1560/1879] lr: 2.0000e-03 eta: 5:26:22 time: 0.3543 data_time: 0.0127 memory: 6717 grad_norm: 3.3550 loss: 1.1304 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1304 2023/04/14 11:03:27 - mmengine - INFO - Epoch(train) [72][1580/1879] lr: 2.0000e-03 eta: 5:26:15 time: 0.3992 data_time: 0.0157 memory: 6717 grad_norm: 3.2860 loss: 1.0341 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0341 2023/04/14 11:03:31 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 11:03:34 - mmengine - INFO - Epoch(train) [72][1600/1879] lr: 2.0000e-03 eta: 5:26:08 time: 0.3312 data_time: 0.0140 memory: 6717 grad_norm: 3.3282 loss: 1.2471 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2471 2023/04/14 11:03:42 - mmengine - INFO - Epoch(train) [72][1620/1879] lr: 2.0000e-03 eta: 5:26:00 time: 0.4129 data_time: 0.0138 memory: 6717 grad_norm: 3.2218 loss: 1.1189 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1189 2023/04/14 11:03:50 - mmengine - INFO - Epoch(train) [72][1640/1879] lr: 2.0000e-03 eta: 5:25:53 time: 0.3736 data_time: 0.0149 memory: 6717 grad_norm: 3.3108 loss: 1.1636 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1636 2023/04/14 11:03:57 - mmengine - INFO - Epoch(train) [72][1660/1879] lr: 2.0000e-03 eta: 5:25:46 time: 0.3676 data_time: 0.0154 memory: 6717 grad_norm: 3.3331 loss: 1.3116 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3116 2023/04/14 11:04:04 - mmengine - INFO - Epoch(train) [72][1680/1879] lr: 2.0000e-03 eta: 5:25:38 time: 0.3420 data_time: 0.0159 memory: 6717 grad_norm: 3.2564 loss: 1.0652 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0652 2023/04/14 11:04:11 - mmengine - INFO - Epoch(train) [72][1700/1879] lr: 2.0000e-03 eta: 5:25:31 time: 0.3702 data_time: 0.0137 memory: 6717 grad_norm: 3.2341 loss: 1.1378 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1378 2023/04/14 11:04:18 - mmengine - INFO - Epoch(train) [72][1720/1879] lr: 2.0000e-03 eta: 5:25:23 time: 0.3487 data_time: 0.0149 memory: 6717 grad_norm: 3.2237 loss: 1.2523 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2523 2023/04/14 11:04:25 - mmengine - INFO - Epoch(train) [72][1740/1879] lr: 2.0000e-03 eta: 5:25:16 time: 0.3546 data_time: 0.0146 memory: 6717 grad_norm: 3.3439 loss: 1.1800 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1800 2023/04/14 11:04:33 - mmengine - INFO - Epoch(train) [72][1760/1879] lr: 2.0000e-03 eta: 5:25:08 time: 0.3761 data_time: 0.0144 memory: 6717 grad_norm: 3.3398 loss: 1.1912 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1912 2023/04/14 11:04:40 - mmengine - INFO - Epoch(train) [72][1780/1879] lr: 2.0000e-03 eta: 5:25:01 time: 0.3419 data_time: 0.0160 memory: 6717 grad_norm: 3.3000 loss: 1.0397 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0397 2023/04/14 11:04:48 - mmengine - INFO - Epoch(train) [72][1800/1879] lr: 2.0000e-03 eta: 5:24:54 time: 0.4119 data_time: 0.0146 memory: 6717 grad_norm: 3.3240 loss: 1.0920 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0920 2023/04/14 11:04:55 - mmengine - INFO - Epoch(train) [72][1820/1879] lr: 2.0000e-03 eta: 5:24:46 time: 0.3394 data_time: 0.0148 memory: 6717 grad_norm: 3.2878 loss: 1.2202 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2202 2023/04/14 11:05:02 - mmengine - INFO - Epoch(train) [72][1840/1879] lr: 2.0000e-03 eta: 5:24:39 time: 0.3717 data_time: 0.0152 memory: 6717 grad_norm: 3.3298 loss: 1.1756 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1756 2023/04/14 11:05:10 - mmengine - INFO - Epoch(train) [72][1860/1879] lr: 2.0000e-03 eta: 5:24:31 time: 0.3976 data_time: 0.0145 memory: 6717 grad_norm: 3.2613 loss: 1.1487 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1487 2023/04/14 11:05:16 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 11:05:16 - mmengine - INFO - Epoch(train) [72][1879/1879] lr: 2.0000e-03 eta: 5:24:24 time: 0.3416 data_time: 0.0124 memory: 6717 grad_norm: 3.4261 loss: 1.0843 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 1.0843 2023/04/14 11:05:16 - mmengine - INFO - Saving checkpoint at 72 epochs 2023/04/14 11:05:25 - mmengine - INFO - Epoch(val) [72][ 20/155] eta: 0:01:00 time: 0.4479 data_time: 0.4138 memory: 1391 2023/04/14 11:05:32 - mmengine - INFO - Epoch(val) [72][ 40/155] eta: 0:00:44 time: 0.3308 data_time: 0.2973 memory: 1391 2023/04/14 11:05:40 - mmengine - INFO - Epoch(val) [72][ 60/155] eta: 0:00:38 time: 0.4301 data_time: 0.3966 memory: 1391 2023/04/14 11:05:47 - mmengine - INFO - Epoch(val) [72][ 80/155] eta: 0:00:28 time: 0.3155 data_time: 0.2825 memory: 1391 2023/04/14 11:05:56 - mmengine - INFO - Epoch(val) [72][100/155] eta: 0:00:21 time: 0.4539 data_time: 0.4201 memory: 1391 2023/04/14 11:06:02 - mmengine - INFO - Epoch(val) [72][120/155] eta: 0:00:13 time: 0.3111 data_time: 0.2774 memory: 1391 2023/04/14 11:06:12 - mmengine - INFO - Epoch(val) [72][140/155] eta: 0:00:05 time: 0.4825 data_time: 0.4496 memory: 1391 2023/04/14 11:06:19 - mmengine - INFO - Epoch(val) [72][155/155] acc/top1: 0.6627 acc/top5: 0.8714 acc/mean1: 0.6626 data_time: 0.4179 time: 0.4499 2023/04/14 11:06:29 - mmengine - INFO - Epoch(train) [73][ 20/1879] lr: 2.0000e-03 eta: 5:24:17 time: 0.5040 data_time: 0.2739 memory: 6717 grad_norm: 3.2574 loss: 1.1015 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1015 2023/04/14 11:06:35 - mmengine - INFO - Epoch(train) [73][ 40/1879] lr: 2.0000e-03 eta: 5:24:10 time: 0.3191 data_time: 0.1021 memory: 6717 grad_norm: 3.3134 loss: 1.1320 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1320 2023/04/14 11:06:44 - mmengine - INFO - Epoch(train) [73][ 60/1879] lr: 2.0000e-03 eta: 5:24:03 time: 0.4370 data_time: 0.2220 memory: 6717 grad_norm: 3.2051 loss: 1.1501 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1501 2023/04/14 11:06:50 - mmengine - INFO - Epoch(train) [73][ 80/1879] lr: 2.0000e-03 eta: 5:23:55 time: 0.3154 data_time: 0.1479 memory: 6717 grad_norm: 3.2283 loss: 1.0874 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0874 2023/04/14 11:06:59 - mmengine - INFO - Epoch(train) [73][ 100/1879] lr: 2.0000e-03 eta: 5:23:48 time: 0.4307 data_time: 0.2928 memory: 6717 grad_norm: 3.3230 loss: 1.1076 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1076 2023/04/14 11:07:05 - mmengine - INFO - Epoch(train) [73][ 120/1879] lr: 2.0000e-03 eta: 5:23:40 time: 0.3273 data_time: 0.1267 memory: 6717 grad_norm: 3.2500 loss: 1.1193 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1193 2023/04/14 11:07:14 - mmengine - INFO - Epoch(train) [73][ 140/1879] lr: 2.0000e-03 eta: 5:23:33 time: 0.4069 data_time: 0.2353 memory: 6717 grad_norm: 3.3603 loss: 1.1452 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1452 2023/04/14 11:07:20 - mmengine - INFO - Epoch(train) [73][ 160/1879] lr: 2.0000e-03 eta: 5:23:25 time: 0.3378 data_time: 0.1773 memory: 6717 grad_norm: 3.2490 loss: 1.1945 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1945 2023/04/14 11:07:29 - mmengine - INFO - Epoch(train) [73][ 180/1879] lr: 2.0000e-03 eta: 5:23:18 time: 0.4133 data_time: 0.2620 memory: 6717 grad_norm: 3.2858 loss: 1.1589 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1589 2023/04/14 11:07:35 - mmengine - INFO - Epoch(train) [73][ 200/1879] lr: 2.0000e-03 eta: 5:23:11 time: 0.3189 data_time: 0.1247 memory: 6717 grad_norm: 3.2200 loss: 1.1152 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1152 2023/04/14 11:07:43 - mmengine - INFO - Epoch(train) [73][ 220/1879] lr: 2.0000e-03 eta: 5:23:03 time: 0.3979 data_time: 0.1193 memory: 6717 grad_norm: 3.3174 loss: 1.1412 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1412 2023/04/14 11:07:50 - mmengine - INFO - Epoch(train) [73][ 240/1879] lr: 2.0000e-03 eta: 5:22:56 time: 0.3348 data_time: 0.0272 memory: 6717 grad_norm: 3.3916 loss: 1.0911 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0911 2023/04/14 11:07:58 - mmengine - INFO - Epoch(train) [73][ 260/1879] lr: 2.0000e-03 eta: 5:22:49 time: 0.3920 data_time: 0.0160 memory: 6717 grad_norm: 3.3038 loss: 1.4272 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4272 2023/04/14 11:08:05 - mmengine - INFO - Epoch(train) [73][ 280/1879] lr: 2.0000e-03 eta: 5:22:41 time: 0.3491 data_time: 0.0233 memory: 6717 grad_norm: 3.3126 loss: 1.2849 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2849 2023/04/14 11:08:13 - mmengine - INFO - Epoch(train) [73][ 300/1879] lr: 2.0000e-03 eta: 5:22:34 time: 0.4195 data_time: 0.0162 memory: 6717 grad_norm: 3.2504 loss: 0.9907 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 0.9907 2023/04/14 11:08:20 - mmengine - INFO - Epoch(train) [73][ 320/1879] lr: 2.0000e-03 eta: 5:22:26 time: 0.3404 data_time: 0.0129 memory: 6717 grad_norm: 3.3580 loss: 1.0923 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0923 2023/04/14 11:08:28 - mmengine - INFO - Epoch(train) [73][ 340/1879] lr: 2.0000e-03 eta: 5:22:19 time: 0.3891 data_time: 0.0160 memory: 6717 grad_norm: 3.3850 loss: 1.0580 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0580 2023/04/14 11:08:34 - mmengine - INFO - Epoch(train) [73][ 360/1879] lr: 2.0000e-03 eta: 5:22:11 time: 0.3032 data_time: 0.0130 memory: 6717 grad_norm: 3.3072 loss: 1.1806 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1806 2023/04/14 11:08:42 - mmengine - INFO - Epoch(train) [73][ 380/1879] lr: 2.0000e-03 eta: 5:22:04 time: 0.4081 data_time: 0.0163 memory: 6717 grad_norm: 3.2959 loss: 1.1849 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1849 2023/04/14 11:08:49 - mmengine - INFO - Epoch(train) [73][ 400/1879] lr: 2.0000e-03 eta: 5:21:56 time: 0.3416 data_time: 0.0350 memory: 6717 grad_norm: 3.2432 loss: 1.2711 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2711 2023/04/14 11:08:57 - mmengine - INFO - Epoch(train) [73][ 420/1879] lr: 2.0000e-03 eta: 5:21:50 time: 0.4304 data_time: 0.0833 memory: 6717 grad_norm: 3.2589 loss: 1.1821 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1821 2023/04/14 11:09:04 - mmengine - INFO - Epoch(train) [73][ 440/1879] lr: 2.0000e-03 eta: 5:21:42 time: 0.3321 data_time: 0.0857 memory: 6717 grad_norm: 3.2637 loss: 1.0439 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0439 2023/04/14 11:09:12 - mmengine - INFO - Epoch(train) [73][ 460/1879] lr: 2.0000e-03 eta: 5:21:35 time: 0.4058 data_time: 0.0637 memory: 6717 grad_norm: 3.3137 loss: 1.2759 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2759 2023/04/14 11:09:19 - mmengine - INFO - Epoch(train) [73][ 480/1879] lr: 2.0000e-03 eta: 5:21:27 time: 0.3276 data_time: 0.0123 memory: 6717 grad_norm: 3.2802 loss: 1.1506 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1506 2023/04/14 11:09:26 - mmengine - INFO - Epoch(train) [73][ 500/1879] lr: 2.0000e-03 eta: 5:21:20 time: 0.3959 data_time: 0.0166 memory: 6717 grad_norm: 3.2395 loss: 1.0641 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0641 2023/04/14 11:09:33 - mmengine - INFO - Epoch(train) [73][ 520/1879] lr: 2.0000e-03 eta: 5:21:12 time: 0.3110 data_time: 0.0333 memory: 6717 grad_norm: 3.2655 loss: 1.1268 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1268 2023/04/14 11:09:41 - mmengine - INFO - Epoch(train) [73][ 540/1879] lr: 2.0000e-03 eta: 5:21:05 time: 0.4314 data_time: 0.0769 memory: 6717 grad_norm: 3.2307 loss: 1.1299 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1299 2023/04/14 11:09:48 - mmengine - INFO - Epoch(train) [73][ 560/1879] lr: 2.0000e-03 eta: 5:20:57 time: 0.3430 data_time: 0.1183 memory: 6717 grad_norm: 3.4124 loss: 1.2360 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2360 2023/04/14 11:09:56 - mmengine - INFO - Epoch(train) [73][ 580/1879] lr: 2.0000e-03 eta: 5:20:50 time: 0.4005 data_time: 0.2236 memory: 6717 grad_norm: 3.2827 loss: 1.3987 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3987 2023/04/14 11:10:03 - mmengine - INFO - Epoch(train) [73][ 600/1879] lr: 2.0000e-03 eta: 5:20:42 time: 0.3226 data_time: 0.1508 memory: 6717 grad_norm: 3.2980 loss: 1.0830 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0830 2023/04/14 11:10:11 - mmengine - INFO - Epoch(train) [73][ 620/1879] lr: 2.0000e-03 eta: 5:20:35 time: 0.3953 data_time: 0.1869 memory: 6717 grad_norm: 3.2296 loss: 1.2290 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2290 2023/04/14 11:10:17 - mmengine - INFO - Epoch(train) [73][ 640/1879] lr: 2.0000e-03 eta: 5:20:28 time: 0.3367 data_time: 0.1842 memory: 6717 grad_norm: 3.3684 loss: 1.1452 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1452 2023/04/14 11:10:26 - mmengine - INFO - Epoch(train) [73][ 660/1879] lr: 2.0000e-03 eta: 5:20:21 time: 0.4196 data_time: 0.2757 memory: 6717 grad_norm: 3.3580 loss: 1.2943 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2943 2023/04/14 11:10:32 - mmengine - INFO - Epoch(train) [73][ 680/1879] lr: 2.0000e-03 eta: 5:20:13 time: 0.3135 data_time: 0.1700 memory: 6717 grad_norm: 3.2504 loss: 1.2570 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2570 2023/04/14 11:10:40 - mmengine - INFO - Epoch(train) [73][ 700/1879] lr: 2.0000e-03 eta: 5:20:06 time: 0.3921 data_time: 0.2460 memory: 6717 grad_norm: 3.2764 loss: 1.2994 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2994 2023/04/14 11:10:43 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 11:10:46 - mmengine - INFO - Epoch(train) [73][ 720/1879] lr: 2.0000e-03 eta: 5:19:58 time: 0.3103 data_time: 0.1357 memory: 6717 grad_norm: 3.2839 loss: 1.1604 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1604 2023/04/14 11:10:54 - mmengine - INFO - Epoch(train) [73][ 740/1879] lr: 2.0000e-03 eta: 5:19:51 time: 0.4050 data_time: 0.2578 memory: 6717 grad_norm: 3.3309 loss: 1.1957 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1957 2023/04/14 11:11:01 - mmengine - INFO - Epoch(train) [73][ 760/1879] lr: 2.0000e-03 eta: 5:19:43 time: 0.3508 data_time: 0.2064 memory: 6717 grad_norm: 3.3249 loss: 1.1813 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1813 2023/04/14 11:11:09 - mmengine - INFO - Epoch(train) [73][ 780/1879] lr: 2.0000e-03 eta: 5:19:36 time: 0.3995 data_time: 0.2437 memory: 6717 grad_norm: 3.2964 loss: 1.3112 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.3112 2023/04/14 11:11:16 - mmengine - INFO - Epoch(train) [73][ 800/1879] lr: 2.0000e-03 eta: 5:19:28 time: 0.3258 data_time: 0.1726 memory: 6717 grad_norm: 3.4789 loss: 1.1536 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1536 2023/04/14 11:11:24 - mmengine - INFO - Epoch(train) [73][ 820/1879] lr: 2.0000e-03 eta: 5:19:21 time: 0.4130 data_time: 0.2470 memory: 6717 grad_norm: 3.2914 loss: 1.2202 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2202 2023/04/14 11:11:31 - mmengine - INFO - Epoch(train) [73][ 840/1879] lr: 2.0000e-03 eta: 5:19:13 time: 0.3284 data_time: 0.1616 memory: 6717 grad_norm: 3.3862 loss: 1.2822 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2822 2023/04/14 11:11:39 - mmengine - INFO - Epoch(train) [73][ 860/1879] lr: 2.0000e-03 eta: 5:19:06 time: 0.4350 data_time: 0.2115 memory: 6717 grad_norm: 3.2743 loss: 1.3132 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.3132 2023/04/14 11:11:45 - mmengine - INFO - Epoch(train) [73][ 880/1879] lr: 2.0000e-03 eta: 5:18:58 time: 0.3037 data_time: 0.0831 memory: 6717 grad_norm: 3.2973 loss: 1.2563 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2563 2023/04/14 11:11:54 - mmengine - INFO - Epoch(train) [73][ 900/1879] lr: 2.0000e-03 eta: 5:18:52 time: 0.4376 data_time: 0.2437 memory: 6717 grad_norm: 3.3958 loss: 1.2163 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2163 2023/04/14 11:12:01 - mmengine - INFO - Epoch(train) [73][ 920/1879] lr: 2.0000e-03 eta: 5:18:44 time: 0.3323 data_time: 0.1445 memory: 6717 grad_norm: 3.3116 loss: 1.1871 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1871 2023/04/14 11:12:09 - mmengine - INFO - Epoch(train) [73][ 940/1879] lr: 2.0000e-03 eta: 5:18:37 time: 0.4216 data_time: 0.2409 memory: 6717 grad_norm: 3.3481 loss: 1.1301 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1301 2023/04/14 11:12:16 - mmengine - INFO - Epoch(train) [73][ 960/1879] lr: 2.0000e-03 eta: 5:18:29 time: 0.3479 data_time: 0.1439 memory: 6717 grad_norm: 3.3654 loss: 1.1959 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1959 2023/04/14 11:12:24 - mmengine - INFO - Epoch(train) [73][ 980/1879] lr: 2.0000e-03 eta: 5:18:22 time: 0.4068 data_time: 0.1245 memory: 6717 grad_norm: 3.2843 loss: 1.0939 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0939 2023/04/14 11:12:30 - mmengine - INFO - Epoch(train) [73][1000/1879] lr: 2.0000e-03 eta: 5:18:14 time: 0.2986 data_time: 0.0487 memory: 6717 grad_norm: 3.4298 loss: 1.0723 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0723 2023/04/14 11:12:38 - mmengine - INFO - Epoch(train) [73][1020/1879] lr: 2.0000e-03 eta: 5:18:07 time: 0.3970 data_time: 0.1101 memory: 6717 grad_norm: 3.4299 loss: 1.1085 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1085 2023/04/14 11:12:45 - mmengine - INFO - Epoch(train) [73][1040/1879] lr: 2.0000e-03 eta: 5:17:59 time: 0.3323 data_time: 0.0894 memory: 6717 grad_norm: 3.3087 loss: 1.2765 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2765 2023/04/14 11:12:53 - mmengine - INFO - Epoch(train) [73][1060/1879] lr: 2.0000e-03 eta: 5:17:52 time: 0.3926 data_time: 0.1238 memory: 6717 grad_norm: 3.2208 loss: 1.2269 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2269 2023/04/14 11:12:59 - mmengine - INFO - Epoch(train) [73][1080/1879] lr: 2.0000e-03 eta: 5:17:44 time: 0.3077 data_time: 0.1002 memory: 6717 grad_norm: 3.3284 loss: 1.1562 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1562 2023/04/14 11:13:07 - mmengine - INFO - Epoch(train) [73][1100/1879] lr: 2.0000e-03 eta: 5:17:37 time: 0.4326 data_time: 0.1747 memory: 6717 grad_norm: 3.2853 loss: 1.1668 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1668 2023/04/14 11:13:14 - mmengine - INFO - Epoch(train) [73][1120/1879] lr: 2.0000e-03 eta: 5:17:30 time: 0.3220 data_time: 0.0310 memory: 6717 grad_norm: 3.4089 loss: 1.0851 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0851 2023/04/14 11:13:22 - mmengine - INFO - Epoch(train) [73][1140/1879] lr: 2.0000e-03 eta: 5:17:22 time: 0.3948 data_time: 0.0248 memory: 6717 grad_norm: 3.3127 loss: 1.2870 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2870 2023/04/14 11:13:29 - mmengine - INFO - Epoch(train) [73][1160/1879] lr: 2.0000e-03 eta: 5:17:15 time: 0.3536 data_time: 0.0198 memory: 6717 grad_norm: 3.3767 loss: 1.2554 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2554 2023/04/14 11:13:36 - mmengine - INFO - Epoch(train) [73][1180/1879] lr: 2.0000e-03 eta: 5:17:07 time: 0.3630 data_time: 0.0167 memory: 6717 grad_norm: 3.3212 loss: 1.1019 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1019 2023/04/14 11:13:44 - mmengine - INFO - Epoch(train) [73][1200/1879] lr: 2.0000e-03 eta: 5:17:00 time: 0.4018 data_time: 0.0119 memory: 6717 grad_norm: 3.2479 loss: 0.9796 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9796 2023/04/14 11:13:50 - mmengine - INFO - Epoch(train) [73][1220/1879] lr: 2.0000e-03 eta: 5:16:52 time: 0.3137 data_time: 0.0159 memory: 6717 grad_norm: 3.3213 loss: 1.0692 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0692 2023/04/14 11:13:58 - mmengine - INFO - Epoch(train) [73][1240/1879] lr: 2.0000e-03 eta: 5:16:45 time: 0.3794 data_time: 0.0182 memory: 6717 grad_norm: 3.2834 loss: 1.2270 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2270 2023/04/14 11:14:05 - mmengine - INFO - Epoch(train) [73][1260/1879] lr: 2.0000e-03 eta: 5:16:38 time: 0.3646 data_time: 0.0206 memory: 6717 grad_norm: 3.2965 loss: 1.0717 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.0717 2023/04/14 11:14:12 - mmengine - INFO - Epoch(train) [73][1280/1879] lr: 2.0000e-03 eta: 5:16:30 time: 0.3390 data_time: 0.0151 memory: 6717 grad_norm: 3.3327 loss: 1.1637 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1637 2023/04/14 11:14:21 - mmengine - INFO - Epoch(train) [73][1300/1879] lr: 2.0000e-03 eta: 5:16:23 time: 0.4222 data_time: 0.0456 memory: 6717 grad_norm: 3.3060 loss: 1.3367 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3367 2023/04/14 11:14:27 - mmengine - INFO - Epoch(train) [73][1320/1879] lr: 2.0000e-03 eta: 5:16:15 time: 0.3345 data_time: 0.0128 memory: 6717 grad_norm: 3.3823 loss: 1.2185 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2185 2023/04/14 11:14:35 - mmengine - INFO - Epoch(train) [73][1340/1879] lr: 2.0000e-03 eta: 5:16:08 time: 0.3955 data_time: 0.0159 memory: 6717 grad_norm: 3.2880 loss: 1.2170 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2170 2023/04/14 11:14:42 - mmengine - INFO - Epoch(train) [73][1360/1879] lr: 2.0000e-03 eta: 5:16:01 time: 0.3541 data_time: 0.0136 memory: 6717 grad_norm: 3.2163 loss: 1.2562 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2562 2023/04/14 11:14:50 - mmengine - INFO - Epoch(train) [73][1380/1879] lr: 2.0000e-03 eta: 5:15:53 time: 0.3818 data_time: 0.0148 memory: 6717 grad_norm: 3.3304 loss: 1.1864 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1864 2023/04/14 11:14:57 - mmengine - INFO - Epoch(train) [73][1400/1879] lr: 2.0000e-03 eta: 5:15:46 time: 0.3335 data_time: 0.0135 memory: 6717 grad_norm: 3.3609 loss: 0.9699 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9699 2023/04/14 11:15:05 - mmengine - INFO - Epoch(train) [73][1420/1879] lr: 2.0000e-03 eta: 5:15:39 time: 0.4194 data_time: 0.0152 memory: 6717 grad_norm: 3.3098 loss: 1.0718 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0718 2023/04/14 11:15:12 - mmengine - INFO - Epoch(train) [73][1440/1879] lr: 2.0000e-03 eta: 5:15:31 time: 0.3247 data_time: 0.0127 memory: 6717 grad_norm: 3.2827 loss: 1.0634 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0634 2023/04/14 11:15:19 - mmengine - INFO - Epoch(train) [73][1460/1879] lr: 2.0000e-03 eta: 5:15:24 time: 0.3896 data_time: 0.0157 memory: 6717 grad_norm: 3.4720 loss: 1.3146 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3146 2023/04/14 11:15:26 - mmengine - INFO - Epoch(train) [73][1480/1879] lr: 2.0000e-03 eta: 5:15:16 time: 0.3573 data_time: 0.0137 memory: 6717 grad_norm: 3.3086 loss: 1.1664 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1664 2023/04/14 11:15:35 - mmengine - INFO - Epoch(train) [73][1500/1879] lr: 2.0000e-03 eta: 5:15:09 time: 0.4220 data_time: 0.0143 memory: 6717 grad_norm: 3.2730 loss: 1.1229 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1229 2023/04/14 11:15:41 - mmengine - INFO - Epoch(train) [73][1520/1879] lr: 2.0000e-03 eta: 5:15:01 time: 0.3149 data_time: 0.0138 memory: 6717 grad_norm: 3.3056 loss: 1.2929 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2929 2023/04/14 11:15:50 - mmengine - INFO - Epoch(train) [73][1540/1879] lr: 2.0000e-03 eta: 5:14:54 time: 0.4186 data_time: 0.0157 memory: 6717 grad_norm: 3.2718 loss: 1.2801 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2801 2023/04/14 11:15:56 - mmengine - INFO - Epoch(train) [73][1560/1879] lr: 2.0000e-03 eta: 5:14:46 time: 0.3162 data_time: 0.0149 memory: 6717 grad_norm: 3.3018 loss: 1.0619 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0619 2023/04/14 11:16:04 - mmengine - INFO - Epoch(train) [73][1580/1879] lr: 2.0000e-03 eta: 5:14:39 time: 0.3925 data_time: 0.0146 memory: 6717 grad_norm: 3.2878 loss: 1.1152 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1152 2023/04/14 11:16:11 - mmengine - INFO - Epoch(train) [73][1600/1879] lr: 2.0000e-03 eta: 5:14:32 time: 0.3463 data_time: 0.0155 memory: 6717 grad_norm: 3.3323 loss: 1.2434 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2434 2023/04/14 11:16:18 - mmengine - INFO - Epoch(train) [73][1620/1879] lr: 2.0000e-03 eta: 5:14:24 time: 0.3845 data_time: 0.0132 memory: 6717 grad_norm: 3.2357 loss: 1.1278 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1278 2023/04/14 11:16:30 - mmengine - INFO - Epoch(train) [73][1640/1879] lr: 2.0000e-03 eta: 5:14:18 time: 0.5668 data_time: 0.0159 memory: 6717 grad_norm: 3.3291 loss: 1.1200 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1200 2023/04/14 11:16:38 - mmengine - INFO - Epoch(train) [73][1660/1879] lr: 2.0000e-03 eta: 5:14:11 time: 0.4069 data_time: 0.0164 memory: 6717 grad_norm: 3.3139 loss: 1.2583 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2583 2023/04/14 11:16:44 - mmengine - INFO - Epoch(train) [73][1680/1879] lr: 2.0000e-03 eta: 5:14:04 time: 0.3184 data_time: 0.0154 memory: 6717 grad_norm: 3.3595 loss: 1.2026 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2026 2023/04/14 11:16:52 - mmengine - INFO - Epoch(train) [73][1700/1879] lr: 2.0000e-03 eta: 5:13:56 time: 0.3990 data_time: 0.0134 memory: 6717 grad_norm: 3.2801 loss: 1.2021 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2021 2023/04/14 11:16:56 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 11:16:59 - mmengine - INFO - Epoch(train) [73][1720/1879] lr: 2.0000e-03 eta: 5:13:49 time: 0.3348 data_time: 0.0140 memory: 6717 grad_norm: 3.3977 loss: 1.3514 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3514 2023/04/14 11:17:07 - mmengine - INFO - Epoch(train) [73][1740/1879] lr: 2.0000e-03 eta: 5:13:42 time: 0.3958 data_time: 0.0143 memory: 6717 grad_norm: 3.3517 loss: 1.2483 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2483 2023/04/14 11:17:13 - mmengine - INFO - Epoch(train) [73][1760/1879] lr: 2.0000e-03 eta: 5:13:34 time: 0.3277 data_time: 0.0190 memory: 6717 grad_norm: 3.3421 loss: 1.2712 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2712 2023/04/14 11:17:21 - mmengine - INFO - Epoch(train) [73][1780/1879] lr: 2.0000e-03 eta: 5:13:27 time: 0.3877 data_time: 0.0168 memory: 6717 grad_norm: 3.2840 loss: 1.3564 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3564 2023/04/14 11:17:28 - mmengine - INFO - Epoch(train) [73][1800/1879] lr: 2.0000e-03 eta: 5:13:19 time: 0.3419 data_time: 0.0133 memory: 6717 grad_norm: 3.2939 loss: 1.1146 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1146 2023/04/14 11:17:37 - mmengine - INFO - Epoch(train) [73][1820/1879] lr: 2.0000e-03 eta: 5:13:12 time: 0.4510 data_time: 0.0166 memory: 6717 grad_norm: 3.2624 loss: 1.1713 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1713 2023/04/14 11:17:43 - mmengine - INFO - Epoch(train) [73][1840/1879] lr: 2.0000e-03 eta: 5:13:04 time: 0.2939 data_time: 0.0147 memory: 6717 grad_norm: 3.2878 loss: 1.3005 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.3005 2023/04/14 11:17:51 - mmengine - INFO - Epoch(train) [73][1860/1879] lr: 2.0000e-03 eta: 5:12:57 time: 0.4038 data_time: 0.0219 memory: 6717 grad_norm: 3.3923 loss: 1.2166 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2166 2023/04/14 11:17:58 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 11:17:58 - mmengine - INFO - Epoch(train) [73][1879/1879] lr: 2.0000e-03 eta: 5:12:50 time: 0.3529 data_time: 0.0193 memory: 6717 grad_norm: 3.4495 loss: 1.1282 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 1.1282 2023/04/14 11:18:07 - mmengine - INFO - Epoch(val) [73][ 20/155] eta: 0:01:02 time: 0.4620 data_time: 0.4288 memory: 1391 2023/04/14 11:18:13 - mmengine - INFO - Epoch(val) [73][ 40/155] eta: 0:00:44 time: 0.3043 data_time: 0.2714 memory: 1391 2023/04/14 11:18:22 - mmengine - INFO - Epoch(val) [73][ 60/155] eta: 0:00:38 time: 0.4460 data_time: 0.4128 memory: 1391 2023/04/14 11:18:28 - mmengine - INFO - Epoch(val) [73][ 80/155] eta: 0:00:28 time: 0.3133 data_time: 0.2801 memory: 1391 2023/04/14 11:18:37 - mmengine - INFO - Epoch(val) [73][100/155] eta: 0:00:21 time: 0.4578 data_time: 0.4247 memory: 1391 2023/04/14 11:18:43 - mmengine - INFO - Epoch(val) [73][120/155] eta: 0:00:13 time: 0.3015 data_time: 0.2678 memory: 1391 2023/04/14 11:18:53 - mmengine - INFO - Epoch(val) [73][140/155] eta: 0:00:05 time: 0.4839 data_time: 0.4510 memory: 1391 2023/04/14 11:19:00 - mmengine - INFO - Epoch(val) [73][155/155] acc/top1: 0.6641 acc/top5: 0.8730 acc/mean1: 0.6641 data_time: 0.4193 time: 0.4520 2023/04/14 11:19:10 - mmengine - INFO - Epoch(train) [74][ 20/1879] lr: 2.0000e-03 eta: 5:12:43 time: 0.4808 data_time: 0.3363 memory: 6717 grad_norm: 3.3501 loss: 1.1928 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1928 2023/04/14 11:19:16 - mmengine - INFO - Epoch(train) [74][ 40/1879] lr: 2.0000e-03 eta: 5:12:35 time: 0.3219 data_time: 0.1656 memory: 6717 grad_norm: 3.2985 loss: 1.1100 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1100 2023/04/14 11:19:25 - mmengine - INFO - Epoch(train) [74][ 60/1879] lr: 2.0000e-03 eta: 5:12:29 time: 0.4255 data_time: 0.2698 memory: 6717 grad_norm: 3.2490 loss: 1.1200 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1200 2023/04/14 11:19:31 - mmengine - INFO - Epoch(train) [74][ 80/1879] lr: 2.0000e-03 eta: 5:12:21 time: 0.3203 data_time: 0.1775 memory: 6717 grad_norm: 3.4151 loss: 1.2307 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2307 2023/04/14 11:19:40 - mmengine - INFO - Epoch(train) [74][ 100/1879] lr: 2.0000e-03 eta: 5:12:14 time: 0.4133 data_time: 0.2551 memory: 6717 grad_norm: 3.2398 loss: 1.2388 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2388 2023/04/14 11:19:46 - mmengine - INFO - Epoch(train) [74][ 120/1879] lr: 2.0000e-03 eta: 5:12:06 time: 0.3240 data_time: 0.1197 memory: 6717 grad_norm: 3.2473 loss: 1.1373 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1373 2023/04/14 11:19:54 - mmengine - INFO - Epoch(train) [74][ 140/1879] lr: 2.0000e-03 eta: 5:11:59 time: 0.4078 data_time: 0.1818 memory: 6717 grad_norm: 3.2376 loss: 1.1771 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1771 2023/04/14 11:20:01 - mmengine - INFO - Epoch(train) [74][ 160/1879] lr: 2.0000e-03 eta: 5:11:51 time: 0.3293 data_time: 0.1096 memory: 6717 grad_norm: 3.3262 loss: 1.0967 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0967 2023/04/14 11:20:09 - mmengine - INFO - Epoch(train) [74][ 180/1879] lr: 2.0000e-03 eta: 5:11:44 time: 0.4167 data_time: 0.1197 memory: 6717 grad_norm: 3.3432 loss: 1.2340 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2340 2023/04/14 11:20:16 - mmengine - INFO - Epoch(train) [74][ 200/1879] lr: 2.0000e-03 eta: 5:11:36 time: 0.3408 data_time: 0.0705 memory: 6717 grad_norm: 3.2557 loss: 1.1472 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1472 2023/04/14 11:20:23 - mmengine - INFO - Epoch(train) [74][ 220/1879] lr: 2.0000e-03 eta: 5:11:29 time: 0.3564 data_time: 0.0527 memory: 6717 grad_norm: 3.2685 loss: 1.2467 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2467 2023/04/14 11:20:31 - mmengine - INFO - Epoch(train) [74][ 240/1879] lr: 2.0000e-03 eta: 5:11:22 time: 0.3710 data_time: 0.0132 memory: 6717 grad_norm: 3.3311 loss: 1.1520 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1520 2023/04/14 11:20:38 - mmengine - INFO - Epoch(train) [74][ 260/1879] lr: 2.0000e-03 eta: 5:11:14 time: 0.3734 data_time: 0.0256 memory: 6717 grad_norm: 3.3607 loss: 1.1558 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1558 2023/04/14 11:20:45 - mmengine - INFO - Epoch(train) [74][ 280/1879] lr: 2.0000e-03 eta: 5:11:07 time: 0.3379 data_time: 0.0134 memory: 6717 grad_norm: 3.3354 loss: 1.2541 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2541 2023/04/14 11:20:53 - mmengine - INFO - Epoch(train) [74][ 300/1879] lr: 2.0000e-03 eta: 5:10:59 time: 0.4007 data_time: 0.0702 memory: 6717 grad_norm: 3.2268 loss: 1.3672 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3672 2023/04/14 11:20:59 - mmengine - INFO - Epoch(train) [74][ 320/1879] lr: 2.0000e-03 eta: 5:10:52 time: 0.3122 data_time: 0.0456 memory: 6717 grad_norm: 3.2941 loss: 1.3003 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3003 2023/04/14 11:21:08 - mmengine - INFO - Epoch(train) [74][ 340/1879] lr: 2.0000e-03 eta: 5:10:45 time: 0.4248 data_time: 0.1233 memory: 6717 grad_norm: 3.3137 loss: 1.1039 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1039 2023/04/14 11:21:14 - mmengine - INFO - Epoch(train) [74][ 360/1879] lr: 2.0000e-03 eta: 5:10:37 time: 0.3386 data_time: 0.0580 memory: 6717 grad_norm: 3.3417 loss: 1.1984 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1984 2023/04/14 11:21:21 - mmengine - INFO - Epoch(train) [74][ 380/1879] lr: 2.0000e-03 eta: 5:10:29 time: 0.3576 data_time: 0.0695 memory: 6717 grad_norm: 3.3107 loss: 1.2608 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2608 2023/04/14 11:21:29 - mmengine - INFO - Epoch(train) [74][ 400/1879] lr: 2.0000e-03 eta: 5:10:22 time: 0.3641 data_time: 0.0561 memory: 6717 grad_norm: 3.3657 loss: 1.1140 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1140 2023/04/14 11:21:36 - mmengine - INFO - Epoch(train) [74][ 420/1879] lr: 2.0000e-03 eta: 5:10:15 time: 0.3734 data_time: 0.0413 memory: 6717 grad_norm: 3.3091 loss: 1.1501 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1501 2023/04/14 11:21:43 - mmengine - INFO - Epoch(train) [74][ 440/1879] lr: 2.0000e-03 eta: 5:10:07 time: 0.3496 data_time: 0.0130 memory: 6717 grad_norm: 3.2914 loss: 1.0945 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0945 2023/04/14 11:21:51 - mmengine - INFO - Epoch(train) [74][ 460/1879] lr: 2.0000e-03 eta: 5:10:00 time: 0.3798 data_time: 0.0159 memory: 6717 grad_norm: 3.3300 loss: 1.3985 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.3985 2023/04/14 11:21:57 - mmengine - INFO - Epoch(train) [74][ 480/1879] lr: 2.0000e-03 eta: 5:09:52 time: 0.3275 data_time: 0.0145 memory: 6717 grad_norm: 3.2852 loss: 1.2253 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2253 2023/04/14 11:22:06 - mmengine - INFO - Epoch(train) [74][ 500/1879] lr: 2.0000e-03 eta: 5:09:45 time: 0.4162 data_time: 0.0262 memory: 6717 grad_norm: 3.2807 loss: 1.0160 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0160 2023/04/14 11:22:12 - mmengine - INFO - Epoch(train) [74][ 520/1879] lr: 2.0000e-03 eta: 5:09:37 time: 0.3307 data_time: 0.0431 memory: 6717 grad_norm: 3.3198 loss: 1.3314 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3314 2023/04/14 11:22:21 - mmengine - INFO - Epoch(train) [74][ 540/1879] lr: 2.0000e-03 eta: 5:09:30 time: 0.4563 data_time: 0.0133 memory: 6717 grad_norm: 3.3686 loss: 1.2830 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2830 2023/04/14 11:22:28 - mmengine - INFO - Epoch(train) [74][ 560/1879] lr: 2.0000e-03 eta: 5:09:23 time: 0.3111 data_time: 0.0137 memory: 6717 grad_norm: 3.3412 loss: 1.3036 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3036 2023/04/14 11:22:36 - mmengine - INFO - Epoch(train) [74][ 580/1879] lr: 2.0000e-03 eta: 5:09:16 time: 0.4117 data_time: 0.0151 memory: 6717 grad_norm: 3.3422 loss: 1.2495 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2495 2023/04/14 11:22:43 - mmengine - INFO - Epoch(train) [74][ 600/1879] lr: 2.0000e-03 eta: 5:09:08 time: 0.3288 data_time: 0.0556 memory: 6717 grad_norm: 3.3607 loss: 1.1340 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1340 2023/04/14 11:22:51 - mmengine - INFO - Epoch(train) [74][ 620/1879] lr: 2.0000e-03 eta: 5:09:01 time: 0.4001 data_time: 0.0444 memory: 6717 grad_norm: 3.2755 loss: 1.1617 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1617 2023/04/14 11:22:57 - mmengine - INFO - Epoch(train) [74][ 640/1879] lr: 2.0000e-03 eta: 5:08:53 time: 0.3271 data_time: 0.0298 memory: 6717 grad_norm: 3.3385 loss: 1.0738 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0738 2023/04/14 11:23:05 - mmengine - INFO - Epoch(train) [74][ 660/1879] lr: 2.0000e-03 eta: 5:08:46 time: 0.3919 data_time: 0.0363 memory: 6717 grad_norm: 3.2917 loss: 1.1712 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1712 2023/04/14 11:23:12 - mmengine - INFO - Epoch(train) [74][ 680/1879] lr: 2.0000e-03 eta: 5:08:38 time: 0.3361 data_time: 0.0212 memory: 6717 grad_norm: 3.4123 loss: 1.1082 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1082 2023/04/14 11:23:20 - mmengine - INFO - Epoch(train) [74][ 700/1879] lr: 2.0000e-03 eta: 5:08:31 time: 0.4172 data_time: 0.0746 memory: 6717 grad_norm: 3.3334 loss: 1.2085 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2085 2023/04/14 11:23:27 - mmengine - INFO - Epoch(train) [74][ 720/1879] lr: 2.0000e-03 eta: 5:08:23 time: 0.3267 data_time: 0.0583 memory: 6717 grad_norm: 3.3284 loss: 1.0307 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0307 2023/04/14 11:23:34 - mmengine - INFO - Epoch(train) [74][ 740/1879] lr: 2.0000e-03 eta: 5:08:16 time: 0.3832 data_time: 0.0965 memory: 6717 grad_norm: 3.3031 loss: 1.3321 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3321 2023/04/14 11:23:41 - mmengine - INFO - Epoch(train) [74][ 760/1879] lr: 2.0000e-03 eta: 5:08:08 time: 0.3481 data_time: 0.0341 memory: 6717 grad_norm: 3.3610 loss: 1.1922 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1922 2023/04/14 11:23:49 - mmengine - INFO - Epoch(train) [74][ 780/1879] lr: 2.0000e-03 eta: 5:08:01 time: 0.4145 data_time: 0.0580 memory: 6717 grad_norm: 3.3668 loss: 1.4148 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4148 2023/04/14 11:23:56 - mmengine - INFO - Epoch(train) [74][ 800/1879] lr: 2.0000e-03 eta: 5:07:54 time: 0.3319 data_time: 0.0147 memory: 6717 grad_norm: 3.3019 loss: 1.1987 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.1987 2023/04/14 11:24:04 - mmengine - INFO - Epoch(train) [74][ 820/1879] lr: 2.0000e-03 eta: 5:07:47 time: 0.4067 data_time: 0.0155 memory: 6717 grad_norm: 3.3183 loss: 0.9592 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9592 2023/04/14 11:24:08 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 11:24:12 - mmengine - INFO - Epoch(train) [74][ 840/1879] lr: 2.0000e-03 eta: 5:07:39 time: 0.3647 data_time: 0.0131 memory: 6717 grad_norm: 3.6113 loss: 1.2419 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2419 2023/04/14 11:24:20 - mmengine - INFO - Epoch(train) [74][ 860/1879] lr: 2.0000e-03 eta: 5:07:32 time: 0.4157 data_time: 0.0157 memory: 6717 grad_norm: 3.3715 loss: 1.2586 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2586 2023/04/14 11:24:26 - mmengine - INFO - Epoch(train) [74][ 880/1879] lr: 2.0000e-03 eta: 5:07:24 time: 0.3163 data_time: 0.0127 memory: 6717 grad_norm: 3.3344 loss: 1.2267 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2267 2023/04/14 11:24:35 - mmengine - INFO - Epoch(train) [74][ 900/1879] lr: 2.0000e-03 eta: 5:07:17 time: 0.4296 data_time: 0.0182 memory: 6717 grad_norm: 3.2769 loss: 1.1451 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1451 2023/04/14 11:24:41 - mmengine - INFO - Epoch(train) [74][ 920/1879] lr: 2.0000e-03 eta: 5:07:10 time: 0.3211 data_time: 0.0254 memory: 6717 grad_norm: 3.3247 loss: 1.1360 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1360 2023/04/14 11:24:49 - mmengine - INFO - Epoch(train) [74][ 940/1879] lr: 2.0000e-03 eta: 5:07:02 time: 0.3955 data_time: 0.0205 memory: 6717 grad_norm: 3.2866 loss: 1.2528 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2528 2023/04/14 11:24:56 - mmengine - INFO - Epoch(train) [74][ 960/1879] lr: 2.0000e-03 eta: 5:06:55 time: 0.3189 data_time: 0.0289 memory: 6717 grad_norm: 3.3122 loss: 1.2874 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.2874 2023/04/14 11:25:04 - mmengine - INFO - Epoch(train) [74][ 980/1879] lr: 2.0000e-03 eta: 5:06:48 time: 0.4463 data_time: 0.0508 memory: 6717 grad_norm: 3.2743 loss: 1.4206 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.4206 2023/04/14 11:25:11 - mmengine - INFO - Epoch(train) [74][1000/1879] lr: 2.0000e-03 eta: 5:06:40 time: 0.3384 data_time: 0.0133 memory: 6717 grad_norm: 3.3267 loss: 1.2650 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2650 2023/04/14 11:25:19 - mmengine - INFO - Epoch(train) [74][1020/1879] lr: 2.0000e-03 eta: 5:06:33 time: 0.4024 data_time: 0.0177 memory: 6717 grad_norm: 3.3653 loss: 1.1052 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1052 2023/04/14 11:25:25 - mmengine - INFO - Epoch(train) [74][1040/1879] lr: 2.0000e-03 eta: 5:06:25 time: 0.3099 data_time: 0.0125 memory: 6717 grad_norm: 3.3628 loss: 1.4030 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4030 2023/04/14 11:25:34 - mmengine - INFO - Epoch(train) [74][1060/1879] lr: 2.0000e-03 eta: 5:06:18 time: 0.4028 data_time: 0.0167 memory: 6717 grad_norm: 3.3354 loss: 1.2256 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2256 2023/04/14 11:25:40 - mmengine - INFO - Epoch(train) [74][1080/1879] lr: 2.0000e-03 eta: 5:06:10 time: 0.3305 data_time: 0.0135 memory: 6717 grad_norm: 3.2362 loss: 1.0861 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0861 2023/04/14 11:25:48 - mmengine - INFO - Epoch(train) [74][1100/1879] lr: 2.0000e-03 eta: 5:06:03 time: 0.3792 data_time: 0.0160 memory: 6717 grad_norm: 3.2909 loss: 1.0783 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.0783 2023/04/14 11:25:55 - mmengine - INFO - Epoch(train) [74][1120/1879] lr: 2.0000e-03 eta: 5:05:55 time: 0.3554 data_time: 0.0135 memory: 6717 grad_norm: 3.3030 loss: 1.1093 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1093 2023/04/14 11:26:03 - mmengine - INFO - Epoch(train) [74][1140/1879] lr: 2.0000e-03 eta: 5:05:48 time: 0.3824 data_time: 0.0472 memory: 6717 grad_norm: 3.3358 loss: 0.9960 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9960 2023/04/14 11:26:10 - mmengine - INFO - Epoch(train) [74][1160/1879] lr: 2.0000e-03 eta: 5:05:41 time: 0.3607 data_time: 0.0569 memory: 6717 grad_norm: 3.2996 loss: 1.3551 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3551 2023/04/14 11:26:18 - mmengine - INFO - Epoch(train) [74][1180/1879] lr: 2.0000e-03 eta: 5:05:34 time: 0.4168 data_time: 0.1613 memory: 6717 grad_norm: 3.2044 loss: 1.1915 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1915 2023/04/14 11:26:24 - mmengine - INFO - Epoch(train) [74][1200/1879] lr: 2.0000e-03 eta: 5:05:26 time: 0.3176 data_time: 0.1436 memory: 6717 grad_norm: 3.3861 loss: 1.0508 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0508 2023/04/14 11:26:32 - mmengine - INFO - Epoch(train) [74][1220/1879] lr: 2.0000e-03 eta: 5:05:18 time: 0.3801 data_time: 0.2284 memory: 6717 grad_norm: 3.3373 loss: 1.3508 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3508 2023/04/14 11:26:39 - mmengine - INFO - Epoch(train) [74][1240/1879] lr: 2.0000e-03 eta: 5:05:11 time: 0.3410 data_time: 0.0733 memory: 6717 grad_norm: 3.2967 loss: 1.2439 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2439 2023/04/14 11:26:46 - mmengine - INFO - Epoch(train) [74][1260/1879] lr: 2.0000e-03 eta: 5:05:03 time: 0.3746 data_time: 0.0539 memory: 6717 grad_norm: 3.3469 loss: 1.2213 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2213 2023/04/14 11:26:54 - mmengine - INFO - Epoch(train) [74][1280/1879] lr: 2.0000e-03 eta: 5:04:56 time: 0.3843 data_time: 0.0288 memory: 6717 grad_norm: 3.2959 loss: 1.1861 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1861 2023/04/14 11:27:01 - mmengine - INFO - Epoch(train) [74][1300/1879] lr: 2.0000e-03 eta: 5:04:49 time: 0.3320 data_time: 0.0203 memory: 6717 grad_norm: 3.3355 loss: 1.0646 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0646 2023/04/14 11:27:09 - mmengine - INFO - Epoch(train) [74][1320/1879] lr: 2.0000e-03 eta: 5:04:41 time: 0.3981 data_time: 0.1095 memory: 6717 grad_norm: 3.2959 loss: 1.0497 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0497 2023/04/14 11:27:16 - mmengine - INFO - Epoch(train) [74][1340/1879] lr: 2.0000e-03 eta: 5:04:34 time: 0.3646 data_time: 0.0483 memory: 6717 grad_norm: 3.3506 loss: 1.0887 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0887 2023/04/14 11:27:23 - mmengine - INFO - Epoch(train) [74][1360/1879] lr: 2.0000e-03 eta: 5:04:26 time: 0.3603 data_time: 0.1057 memory: 6717 grad_norm: 3.4421 loss: 1.2119 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.2119 2023/04/14 11:27:31 - mmengine - INFO - Epoch(train) [74][1380/1879] lr: 2.0000e-03 eta: 5:04:19 time: 0.3955 data_time: 0.1204 memory: 6717 grad_norm: 3.3070 loss: 1.2899 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2899 2023/04/14 11:27:39 - mmengine - INFO - Epoch(train) [74][1400/1879] lr: 2.0000e-03 eta: 5:04:12 time: 0.3785 data_time: 0.1718 memory: 6717 grad_norm: 3.3598 loss: 1.1597 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1597 2023/04/14 11:27:46 - mmengine - INFO - Epoch(train) [74][1420/1879] lr: 2.0000e-03 eta: 5:04:04 time: 0.3717 data_time: 0.0952 memory: 6717 grad_norm: 3.3443 loss: 1.1037 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1037 2023/04/14 11:27:52 - mmengine - INFO - Epoch(train) [74][1440/1879] lr: 2.0000e-03 eta: 5:03:57 time: 0.3169 data_time: 0.0726 memory: 6717 grad_norm: 3.3577 loss: 1.1352 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1352 2023/04/14 11:28:00 - mmengine - INFO - Epoch(train) [74][1460/1879] lr: 2.0000e-03 eta: 5:03:49 time: 0.3919 data_time: 0.0129 memory: 6717 grad_norm: 3.3522 loss: 1.2653 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.2653 2023/04/14 11:28:08 - mmengine - INFO - Epoch(train) [74][1480/1879] lr: 2.0000e-03 eta: 5:03:42 time: 0.3790 data_time: 0.0135 memory: 6717 grad_norm: 3.2970 loss: 1.0686 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0686 2023/04/14 11:28:15 - mmengine - INFO - Epoch(train) [74][1500/1879] lr: 2.0000e-03 eta: 5:03:34 time: 0.3323 data_time: 0.0550 memory: 6717 grad_norm: 3.3135 loss: 1.2007 top1_acc: 0.6875 top5_acc: 0.6875 loss_cls: 1.2007 2023/04/14 11:28:22 - mmengine - INFO - Epoch(train) [74][1520/1879] lr: 2.0000e-03 eta: 5:03:27 time: 0.3975 data_time: 0.1592 memory: 6717 grad_norm: 3.2931 loss: 1.1870 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1870 2023/04/14 11:28:29 - mmengine - INFO - Epoch(train) [74][1540/1879] lr: 2.0000e-03 eta: 5:03:20 time: 0.3285 data_time: 0.1202 memory: 6717 grad_norm: 3.2256 loss: 1.2958 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2958 2023/04/14 11:28:37 - mmengine - INFO - Epoch(train) [74][1560/1879] lr: 2.0000e-03 eta: 5:03:12 time: 0.4005 data_time: 0.1684 memory: 6717 grad_norm: 3.4378 loss: 1.3452 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3452 2023/04/14 11:28:44 - mmengine - INFO - Epoch(train) [74][1580/1879] lr: 2.0000e-03 eta: 5:03:05 time: 0.3405 data_time: 0.1038 memory: 6717 grad_norm: 3.1967 loss: 1.1891 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1891 2023/04/14 11:28:52 - mmengine - INFO - Epoch(train) [74][1600/1879] lr: 2.0000e-03 eta: 5:02:58 time: 0.3928 data_time: 0.1094 memory: 6717 grad_norm: 3.3618 loss: 1.2140 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2140 2023/04/14 11:28:58 - mmengine - INFO - Epoch(train) [74][1620/1879] lr: 2.0000e-03 eta: 5:02:50 time: 0.3314 data_time: 0.0745 memory: 6717 grad_norm: 3.3211 loss: 1.2069 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2069 2023/04/14 11:29:07 - mmengine - INFO - Epoch(train) [74][1640/1879] lr: 2.0000e-03 eta: 5:02:43 time: 0.4449 data_time: 0.0578 memory: 6717 grad_norm: 3.3187 loss: 1.1790 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1790 2023/04/14 11:29:14 - mmengine - INFO - Epoch(train) [74][1660/1879] lr: 2.0000e-03 eta: 5:02:35 time: 0.3112 data_time: 0.0137 memory: 6717 grad_norm: 3.3078 loss: 1.2671 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2671 2023/04/14 11:29:22 - mmengine - INFO - Epoch(train) [74][1680/1879] lr: 2.0000e-03 eta: 5:02:28 time: 0.4021 data_time: 0.0137 memory: 6717 grad_norm: 3.2207 loss: 1.1028 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1028 2023/04/14 11:29:28 - mmengine - INFO - Epoch(train) [74][1700/1879] lr: 2.0000e-03 eta: 5:02:20 time: 0.3128 data_time: 0.0146 memory: 6717 grad_norm: 3.2703 loss: 1.2008 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.2008 2023/04/14 11:29:36 - mmengine - INFO - Epoch(train) [74][1720/1879] lr: 2.0000e-03 eta: 5:02:13 time: 0.3876 data_time: 0.0145 memory: 6717 grad_norm: 3.4110 loss: 1.1351 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1351 2023/04/14 11:29:43 - mmengine - INFO - Epoch(train) [74][1740/1879] lr: 2.0000e-03 eta: 5:02:06 time: 0.3732 data_time: 0.0551 memory: 6717 grad_norm: 3.3006 loss: 1.0531 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0531 2023/04/14 11:29:50 - mmengine - INFO - Epoch(train) [74][1760/1879] lr: 2.0000e-03 eta: 5:01:58 time: 0.3521 data_time: 0.0374 memory: 6717 grad_norm: 3.3589 loss: 1.1387 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1387 2023/04/14 11:29:58 - mmengine - INFO - Epoch(train) [74][1780/1879] lr: 2.0000e-03 eta: 5:01:51 time: 0.3846 data_time: 0.0963 memory: 6717 grad_norm: 3.3181 loss: 1.3055 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3055 2023/04/14 11:30:05 - mmengine - INFO - Epoch(train) [74][1800/1879] lr: 2.0000e-03 eta: 5:01:43 time: 0.3511 data_time: 0.0739 memory: 6717 grad_norm: 3.3268 loss: 1.1578 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1578 2023/04/14 11:30:13 - mmengine - INFO - Epoch(train) [74][1820/1879] lr: 2.0000e-03 eta: 5:01:36 time: 0.3926 data_time: 0.1848 memory: 6717 grad_norm: 3.3300 loss: 1.0997 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0997 2023/04/14 11:30:18 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 11:30:19 - mmengine - INFO - Epoch(train) [74][1840/1879] lr: 2.0000e-03 eta: 5:01:28 time: 0.3340 data_time: 0.1395 memory: 6717 grad_norm: 3.4041 loss: 1.2721 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2721 2023/04/14 11:30:28 - mmengine - INFO - Epoch(train) [74][1860/1879] lr: 2.0000e-03 eta: 5:01:21 time: 0.4213 data_time: 0.0840 memory: 6717 grad_norm: 3.3105 loss: 1.2096 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2096 2023/04/14 11:30:34 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 11:30:34 - mmengine - INFO - Epoch(train) [74][1879/1879] lr: 2.0000e-03 eta: 5:01:14 time: 0.3851 data_time: 0.0197 memory: 6717 grad_norm: 3.5190 loss: 1.1568 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.1568 2023/04/14 11:30:43 - mmengine - INFO - Epoch(val) [74][ 20/155] eta: 0:01:02 time: 0.4596 data_time: 0.4268 memory: 1391 2023/04/14 11:30:49 - mmengine - INFO - Epoch(val) [74][ 40/155] eta: 0:00:44 time: 0.3178 data_time: 0.2848 memory: 1391 2023/04/14 11:30:58 - mmengine - INFO - Epoch(val) [74][ 60/155] eta: 0:00:38 time: 0.4323 data_time: 0.3987 memory: 1391 2023/04/14 11:31:04 - mmengine - INFO - Epoch(val) [74][ 80/155] eta: 0:00:28 time: 0.3218 data_time: 0.2880 memory: 1391 2023/04/14 11:31:13 - mmengine - INFO - Epoch(val) [74][100/155] eta: 0:00:21 time: 0.4548 data_time: 0.4212 memory: 1391 2023/04/14 11:31:19 - mmengine - INFO - Epoch(val) [74][120/155] eta: 0:00:13 time: 0.2981 data_time: 0.2643 memory: 1391 2023/04/14 11:31:28 - mmengine - INFO - Epoch(val) [74][140/155] eta: 0:00:05 time: 0.4455 data_time: 0.4119 memory: 1391 2023/04/14 11:31:35 - mmengine - INFO - Epoch(val) [74][155/155] acc/top1: 0.6626 acc/top5: 0.8735 acc/mean1: 0.6625 data_time: 0.3737 time: 0.4063 2023/04/14 11:31:45 - mmengine - INFO - Epoch(train) [75][ 20/1879] lr: 2.0000e-03 eta: 5:01:07 time: 0.4854 data_time: 0.2878 memory: 6717 grad_norm: 3.2881 loss: 1.0617 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0617 2023/04/14 11:31:52 - mmengine - INFO - Epoch(train) [75][ 40/1879] lr: 2.0000e-03 eta: 5:00:59 time: 0.3233 data_time: 0.1037 memory: 6717 grad_norm: 3.3490 loss: 1.2745 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2745 2023/04/14 11:32:00 - mmengine - INFO - Epoch(train) [75][ 60/1879] lr: 2.0000e-03 eta: 5:00:52 time: 0.4187 data_time: 0.1490 memory: 6717 grad_norm: 3.3368 loss: 1.3328 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3328 2023/04/14 11:32:06 - mmengine - INFO - Epoch(train) [75][ 80/1879] lr: 2.0000e-03 eta: 5:00:45 time: 0.3230 data_time: 0.0784 memory: 6717 grad_norm: 3.2615 loss: 1.2360 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2360 2023/04/14 11:32:15 - mmengine - INFO - Epoch(train) [75][ 100/1879] lr: 2.0000e-03 eta: 5:00:38 time: 0.4137 data_time: 0.1488 memory: 6717 grad_norm: 3.2439 loss: 1.1637 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1637 2023/04/14 11:32:22 - mmengine - INFO - Epoch(train) [75][ 120/1879] lr: 2.0000e-03 eta: 5:00:30 time: 0.3465 data_time: 0.1136 memory: 6717 grad_norm: 3.2654 loss: 1.0673 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0673 2023/04/14 11:32:30 - mmengine - INFO - Epoch(train) [75][ 140/1879] lr: 2.0000e-03 eta: 5:00:23 time: 0.4170 data_time: 0.1370 memory: 6717 grad_norm: 3.3178 loss: 1.2755 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2755 2023/04/14 11:32:37 - mmengine - INFO - Epoch(train) [75][ 160/1879] lr: 2.0000e-03 eta: 5:00:15 time: 0.3330 data_time: 0.1313 memory: 6717 grad_norm: 3.3114 loss: 1.1203 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1203 2023/04/14 11:32:45 - mmengine - INFO - Epoch(train) [75][ 180/1879] lr: 2.0000e-03 eta: 5:00:08 time: 0.4069 data_time: 0.1326 memory: 6717 grad_norm: 3.2558 loss: 1.1069 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1069 2023/04/14 11:32:51 - mmengine - INFO - Epoch(train) [75][ 200/1879] lr: 2.0000e-03 eta: 5:00:01 time: 0.3323 data_time: 0.1122 memory: 6717 grad_norm: 3.3336 loss: 1.0238 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0238 2023/04/14 11:33:00 - mmengine - INFO - Epoch(train) [75][ 220/1879] lr: 2.0000e-03 eta: 4:59:53 time: 0.4147 data_time: 0.0544 memory: 6717 grad_norm: 3.3072 loss: 1.0323 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0323 2023/04/14 11:33:06 - mmengine - INFO - Epoch(train) [75][ 240/1879] lr: 2.0000e-03 eta: 4:59:46 time: 0.3355 data_time: 0.0125 memory: 6717 grad_norm: 3.3567 loss: 1.0829 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0829 2023/04/14 11:33:14 - mmengine - INFO - Epoch(train) [75][ 260/1879] lr: 2.0000e-03 eta: 4:59:39 time: 0.3950 data_time: 0.0157 memory: 6717 grad_norm: 3.3617 loss: 1.0490 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0490 2023/04/14 11:33:21 - mmengine - INFO - Epoch(train) [75][ 280/1879] lr: 2.0000e-03 eta: 4:59:31 time: 0.3264 data_time: 0.0125 memory: 6717 grad_norm: 3.3167 loss: 1.1190 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1190 2023/04/14 11:33:29 - mmengine - INFO - Epoch(train) [75][ 300/1879] lr: 2.0000e-03 eta: 4:59:24 time: 0.4046 data_time: 0.0153 memory: 6717 grad_norm: 3.4022 loss: 1.1731 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1731 2023/04/14 11:33:35 - mmengine - INFO - Epoch(train) [75][ 320/1879] lr: 2.0000e-03 eta: 4:59:16 time: 0.3142 data_time: 0.0128 memory: 6717 grad_norm: 3.3991 loss: 1.2553 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2553 2023/04/14 11:33:43 - mmengine - INFO - Epoch(train) [75][ 340/1879] lr: 2.0000e-03 eta: 4:59:09 time: 0.4107 data_time: 0.0159 memory: 6717 grad_norm: 3.3796 loss: 1.1081 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1081 2023/04/14 11:33:50 - mmengine - INFO - Epoch(train) [75][ 360/1879] lr: 2.0000e-03 eta: 4:59:01 time: 0.3494 data_time: 0.0131 memory: 6717 grad_norm: 3.3511 loss: 1.1790 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1790 2023/04/14 11:33:58 - mmengine - INFO - Epoch(train) [75][ 380/1879] lr: 2.0000e-03 eta: 4:58:54 time: 0.3842 data_time: 0.0150 memory: 6717 grad_norm: 3.2707 loss: 1.1827 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1827 2023/04/14 11:34:05 - mmengine - INFO - Epoch(train) [75][ 400/1879] lr: 2.0000e-03 eta: 4:58:46 time: 0.3555 data_time: 0.0124 memory: 6717 grad_norm: 3.3393 loss: 1.1744 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1744 2023/04/14 11:34:13 - mmengine - INFO - Epoch(train) [75][ 420/1879] lr: 2.0000e-03 eta: 4:58:39 time: 0.3724 data_time: 0.0147 memory: 6717 grad_norm: 3.3059 loss: 1.1077 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1077 2023/04/14 11:34:19 - mmengine - INFO - Epoch(train) [75][ 440/1879] lr: 2.0000e-03 eta: 4:58:31 time: 0.3299 data_time: 0.0178 memory: 6717 grad_norm: 3.4144 loss: 1.1550 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1550 2023/04/14 11:34:28 - mmengine - INFO - Epoch(train) [75][ 460/1879] lr: 2.0000e-03 eta: 4:58:24 time: 0.4169 data_time: 0.0173 memory: 6717 grad_norm: 3.2879 loss: 1.1843 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1843 2023/04/14 11:34:34 - mmengine - INFO - Epoch(train) [75][ 480/1879] lr: 2.0000e-03 eta: 4:58:17 time: 0.3379 data_time: 0.0129 memory: 6717 grad_norm: 3.3503 loss: 1.1675 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1675 2023/04/14 11:34:43 - mmengine - INFO - Epoch(train) [75][ 500/1879] lr: 2.0000e-03 eta: 4:58:10 time: 0.4227 data_time: 0.0146 memory: 6717 grad_norm: 3.3576 loss: 1.2304 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2304 2023/04/14 11:34:49 - mmengine - INFO - Epoch(train) [75][ 520/1879] lr: 2.0000e-03 eta: 4:58:02 time: 0.3233 data_time: 0.0142 memory: 6717 grad_norm: 3.3662 loss: 1.1162 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.1162 2023/04/14 11:34:58 - mmengine - INFO - Epoch(train) [75][ 540/1879] lr: 2.0000e-03 eta: 4:57:55 time: 0.4146 data_time: 0.0137 memory: 6717 grad_norm: 3.3644 loss: 1.1734 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1734 2023/04/14 11:35:04 - mmengine - INFO - Epoch(train) [75][ 560/1879] lr: 2.0000e-03 eta: 4:57:47 time: 0.3390 data_time: 0.0140 memory: 6717 grad_norm: 3.2929 loss: 1.0588 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0588 2023/04/14 11:35:13 - mmengine - INFO - Epoch(train) [75][ 580/1879] lr: 2.0000e-03 eta: 4:57:40 time: 0.4258 data_time: 0.0146 memory: 6717 grad_norm: 3.3878 loss: 1.0943 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.0943 2023/04/14 11:35:19 - mmengine - INFO - Epoch(train) [75][ 600/1879] lr: 2.0000e-03 eta: 4:57:32 time: 0.3175 data_time: 0.0133 memory: 6717 grad_norm: 3.3266 loss: 1.3033 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3033 2023/04/14 11:35:28 - mmengine - INFO - Epoch(train) [75][ 620/1879] lr: 2.0000e-03 eta: 4:57:25 time: 0.4326 data_time: 0.0134 memory: 6717 grad_norm: 3.3419 loss: 1.1888 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1888 2023/04/14 11:35:34 - mmengine - INFO - Epoch(train) [75][ 640/1879] lr: 2.0000e-03 eta: 4:57:18 time: 0.3090 data_time: 0.0157 memory: 6717 grad_norm: 3.2880 loss: 1.1424 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1424 2023/04/14 11:35:42 - mmengine - INFO - Epoch(train) [75][ 660/1879] lr: 2.0000e-03 eta: 4:57:10 time: 0.3989 data_time: 0.0141 memory: 6717 grad_norm: 3.2662 loss: 1.1103 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1103 2023/04/14 11:35:48 - mmengine - INFO - Epoch(train) [75][ 680/1879] lr: 2.0000e-03 eta: 4:57:03 time: 0.3163 data_time: 0.0146 memory: 6717 grad_norm: 3.3447 loss: 1.2175 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2175 2023/04/14 11:35:56 - mmengine - INFO - Epoch(train) [75][ 700/1879] lr: 2.0000e-03 eta: 4:56:55 time: 0.3938 data_time: 0.0149 memory: 6717 grad_norm: 3.3734 loss: 1.2005 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.2005 2023/04/14 11:36:03 - mmengine - INFO - Epoch(train) [75][ 720/1879] lr: 2.0000e-03 eta: 4:56:48 time: 0.3108 data_time: 0.0147 memory: 6717 grad_norm: 3.3579 loss: 1.2663 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2663 2023/04/14 11:36:10 - mmengine - INFO - Epoch(train) [75][ 740/1879] lr: 2.0000e-03 eta: 4:56:40 time: 0.3792 data_time: 0.0161 memory: 6717 grad_norm: 3.2938 loss: 1.0844 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0844 2023/04/14 11:36:17 - mmengine - INFO - Epoch(train) [75][ 760/1879] lr: 2.0000e-03 eta: 4:56:33 time: 0.3292 data_time: 0.0136 memory: 6717 grad_norm: 3.4187 loss: 1.2293 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2293 2023/04/14 11:36:27 - mmengine - INFO - Epoch(train) [75][ 780/1879] lr: 2.0000e-03 eta: 4:56:26 time: 0.5189 data_time: 0.0166 memory: 6717 grad_norm: 3.3699 loss: 0.9687 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9687 2023/04/14 11:36:36 - mmengine - INFO - Epoch(train) [75][ 800/1879] lr: 2.0000e-03 eta: 4:56:20 time: 0.4673 data_time: 0.0118 memory: 6717 grad_norm: 3.3618 loss: 1.1984 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1984 2023/04/14 11:36:43 - mmengine - INFO - Epoch(train) [75][ 820/1879] lr: 2.0000e-03 eta: 4:56:12 time: 0.3392 data_time: 0.0134 memory: 6717 grad_norm: 3.2652 loss: 1.1127 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1127 2023/04/14 11:36:52 - mmengine - INFO - Epoch(train) [75][ 840/1879] lr: 2.0000e-03 eta: 4:56:05 time: 0.4255 data_time: 0.0139 memory: 6717 grad_norm: 3.2336 loss: 1.1334 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1334 2023/04/14 11:36:59 - mmengine - INFO - Epoch(train) [75][ 860/1879] lr: 2.0000e-03 eta: 4:55:57 time: 0.3481 data_time: 0.0133 memory: 6717 grad_norm: 3.4199 loss: 1.3044 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3044 2023/04/14 11:37:07 - mmengine - INFO - Epoch(train) [75][ 880/1879] lr: 2.0000e-03 eta: 4:55:50 time: 0.3941 data_time: 0.0141 memory: 6717 grad_norm: 3.2930 loss: 1.0723 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0723 2023/04/14 11:37:12 - mmengine - INFO - Epoch(train) [75][ 900/1879] lr: 2.0000e-03 eta: 4:55:42 time: 0.2931 data_time: 0.0159 memory: 6717 grad_norm: 3.4138 loss: 1.3365 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3365 2023/04/14 11:37:21 - mmengine - INFO - Epoch(train) [75][ 920/1879] lr: 2.0000e-03 eta: 4:55:35 time: 0.4113 data_time: 0.0138 memory: 6717 grad_norm: 3.4184 loss: 1.1639 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1639 2023/04/14 11:37:27 - mmengine - INFO - Epoch(train) [75][ 940/1879] lr: 2.0000e-03 eta: 4:55:27 time: 0.3231 data_time: 0.0139 memory: 6717 grad_norm: 3.3356 loss: 1.3033 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3033 2023/04/14 11:37:33 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 11:37:36 - mmengine - INFO - Epoch(train) [75][ 960/1879] lr: 2.0000e-03 eta: 4:55:20 time: 0.4476 data_time: 0.0147 memory: 6717 grad_norm: 3.3533 loss: 1.0833 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0833 2023/04/14 11:37:43 - mmengine - INFO - Epoch(train) [75][ 980/1879] lr: 2.0000e-03 eta: 4:55:13 time: 0.3266 data_time: 0.0139 memory: 6717 grad_norm: 3.3590 loss: 1.2078 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2078 2023/04/14 11:37:51 - mmengine - INFO - Epoch(train) [75][1000/1879] lr: 2.0000e-03 eta: 4:55:06 time: 0.4077 data_time: 0.0138 memory: 6717 grad_norm: 3.2510 loss: 1.1139 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1139 2023/04/14 11:37:57 - mmengine - INFO - Epoch(train) [75][1020/1879] lr: 2.0000e-03 eta: 4:54:58 time: 0.2989 data_time: 0.0145 memory: 6717 grad_norm: 3.3947 loss: 1.0735 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0735 2023/04/14 11:38:06 - mmengine - INFO - Epoch(train) [75][1040/1879] lr: 2.0000e-03 eta: 4:54:51 time: 0.4368 data_time: 0.0138 memory: 6717 grad_norm: 3.3724 loss: 1.3186 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3186 2023/04/14 11:38:12 - mmengine - INFO - Epoch(train) [75][1060/1879] lr: 2.0000e-03 eta: 4:54:43 time: 0.3324 data_time: 0.0139 memory: 6717 grad_norm: 3.2091 loss: 1.0509 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0509 2023/04/14 11:38:20 - mmengine - INFO - Epoch(train) [75][1080/1879] lr: 2.0000e-03 eta: 4:54:36 time: 0.3732 data_time: 0.0141 memory: 6717 grad_norm: 3.3222 loss: 1.1410 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1410 2023/04/14 11:38:26 - mmengine - INFO - Epoch(train) [75][1100/1879] lr: 2.0000e-03 eta: 4:54:28 time: 0.2994 data_time: 0.0417 memory: 6717 grad_norm: 3.4138 loss: 1.0764 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0764 2023/04/14 11:38:33 - mmengine - INFO - Epoch(train) [75][1120/1879] lr: 2.0000e-03 eta: 4:54:21 time: 0.3867 data_time: 0.0779 memory: 6717 grad_norm: 3.3363 loss: 1.2094 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2094 2023/04/14 11:38:41 - mmengine - INFO - Epoch(train) [75][1140/1879] lr: 2.0000e-03 eta: 4:54:13 time: 0.3723 data_time: 0.0550 memory: 6717 grad_norm: 3.3812 loss: 1.2462 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2462 2023/04/14 11:38:49 - mmengine - INFO - Epoch(train) [75][1160/1879] lr: 2.0000e-03 eta: 4:54:06 time: 0.3908 data_time: 0.0545 memory: 6717 grad_norm: 3.3104 loss: 1.1337 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1337 2023/04/14 11:38:56 - mmengine - INFO - Epoch(train) [75][1180/1879] lr: 2.0000e-03 eta: 4:53:58 time: 0.3464 data_time: 0.0775 memory: 6717 grad_norm: 3.3023 loss: 1.1034 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1034 2023/04/14 11:39:03 - mmengine - INFO - Epoch(train) [75][1200/1879] lr: 2.0000e-03 eta: 4:53:51 time: 0.3867 data_time: 0.1149 memory: 6717 grad_norm: 3.3768 loss: 1.0328 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0328 2023/04/14 11:39:10 - mmengine - INFO - Epoch(train) [75][1220/1879] lr: 2.0000e-03 eta: 4:53:44 time: 0.3405 data_time: 0.0169 memory: 6717 grad_norm: 3.4270 loss: 1.2940 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2940 2023/04/14 11:39:18 - mmengine - INFO - Epoch(train) [75][1240/1879] lr: 2.0000e-03 eta: 4:53:36 time: 0.4034 data_time: 0.0146 memory: 6717 grad_norm: 3.3322 loss: 1.1633 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1633 2023/04/14 11:39:26 - mmengine - INFO - Epoch(train) [75][1260/1879] lr: 2.0000e-03 eta: 4:53:29 time: 0.3688 data_time: 0.0127 memory: 6717 grad_norm: 3.3578 loss: 1.0280 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0280 2023/04/14 11:39:33 - mmengine - INFO - Epoch(train) [75][1280/1879] lr: 2.0000e-03 eta: 4:53:22 time: 0.3653 data_time: 0.0140 memory: 6717 grad_norm: 3.4059 loss: 1.1611 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1611 2023/04/14 11:39:41 - mmengine - INFO - Epoch(train) [75][1300/1879] lr: 2.0000e-03 eta: 4:53:14 time: 0.4009 data_time: 0.0144 memory: 6717 grad_norm: 3.3844 loss: 1.2713 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2713 2023/04/14 11:39:48 - mmengine - INFO - Epoch(train) [75][1320/1879] lr: 2.0000e-03 eta: 4:53:07 time: 0.3387 data_time: 0.0134 memory: 6717 grad_norm: 3.3075 loss: 1.2384 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2384 2023/04/14 11:39:55 - mmengine - INFO - Epoch(train) [75][1340/1879] lr: 2.0000e-03 eta: 4:52:59 time: 0.3859 data_time: 0.0141 memory: 6717 grad_norm: 3.2299 loss: 1.1781 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1781 2023/04/14 11:40:03 - mmengine - INFO - Epoch(train) [75][1360/1879] lr: 2.0000e-03 eta: 4:52:52 time: 0.3717 data_time: 0.0144 memory: 6717 grad_norm: 3.3298 loss: 1.0947 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.0947 2023/04/14 11:40:10 - mmengine - INFO - Epoch(train) [75][1380/1879] lr: 2.0000e-03 eta: 4:52:44 time: 0.3378 data_time: 0.0151 memory: 6717 grad_norm: 3.3103 loss: 1.1792 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1792 2023/04/14 11:40:17 - mmengine - INFO - Epoch(train) [75][1400/1879] lr: 2.0000e-03 eta: 4:52:37 time: 0.3725 data_time: 0.0138 memory: 6717 grad_norm: 3.3127 loss: 1.1546 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1546 2023/04/14 11:40:24 - mmengine - INFO - Epoch(train) [75][1420/1879] lr: 2.0000e-03 eta: 4:52:30 time: 0.3646 data_time: 0.0153 memory: 6717 grad_norm: 3.3828 loss: 1.0871 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0871 2023/04/14 11:40:32 - mmengine - INFO - Epoch(train) [75][1440/1879] lr: 2.0000e-03 eta: 4:52:22 time: 0.3918 data_time: 0.0142 memory: 6717 grad_norm: 3.2353 loss: 1.2136 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2136 2023/04/14 11:40:39 - mmengine - INFO - Epoch(train) [75][1460/1879] lr: 2.0000e-03 eta: 4:52:15 time: 0.3559 data_time: 0.0129 memory: 6717 grad_norm: 3.3318 loss: 1.2552 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2552 2023/04/14 11:40:46 - mmengine - INFO - Epoch(train) [75][1480/1879] lr: 2.0000e-03 eta: 4:52:07 time: 0.3478 data_time: 0.0146 memory: 6717 grad_norm: 3.3382 loss: 1.0574 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0574 2023/04/14 11:40:54 - mmengine - INFO - Epoch(train) [75][1500/1879] lr: 2.0000e-03 eta: 4:52:00 time: 0.4070 data_time: 0.0137 memory: 6717 grad_norm: 3.3227 loss: 1.0951 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0951 2023/04/14 11:41:01 - mmengine - INFO - Epoch(train) [75][1520/1879] lr: 2.0000e-03 eta: 4:51:53 time: 0.3370 data_time: 0.0137 memory: 6717 grad_norm: 3.3641 loss: 1.0441 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0441 2023/04/14 11:41:09 - mmengine - INFO - Epoch(train) [75][1540/1879] lr: 2.0000e-03 eta: 4:51:45 time: 0.4075 data_time: 0.0160 memory: 6717 grad_norm: 3.3167 loss: 1.1357 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1357 2023/04/14 11:41:16 - mmengine - INFO - Epoch(train) [75][1560/1879] lr: 2.0000e-03 eta: 4:51:38 time: 0.3088 data_time: 0.0127 memory: 6717 grad_norm: 3.3635 loss: 1.1887 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1887 2023/04/14 11:41:24 - mmengine - INFO - Epoch(train) [75][1580/1879] lr: 2.0000e-03 eta: 4:51:31 time: 0.4222 data_time: 0.0145 memory: 6717 grad_norm: 3.4333 loss: 1.3546 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.3546 2023/04/14 11:41:30 - mmengine - INFO - Epoch(train) [75][1600/1879] lr: 2.0000e-03 eta: 4:51:23 time: 0.3077 data_time: 0.0143 memory: 6717 grad_norm: 3.3524 loss: 1.0926 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0926 2023/04/14 11:41:39 - mmengine - INFO - Epoch(train) [75][1620/1879] lr: 2.0000e-03 eta: 4:51:16 time: 0.4195 data_time: 0.0162 memory: 6717 grad_norm: 3.2408 loss: 1.1496 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1496 2023/04/14 11:41:45 - mmengine - INFO - Epoch(train) [75][1640/1879] lr: 2.0000e-03 eta: 4:51:08 time: 0.3030 data_time: 0.0131 memory: 6717 grad_norm: 3.4012 loss: 1.3771 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3771 2023/04/14 11:41:52 - mmengine - INFO - Epoch(train) [75][1660/1879] lr: 2.0000e-03 eta: 4:51:00 time: 0.3854 data_time: 0.0151 memory: 6717 grad_norm: 3.2337 loss: 0.9600 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9600 2023/04/14 11:41:59 - mmengine - INFO - Epoch(train) [75][1680/1879] lr: 2.0000e-03 eta: 4:50:53 time: 0.3270 data_time: 0.0137 memory: 6717 grad_norm: 3.3337 loss: 1.3439 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3439 2023/04/14 11:42:06 - mmengine - INFO - Epoch(train) [75][1700/1879] lr: 2.0000e-03 eta: 4:50:45 time: 0.3597 data_time: 0.0178 memory: 6717 grad_norm: 3.2887 loss: 1.1447 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1447 2023/04/14 11:42:13 - mmengine - INFO - Epoch(train) [75][1720/1879] lr: 2.0000e-03 eta: 4:50:38 time: 0.3693 data_time: 0.0131 memory: 6717 grad_norm: 3.4472 loss: 1.2712 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2712 2023/04/14 11:42:21 - mmengine - INFO - Epoch(train) [75][1740/1879] lr: 2.0000e-03 eta: 4:50:31 time: 0.3744 data_time: 0.0145 memory: 6717 grad_norm: 3.4122 loss: 1.2149 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2149 2023/04/14 11:42:28 - mmengine - INFO - Epoch(train) [75][1760/1879] lr: 2.0000e-03 eta: 4:50:23 time: 0.3538 data_time: 0.0245 memory: 6717 grad_norm: 3.3676 loss: 1.1939 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1939 2023/04/14 11:42:36 - mmengine - INFO - Epoch(train) [75][1780/1879] lr: 2.0000e-03 eta: 4:50:16 time: 0.3876 data_time: 0.0403 memory: 6717 grad_norm: 3.3086 loss: 1.2432 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2432 2023/04/14 11:42:43 - mmengine - INFO - Epoch(train) [75][1800/1879] lr: 2.0000e-03 eta: 4:50:08 time: 0.3796 data_time: 0.0395 memory: 6717 grad_norm: 3.4278 loss: 1.1036 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.1036 2023/04/14 11:42:51 - mmengine - INFO - Epoch(train) [75][1820/1879] lr: 2.0000e-03 eta: 4:50:01 time: 0.3680 data_time: 0.0138 memory: 6717 grad_norm: 3.4058 loss: 1.2183 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2183 2023/04/14 11:42:58 - mmengine - INFO - Epoch(train) [75][1840/1879] lr: 2.0000e-03 eta: 4:49:54 time: 0.3623 data_time: 0.0487 memory: 6717 grad_norm: 3.3263 loss: 1.3242 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3242 2023/04/14 11:43:05 - mmengine - INFO - Epoch(train) [75][1860/1879] lr: 2.0000e-03 eta: 4:49:46 time: 0.3533 data_time: 0.0180 memory: 6717 grad_norm: 3.3740 loss: 1.3180 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3180 2023/04/14 11:43:11 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 11:43:11 - mmengine - INFO - Epoch(train) [75][1879/1879] lr: 2.0000e-03 eta: 4:49:39 time: 0.3305 data_time: 0.0145 memory: 6717 grad_norm: 3.5264 loss: 1.0594 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 1.0594 2023/04/14 11:43:11 - mmengine - INFO - Saving checkpoint at 75 epochs 2023/04/14 11:43:21 - mmengine - INFO - Epoch(val) [75][ 20/155] eta: 0:01:02 time: 0.4602 data_time: 0.4271 memory: 1391 2023/04/14 11:43:28 - mmengine - INFO - Epoch(val) [75][ 40/155] eta: 0:00:45 time: 0.3228 data_time: 0.2895 memory: 1391 2023/04/14 11:43:36 - mmengine - INFO - Epoch(val) [75][ 60/155] eta: 0:00:38 time: 0.4243 data_time: 0.3906 memory: 1391 2023/04/14 11:43:42 - mmengine - INFO - Epoch(val) [75][ 80/155] eta: 0:00:28 time: 0.3187 data_time: 0.2848 memory: 1391 2023/04/14 11:43:51 - mmengine - INFO - Epoch(val) [75][100/155] eta: 0:00:21 time: 0.4537 data_time: 0.4206 memory: 1391 2023/04/14 11:43:57 - mmengine - INFO - Epoch(val) [75][120/155] eta: 0:00:13 time: 0.2957 data_time: 0.2618 memory: 1391 2023/04/14 11:44:06 - mmengine - INFO - Epoch(val) [75][140/155] eta: 0:00:05 time: 0.4428 data_time: 0.4098 memory: 1391 2023/04/14 11:44:13 - mmengine - INFO - Epoch(val) [75][155/155] acc/top1: 0.6642 acc/top5: 0.8738 acc/mean1: 0.6641 data_time: 0.3754 time: 0.4073 2023/04/14 11:44:23 - mmengine - INFO - Epoch(train) [76][ 20/1879] lr: 2.0000e-03 eta: 4:49:32 time: 0.4923 data_time: 0.2135 memory: 6717 grad_norm: 3.2592 loss: 1.1729 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1729 2023/04/14 11:44:29 - mmengine - INFO - Epoch(train) [76][ 40/1879] lr: 2.0000e-03 eta: 4:49:24 time: 0.3273 data_time: 0.0141 memory: 6717 grad_norm: 3.3803 loss: 1.2260 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2260 2023/04/14 11:44:38 - mmengine - INFO - Epoch(train) [76][ 60/1879] lr: 2.0000e-03 eta: 4:49:17 time: 0.4097 data_time: 0.0152 memory: 6717 grad_norm: 3.3268 loss: 1.2155 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2155 2023/04/14 11:44:43 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 11:44:44 - mmengine - INFO - Epoch(train) [76][ 80/1879] lr: 2.0000e-03 eta: 4:49:10 time: 0.3206 data_time: 0.0136 memory: 6717 grad_norm: 3.2929 loss: 1.0062 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0062 2023/04/14 11:44:53 - mmengine - INFO - Epoch(train) [76][ 100/1879] lr: 2.0000e-03 eta: 4:49:03 time: 0.4202 data_time: 0.0151 memory: 6717 grad_norm: 3.3396 loss: 1.2551 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2551 2023/04/14 11:44:59 - mmengine - INFO - Epoch(train) [76][ 120/1879] lr: 2.0000e-03 eta: 4:48:55 time: 0.3247 data_time: 0.0155 memory: 6717 grad_norm: 3.3832 loss: 1.2667 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2667 2023/04/14 11:45:07 - mmengine - INFO - Epoch(train) [76][ 140/1879] lr: 2.0000e-03 eta: 4:48:48 time: 0.4020 data_time: 0.0143 memory: 6717 grad_norm: 3.3589 loss: 1.1548 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1548 2023/04/14 11:45:15 - mmengine - INFO - Epoch(train) [76][ 160/1879] lr: 2.0000e-03 eta: 4:48:40 time: 0.3779 data_time: 0.0139 memory: 6717 grad_norm: 3.4088 loss: 1.1925 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1925 2023/04/14 11:45:23 - mmengine - INFO - Epoch(train) [76][ 180/1879] lr: 2.0000e-03 eta: 4:48:33 time: 0.4018 data_time: 0.0139 memory: 6717 grad_norm: 3.3901 loss: 1.2343 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2343 2023/04/14 11:45:30 - mmengine - INFO - Epoch(train) [76][ 200/1879] lr: 2.0000e-03 eta: 4:48:26 time: 0.3421 data_time: 0.0149 memory: 6717 grad_norm: 3.3248 loss: 1.1060 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1060 2023/04/14 11:45:38 - mmengine - INFO - Epoch(train) [76][ 220/1879] lr: 2.0000e-03 eta: 4:48:18 time: 0.4234 data_time: 0.0150 memory: 6717 grad_norm: 3.3504 loss: 1.1686 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1686 2023/04/14 11:45:45 - mmengine - INFO - Epoch(train) [76][ 240/1879] lr: 2.0000e-03 eta: 4:48:11 time: 0.3302 data_time: 0.0142 memory: 6717 grad_norm: 3.4477 loss: 1.2465 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2465 2023/04/14 11:45:52 - mmengine - INFO - Epoch(train) [76][ 260/1879] lr: 2.0000e-03 eta: 4:48:03 time: 0.3816 data_time: 0.0147 memory: 6717 grad_norm: 3.3296 loss: 1.2744 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2744 2023/04/14 11:45:58 - mmengine - INFO - Epoch(train) [76][ 280/1879] lr: 2.0000e-03 eta: 4:47:56 time: 0.3115 data_time: 0.0148 memory: 6717 grad_norm: 3.4338 loss: 1.2441 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2441 2023/04/14 11:46:07 - mmengine - INFO - Epoch(train) [76][ 300/1879] lr: 2.0000e-03 eta: 4:47:49 time: 0.4165 data_time: 0.0156 memory: 6717 grad_norm: 3.3182 loss: 1.1937 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1937 2023/04/14 11:46:13 - mmengine - INFO - Epoch(train) [76][ 320/1879] lr: 2.0000e-03 eta: 4:47:41 time: 0.3179 data_time: 0.0137 memory: 6717 grad_norm: 3.2290 loss: 1.1408 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1408 2023/04/14 11:46:22 - mmengine - INFO - Epoch(train) [76][ 340/1879] lr: 2.0000e-03 eta: 4:47:34 time: 0.4189 data_time: 0.0150 memory: 6717 grad_norm: 3.4232 loss: 1.3793 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3793 2023/04/14 11:46:31 - mmengine - INFO - Epoch(train) [76][ 360/1879] lr: 2.0000e-03 eta: 4:47:27 time: 0.4962 data_time: 0.0165 memory: 6717 grad_norm: 3.4426 loss: 1.1822 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1822 2023/04/14 11:46:40 - mmengine - INFO - Epoch(train) [76][ 380/1879] lr: 2.0000e-03 eta: 4:47:20 time: 0.4487 data_time: 0.0233 memory: 6717 grad_norm: 3.3693 loss: 1.2538 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2538 2023/04/14 11:46:49 - mmengine - INFO - Epoch(train) [76][ 400/1879] lr: 2.0000e-03 eta: 4:47:13 time: 0.4138 data_time: 0.0148 memory: 6717 grad_norm: 3.2827 loss: 1.0824 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0824 2023/04/14 11:46:55 - mmengine - INFO - Epoch(train) [76][ 420/1879] lr: 2.0000e-03 eta: 4:47:06 time: 0.3327 data_time: 0.0135 memory: 6717 grad_norm: 3.3350 loss: 1.1506 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1506 2023/04/14 11:47:04 - mmengine - INFO - Epoch(train) [76][ 440/1879] lr: 2.0000e-03 eta: 4:46:59 time: 0.4212 data_time: 0.0137 memory: 6717 grad_norm: 3.3001 loss: 1.2877 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2877 2023/04/14 11:47:10 - mmengine - INFO - Epoch(train) [76][ 460/1879] lr: 2.0000e-03 eta: 4:46:51 time: 0.3087 data_time: 0.0144 memory: 6717 grad_norm: 3.2458 loss: 1.1646 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1646 2023/04/14 11:47:18 - mmengine - INFO - Epoch(train) [76][ 480/1879] lr: 2.0000e-03 eta: 4:46:43 time: 0.3970 data_time: 0.0143 memory: 6717 grad_norm: 3.2907 loss: 1.2607 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2607 2023/04/14 11:47:24 - mmengine - INFO - Epoch(train) [76][ 500/1879] lr: 2.0000e-03 eta: 4:46:36 time: 0.3252 data_time: 0.0169 memory: 6717 grad_norm: 3.3534 loss: 1.2260 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2260 2023/04/14 11:47:32 - mmengine - INFO - Epoch(train) [76][ 520/1879] lr: 2.0000e-03 eta: 4:46:29 time: 0.3978 data_time: 0.0139 memory: 6717 grad_norm: 3.4366 loss: 1.2399 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2399 2023/04/14 11:47:39 - mmengine - INFO - Epoch(train) [76][ 540/1879] lr: 2.0000e-03 eta: 4:46:21 time: 0.3271 data_time: 0.0149 memory: 6717 grad_norm: 3.2340 loss: 1.1741 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1741 2023/04/14 11:47:47 - mmengine - INFO - Epoch(train) [76][ 560/1879] lr: 2.0000e-03 eta: 4:46:14 time: 0.4118 data_time: 0.0143 memory: 6717 grad_norm: 3.4381 loss: 1.3080 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3080 2023/04/14 11:47:54 - mmengine - INFO - Epoch(train) [76][ 580/1879] lr: 2.0000e-03 eta: 4:46:06 time: 0.3182 data_time: 0.0135 memory: 6717 grad_norm: 3.3859 loss: 1.2834 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2834 2023/04/14 11:48:02 - mmengine - INFO - Epoch(train) [76][ 600/1879] lr: 2.0000e-03 eta: 4:45:59 time: 0.4344 data_time: 0.0145 memory: 6717 grad_norm: 3.3300 loss: 1.1529 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1529 2023/04/14 11:48:09 - mmengine - INFO - Epoch(train) [76][ 620/1879] lr: 2.0000e-03 eta: 4:45:51 time: 0.3382 data_time: 0.0144 memory: 6717 grad_norm: 3.3291 loss: 1.1973 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.1973 2023/04/14 11:48:17 - mmengine - INFO - Epoch(train) [76][ 640/1879] lr: 2.0000e-03 eta: 4:45:44 time: 0.4168 data_time: 0.0138 memory: 6717 grad_norm: 3.2611 loss: 1.1254 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1254 2023/04/14 11:48:24 - mmengine - INFO - Epoch(train) [76][ 660/1879] lr: 2.0000e-03 eta: 4:45:37 time: 0.3169 data_time: 0.0148 memory: 6717 grad_norm: 3.3775 loss: 1.1778 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1778 2023/04/14 11:48:31 - mmengine - INFO - Epoch(train) [76][ 680/1879] lr: 2.0000e-03 eta: 4:45:29 time: 0.3508 data_time: 0.0140 memory: 6717 grad_norm: 3.3233 loss: 1.1521 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1521 2023/04/14 11:48:38 - mmengine - INFO - Epoch(train) [76][ 700/1879] lr: 2.0000e-03 eta: 4:45:22 time: 0.3438 data_time: 0.0163 memory: 6717 grad_norm: 3.3407 loss: 1.2773 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2773 2023/04/14 11:48:46 - mmengine - INFO - Epoch(train) [76][ 720/1879] lr: 2.0000e-03 eta: 4:45:14 time: 0.4027 data_time: 0.0140 memory: 6717 grad_norm: 3.3316 loss: 1.1715 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1715 2023/04/14 11:48:53 - mmengine - INFO - Epoch(train) [76][ 740/1879] lr: 2.0000e-03 eta: 4:45:07 time: 0.3654 data_time: 0.0145 memory: 6717 grad_norm: 3.3395 loss: 1.1580 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1580 2023/04/14 11:49:00 - mmengine - INFO - Epoch(train) [76][ 760/1879] lr: 2.0000e-03 eta: 4:45:00 time: 0.3731 data_time: 0.0143 memory: 6717 grad_norm: 3.3260 loss: 1.0430 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0430 2023/04/14 11:49:07 - mmengine - INFO - Epoch(train) [76][ 780/1879] lr: 2.0000e-03 eta: 4:44:52 time: 0.3383 data_time: 0.0151 memory: 6717 grad_norm: 3.3749 loss: 1.2982 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2982 2023/04/14 11:49:15 - mmengine - INFO - Epoch(train) [76][ 800/1879] lr: 2.0000e-03 eta: 4:44:45 time: 0.3872 data_time: 0.0146 memory: 6717 grad_norm: 3.2787 loss: 1.1464 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1464 2023/04/14 11:49:22 - mmengine - INFO - Epoch(train) [76][ 820/1879] lr: 2.0000e-03 eta: 4:44:37 time: 0.3326 data_time: 0.0207 memory: 6717 grad_norm: 3.4022 loss: 1.0703 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0703 2023/04/14 11:49:30 - mmengine - INFO - Epoch(train) [76][ 840/1879] lr: 2.0000e-03 eta: 4:44:30 time: 0.3936 data_time: 0.0397 memory: 6717 grad_norm: 3.3403 loss: 1.1779 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1779 2023/04/14 11:49:36 - mmengine - INFO - Epoch(train) [76][ 860/1879] lr: 2.0000e-03 eta: 4:44:22 time: 0.3431 data_time: 0.0149 memory: 6717 grad_norm: 3.3438 loss: 1.0097 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0097 2023/04/14 11:49:44 - mmengine - INFO - Epoch(train) [76][ 880/1879] lr: 2.0000e-03 eta: 4:44:15 time: 0.3756 data_time: 0.0477 memory: 6717 grad_norm: 3.2751 loss: 1.1028 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1028 2023/04/14 11:49:51 - mmengine - INFO - Epoch(train) [76][ 900/1879] lr: 2.0000e-03 eta: 4:44:07 time: 0.3792 data_time: 0.0816 memory: 6717 grad_norm: 3.4323 loss: 1.1370 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1370 2023/04/14 11:49:59 - mmengine - INFO - Epoch(train) [76][ 920/1879] lr: 2.0000e-03 eta: 4:44:00 time: 0.3910 data_time: 0.1641 memory: 6717 grad_norm: 3.3769 loss: 1.1891 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1891 2023/04/14 11:50:07 - mmengine - INFO - Epoch(train) [76][ 940/1879] lr: 2.0000e-03 eta: 4:43:53 time: 0.3599 data_time: 0.0401 memory: 6717 grad_norm: 3.3967 loss: 1.2182 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2182 2023/04/14 11:50:14 - mmengine - INFO - Epoch(train) [76][ 960/1879] lr: 2.0000e-03 eta: 4:43:45 time: 0.3727 data_time: 0.0542 memory: 6717 grad_norm: 3.3532 loss: 1.1064 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.1064 2023/04/14 11:50:21 - mmengine - INFO - Epoch(train) [76][ 980/1879] lr: 2.0000e-03 eta: 4:43:38 time: 0.3609 data_time: 0.0214 memory: 6717 grad_norm: 3.2915 loss: 1.0834 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0834 2023/04/14 11:50:29 - mmengine - INFO - Epoch(train) [76][1000/1879] lr: 2.0000e-03 eta: 4:43:30 time: 0.3729 data_time: 0.0233 memory: 6717 grad_norm: 3.3712 loss: 1.2979 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2979 2023/04/14 11:50:35 - mmengine - INFO - Epoch(train) [76][1020/1879] lr: 2.0000e-03 eta: 4:43:23 time: 0.3251 data_time: 0.0181 memory: 6717 grad_norm: 3.3657 loss: 1.1997 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1997 2023/04/14 11:50:44 - mmengine - INFO - Epoch(train) [76][1040/1879] lr: 2.0000e-03 eta: 4:43:16 time: 0.4210 data_time: 0.0193 memory: 6717 grad_norm: 3.3587 loss: 1.1138 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1138 2023/04/14 11:50:50 - mmengine - INFO - Epoch(train) [76][1060/1879] lr: 2.0000e-03 eta: 4:43:08 time: 0.3444 data_time: 0.0134 memory: 6717 grad_norm: 3.3222 loss: 1.1639 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1639 2023/04/14 11:50:56 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 11:50:58 - mmengine - INFO - Epoch(train) [76][1080/1879] lr: 2.0000e-03 eta: 4:43:01 time: 0.3596 data_time: 0.0155 memory: 6717 grad_norm: 3.2825 loss: 1.1921 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1921 2023/04/14 11:51:06 - mmengine - INFO - Epoch(train) [76][1100/1879] lr: 2.0000e-03 eta: 4:42:54 time: 0.4053 data_time: 0.0420 memory: 6717 grad_norm: 3.4007 loss: 1.1289 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1289 2023/04/14 11:51:13 - mmengine - INFO - Epoch(train) [76][1120/1879] lr: 2.0000e-03 eta: 4:42:46 time: 0.3412 data_time: 0.0159 memory: 6717 grad_norm: 3.3504 loss: 1.3248 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3248 2023/04/14 11:51:20 - mmengine - INFO - Epoch(train) [76][1140/1879] lr: 2.0000e-03 eta: 4:42:39 time: 0.3908 data_time: 0.0129 memory: 6717 grad_norm: 3.3669 loss: 1.1669 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1669 2023/04/14 11:51:27 - mmengine - INFO - Epoch(train) [76][1160/1879] lr: 2.0000e-03 eta: 4:42:31 time: 0.3266 data_time: 0.0166 memory: 6717 grad_norm: 3.2729 loss: 1.1112 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1112 2023/04/14 11:51:34 - mmengine - INFO - Epoch(train) [76][1180/1879] lr: 2.0000e-03 eta: 4:42:24 time: 0.3714 data_time: 0.0155 memory: 6717 grad_norm: 3.3228 loss: 1.1946 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1946 2023/04/14 11:51:42 - mmengine - INFO - Epoch(train) [76][1200/1879] lr: 2.0000e-03 eta: 4:42:16 time: 0.3652 data_time: 0.0209 memory: 6717 grad_norm: 3.4622 loss: 1.2182 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2182 2023/04/14 11:51:49 - mmengine - INFO - Epoch(train) [76][1220/1879] lr: 2.0000e-03 eta: 4:42:09 time: 0.3773 data_time: 0.0923 memory: 6717 grad_norm: 3.2703 loss: 1.1659 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1659 2023/04/14 11:51:57 - mmengine - INFO - Epoch(train) [76][1240/1879] lr: 2.0000e-03 eta: 4:42:01 time: 0.3654 data_time: 0.0285 memory: 6717 grad_norm: 3.3750 loss: 1.1207 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1207 2023/04/14 11:52:04 - mmengine - INFO - Epoch(train) [76][1260/1879] lr: 2.0000e-03 eta: 4:41:54 time: 0.3614 data_time: 0.0630 memory: 6717 grad_norm: 3.3880 loss: 1.1102 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1102 2023/04/14 11:52:11 - mmengine - INFO - Epoch(train) [76][1280/1879] lr: 2.0000e-03 eta: 4:41:47 time: 0.3762 data_time: 0.0591 memory: 6717 grad_norm: 3.3598 loss: 1.0587 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0587 2023/04/14 11:52:18 - mmengine - INFO - Epoch(train) [76][1300/1879] lr: 2.0000e-03 eta: 4:41:39 time: 0.3537 data_time: 0.0837 memory: 6717 grad_norm: 3.3134 loss: 1.1885 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1885 2023/04/14 11:52:26 - mmengine - INFO - Epoch(train) [76][1320/1879] lr: 2.0000e-03 eta: 4:41:32 time: 0.3621 data_time: 0.0875 memory: 6717 grad_norm: 3.3291 loss: 1.0772 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0772 2023/04/14 11:52:33 - mmengine - INFO - Epoch(train) [76][1340/1879] lr: 2.0000e-03 eta: 4:41:24 time: 0.3871 data_time: 0.1597 memory: 6717 grad_norm: 3.3301 loss: 1.3015 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3015 2023/04/14 11:52:41 - mmengine - INFO - Epoch(train) [76][1360/1879] lr: 2.0000e-03 eta: 4:41:17 time: 0.4003 data_time: 0.0463 memory: 6717 grad_norm: 3.4272 loss: 1.2658 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2658 2023/04/14 11:52:48 - mmengine - INFO - Epoch(train) [76][1380/1879] lr: 2.0000e-03 eta: 4:41:09 time: 0.3031 data_time: 0.0154 memory: 6717 grad_norm: 3.3714 loss: 1.2551 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2551 2023/04/14 11:52:56 - mmengine - INFO - Epoch(train) [76][1400/1879] lr: 2.0000e-03 eta: 4:41:02 time: 0.4259 data_time: 0.0134 memory: 6717 grad_norm: 3.2891 loss: 1.2001 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2001 2023/04/14 11:53:03 - mmengine - INFO - Epoch(train) [76][1420/1879] lr: 2.0000e-03 eta: 4:40:55 time: 0.3262 data_time: 0.0154 memory: 6717 grad_norm: 3.4070 loss: 1.1173 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1173 2023/04/14 11:53:10 - mmengine - INFO - Epoch(train) [76][1440/1879] lr: 2.0000e-03 eta: 4:40:47 time: 0.3968 data_time: 0.0138 memory: 6717 grad_norm: 3.4654 loss: 1.1962 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1962 2023/04/14 11:53:18 - mmengine - INFO - Epoch(train) [76][1460/1879] lr: 2.0000e-03 eta: 4:40:40 time: 0.3663 data_time: 0.0130 memory: 6717 grad_norm: 3.3169 loss: 1.1031 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1031 2023/04/14 11:53:26 - mmengine - INFO - Epoch(train) [76][1480/1879] lr: 2.0000e-03 eta: 4:40:33 time: 0.4127 data_time: 0.0139 memory: 6717 grad_norm: 3.3385 loss: 1.1154 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1154 2023/04/14 11:53:32 - mmengine - INFO - Epoch(train) [76][1500/1879] lr: 2.0000e-03 eta: 4:40:25 time: 0.2854 data_time: 0.0143 memory: 6717 grad_norm: 3.3403 loss: 1.0932 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0932 2023/04/14 11:53:40 - mmengine - INFO - Epoch(train) [76][1520/1879] lr: 2.0000e-03 eta: 4:40:18 time: 0.3907 data_time: 0.0146 memory: 6717 grad_norm: 3.4108 loss: 1.1435 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1435 2023/04/14 11:53:46 - mmengine - INFO - Epoch(train) [76][1540/1879] lr: 2.0000e-03 eta: 4:40:10 time: 0.3172 data_time: 0.0150 memory: 6717 grad_norm: 3.3631 loss: 1.2355 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2355 2023/04/14 11:53:55 - mmengine - INFO - Epoch(train) [76][1560/1879] lr: 2.0000e-03 eta: 4:40:03 time: 0.4409 data_time: 0.0144 memory: 6717 grad_norm: 3.4045 loss: 1.2612 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2612 2023/04/14 11:54:01 - mmengine - INFO - Epoch(train) [76][1580/1879] lr: 2.0000e-03 eta: 4:39:55 time: 0.3158 data_time: 0.0161 memory: 6717 grad_norm: 3.3126 loss: 1.1163 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1163 2023/04/14 11:54:10 - mmengine - INFO - Epoch(train) [76][1600/1879] lr: 2.0000e-03 eta: 4:39:48 time: 0.4236 data_time: 0.0142 memory: 6717 grad_norm: 3.4708 loss: 1.3044 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3044 2023/04/14 11:54:16 - mmengine - INFO - Epoch(train) [76][1620/1879] lr: 2.0000e-03 eta: 4:39:40 time: 0.3364 data_time: 0.0162 memory: 6717 grad_norm: 3.3073 loss: 1.1423 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1423 2023/04/14 11:54:24 - mmengine - INFO - Epoch(train) [76][1640/1879] lr: 2.0000e-03 eta: 4:39:33 time: 0.3668 data_time: 0.0144 memory: 6717 grad_norm: 3.3252 loss: 1.2178 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2178 2023/04/14 11:54:31 - mmengine - INFO - Epoch(train) [76][1660/1879] lr: 2.0000e-03 eta: 4:39:26 time: 0.3496 data_time: 0.0149 memory: 6717 grad_norm: 3.4761 loss: 1.3243 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3243 2023/04/14 11:54:38 - mmengine - INFO - Epoch(train) [76][1680/1879] lr: 2.0000e-03 eta: 4:39:18 time: 0.3913 data_time: 0.0156 memory: 6717 grad_norm: 3.2938 loss: 1.0908 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0908 2023/04/14 11:54:45 - mmengine - INFO - Epoch(train) [76][1700/1879] lr: 2.0000e-03 eta: 4:39:11 time: 0.3402 data_time: 0.0155 memory: 6717 grad_norm: 3.3882 loss: 1.1504 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1504 2023/04/14 11:54:54 - mmengine - INFO - Epoch(train) [76][1720/1879] lr: 2.0000e-03 eta: 4:39:04 time: 0.4127 data_time: 0.0141 memory: 6717 grad_norm: 3.3159 loss: 1.1830 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1830 2023/04/14 11:55:00 - mmengine - INFO - Epoch(train) [76][1740/1879] lr: 2.0000e-03 eta: 4:38:56 time: 0.3168 data_time: 0.0156 memory: 6717 grad_norm: 3.3449 loss: 1.1393 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1393 2023/04/14 11:55:08 - mmengine - INFO - Epoch(train) [76][1760/1879] lr: 2.0000e-03 eta: 4:38:49 time: 0.4288 data_time: 0.0142 memory: 6717 grad_norm: 3.4121 loss: 1.2220 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2220 2023/04/14 11:55:15 - mmengine - INFO - Epoch(train) [76][1780/1879] lr: 2.0000e-03 eta: 4:38:41 time: 0.3246 data_time: 0.0147 memory: 6717 grad_norm: 3.3863 loss: 1.3497 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3497 2023/04/14 11:55:23 - mmengine - INFO - Epoch(train) [76][1800/1879] lr: 2.0000e-03 eta: 4:38:34 time: 0.3883 data_time: 0.0150 memory: 6717 grad_norm: 3.3295 loss: 1.2720 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2720 2023/04/14 11:55:30 - mmengine - INFO - Epoch(train) [76][1820/1879] lr: 2.0000e-03 eta: 4:38:26 time: 0.3375 data_time: 0.0144 memory: 6717 grad_norm: 3.2815 loss: 1.2617 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2617 2023/04/14 11:55:38 - mmengine - INFO - Epoch(train) [76][1840/1879] lr: 2.0000e-03 eta: 4:38:19 time: 0.4227 data_time: 0.0142 memory: 6717 grad_norm: 3.3328 loss: 1.0745 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0745 2023/04/14 11:55:44 - mmengine - INFO - Epoch(train) [76][1860/1879] lr: 2.0000e-03 eta: 4:38:11 time: 0.3179 data_time: 0.0140 memory: 6717 grad_norm: 3.3642 loss: 1.2230 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2230 2023/04/14 11:55:51 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 11:55:51 - mmengine - INFO - Epoch(train) [76][1879/1879] lr: 2.0000e-03 eta: 4:38:04 time: 0.3375 data_time: 0.0131 memory: 6717 grad_norm: 3.5145 loss: 1.2170 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.2170 2023/04/14 11:56:00 - mmengine - INFO - Epoch(val) [76][ 20/155] eta: 0:01:02 time: 0.4620 data_time: 0.4290 memory: 1391 2023/04/14 11:56:06 - mmengine - INFO - Epoch(val) [76][ 40/155] eta: 0:00:45 time: 0.3207 data_time: 0.2874 memory: 1391 2023/04/14 11:56:15 - mmengine - INFO - Epoch(val) [76][ 60/155] eta: 0:00:38 time: 0.4317 data_time: 0.3980 memory: 1391 2023/04/14 11:56:21 - mmengine - INFO - Epoch(val) [76][ 80/155] eta: 0:00:28 time: 0.3093 data_time: 0.2767 memory: 1391 2023/04/14 11:56:30 - mmengine - INFO - Epoch(val) [76][100/155] eta: 0:00:21 time: 0.4272 data_time: 0.3944 memory: 1391 2023/04/14 11:56:37 - mmengine - INFO - Epoch(val) [76][120/155] eta: 0:00:13 time: 0.3396 data_time: 0.3068 memory: 1391 2023/04/14 11:56:46 - mmengine - INFO - Epoch(val) [76][140/155] eta: 0:00:05 time: 0.4861 data_time: 0.4524 memory: 1391 2023/04/14 11:56:54 - mmengine - INFO - Epoch(val) [76][155/155] acc/top1: 0.6644 acc/top5: 0.8737 acc/mean1: 0.6643 data_time: 0.4174 time: 0.4495 2023/04/14 11:57:03 - mmengine - INFO - Epoch(train) [77][ 20/1879] lr: 2.0000e-03 eta: 4:37:58 time: 0.4949 data_time: 0.3152 memory: 6717 grad_norm: 3.3887 loss: 1.1491 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1491 2023/04/14 11:57:10 - mmengine - INFO - Epoch(train) [77][ 40/1879] lr: 2.0000e-03 eta: 4:37:50 time: 0.3367 data_time: 0.1620 memory: 6717 grad_norm: 3.3225 loss: 1.1644 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.1644 2023/04/14 11:57:19 - mmengine - INFO - Epoch(train) [77][ 60/1879] lr: 2.0000e-03 eta: 4:37:43 time: 0.4524 data_time: 0.1665 memory: 6717 grad_norm: 3.3043 loss: 1.0674 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0674 2023/04/14 11:57:26 - mmengine - INFO - Epoch(train) [77][ 80/1879] lr: 2.0000e-03 eta: 4:37:36 time: 0.3447 data_time: 0.0331 memory: 6717 grad_norm: 3.4163 loss: 1.0123 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0123 2023/04/14 11:57:34 - mmengine - INFO - Epoch(train) [77][ 100/1879] lr: 2.0000e-03 eta: 4:37:28 time: 0.4052 data_time: 0.0149 memory: 6717 grad_norm: 3.2769 loss: 1.0912 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0912 2023/04/14 11:57:41 - mmengine - INFO - Epoch(train) [77][ 120/1879] lr: 2.0000e-03 eta: 4:37:21 time: 0.3237 data_time: 0.0136 memory: 6717 grad_norm: 3.3788 loss: 1.3876 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3876 2023/04/14 11:57:49 - mmengine - INFO - Epoch(train) [77][ 140/1879] lr: 2.0000e-03 eta: 4:37:13 time: 0.4049 data_time: 0.0159 memory: 6717 grad_norm: 3.2631 loss: 1.1965 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1965 2023/04/14 11:57:55 - mmengine - INFO - Epoch(train) [77][ 160/1879] lr: 2.0000e-03 eta: 4:37:06 time: 0.3098 data_time: 0.0139 memory: 6717 grad_norm: 3.3072 loss: 1.0909 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0909 2023/04/14 11:58:04 - mmengine - INFO - Epoch(train) [77][ 180/1879] lr: 2.0000e-03 eta: 4:36:59 time: 0.4300 data_time: 0.0158 memory: 6717 grad_norm: 3.2633 loss: 1.3174 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.3174 2023/04/14 11:58:10 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 11:58:10 - mmengine - INFO - Epoch(train) [77][ 200/1879] lr: 2.0000e-03 eta: 4:36:51 time: 0.3372 data_time: 0.0129 memory: 6717 grad_norm: 3.4712 loss: 1.1292 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1292 2023/04/14 11:58:18 - mmengine - INFO - Epoch(train) [77][ 220/1879] lr: 2.0000e-03 eta: 4:36:44 time: 0.3902 data_time: 0.0158 memory: 6717 grad_norm: 3.3619 loss: 1.1595 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1595 2023/04/14 11:58:25 - mmengine - INFO - Epoch(train) [77][ 240/1879] lr: 2.0000e-03 eta: 4:36:36 time: 0.3171 data_time: 0.0124 memory: 6717 grad_norm: 3.3876 loss: 1.0991 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0991 2023/04/14 11:58:33 - mmengine - INFO - Epoch(train) [77][ 260/1879] lr: 2.0000e-03 eta: 4:36:29 time: 0.4330 data_time: 0.1001 memory: 6717 grad_norm: 3.3496 loss: 1.2425 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2425 2023/04/14 11:58:40 - mmengine - INFO - Epoch(train) [77][ 280/1879] lr: 2.0000e-03 eta: 4:36:21 time: 0.3242 data_time: 0.0626 memory: 6717 grad_norm: 3.3923 loss: 1.2855 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2855 2023/04/14 11:58:47 - mmengine - INFO - Epoch(train) [77][ 300/1879] lr: 2.0000e-03 eta: 4:36:14 time: 0.3869 data_time: 0.0156 memory: 6717 grad_norm: 3.2675 loss: 1.1443 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.1443 2023/04/14 11:58:54 - mmengine - INFO - Epoch(train) [77][ 320/1879] lr: 2.0000e-03 eta: 4:36:06 time: 0.3106 data_time: 0.0450 memory: 6717 grad_norm: 3.3010 loss: 1.0897 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0897 2023/04/14 11:59:02 - mmengine - INFO - Epoch(train) [77][ 340/1879] lr: 2.0000e-03 eta: 4:35:59 time: 0.4298 data_time: 0.0306 memory: 6717 grad_norm: 3.3726 loss: 1.1717 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1717 2023/04/14 11:59:09 - mmengine - INFO - Epoch(train) [77][ 360/1879] lr: 2.0000e-03 eta: 4:35:52 time: 0.3279 data_time: 0.0128 memory: 6717 grad_norm: 3.3371 loss: 1.0411 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0411 2023/04/14 11:59:17 - mmengine - INFO - Epoch(train) [77][ 380/1879] lr: 2.0000e-03 eta: 4:35:45 time: 0.4205 data_time: 0.0156 memory: 6717 grad_norm: 3.2462 loss: 0.9895 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9895 2023/04/14 11:59:24 - mmengine - INFO - Epoch(train) [77][ 400/1879] lr: 2.0000e-03 eta: 4:35:37 time: 0.3264 data_time: 0.0131 memory: 6717 grad_norm: 3.3742 loss: 0.9953 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9953 2023/04/14 11:59:32 - mmengine - INFO - Epoch(train) [77][ 420/1879] lr: 2.0000e-03 eta: 4:35:30 time: 0.4075 data_time: 0.0160 memory: 6717 grad_norm: 3.4481 loss: 1.1439 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1439 2023/04/14 11:59:39 - mmengine - INFO - Epoch(train) [77][ 440/1879] lr: 2.0000e-03 eta: 4:35:22 time: 0.3386 data_time: 0.0130 memory: 6717 grad_norm: 3.3404 loss: 1.1431 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1431 2023/04/14 11:59:47 - mmengine - INFO - Epoch(train) [77][ 460/1879] lr: 2.0000e-03 eta: 4:35:15 time: 0.4058 data_time: 0.0165 memory: 6717 grad_norm: 3.4498 loss: 1.1123 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1123 2023/04/14 11:59:54 - mmengine - INFO - Epoch(train) [77][ 480/1879] lr: 2.0000e-03 eta: 4:35:07 time: 0.3372 data_time: 0.0125 memory: 6717 grad_norm: 3.4428 loss: 1.2804 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2804 2023/04/14 12:00:02 - mmengine - INFO - Epoch(train) [77][ 500/1879] lr: 2.0000e-03 eta: 4:35:00 time: 0.4228 data_time: 0.0177 memory: 6717 grad_norm: 3.3458 loss: 1.0696 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0696 2023/04/14 12:00:09 - mmengine - INFO - Epoch(train) [77][ 520/1879] lr: 2.0000e-03 eta: 4:34:53 time: 0.3426 data_time: 0.0128 memory: 6717 grad_norm: 3.2996 loss: 1.2197 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2197 2023/04/14 12:00:17 - mmengine - INFO - Epoch(train) [77][ 540/1879] lr: 2.0000e-03 eta: 4:34:45 time: 0.3931 data_time: 0.0432 memory: 6717 grad_norm: 3.3512 loss: 1.1898 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1898 2023/04/14 12:00:23 - mmengine - INFO - Epoch(train) [77][ 560/1879] lr: 2.0000e-03 eta: 4:34:38 time: 0.3325 data_time: 0.1144 memory: 6717 grad_norm: 3.4051 loss: 1.2034 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2034 2023/04/14 12:00:31 - mmengine - INFO - Epoch(train) [77][ 580/1879] lr: 2.0000e-03 eta: 4:34:31 time: 0.3941 data_time: 0.1027 memory: 6717 grad_norm: 3.4513 loss: 1.2650 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2650 2023/04/14 12:00:38 - mmengine - INFO - Epoch(train) [77][ 600/1879] lr: 2.0000e-03 eta: 4:34:23 time: 0.3603 data_time: 0.1512 memory: 6717 grad_norm: 3.3377 loss: 1.1811 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1811 2023/04/14 12:00:47 - mmengine - INFO - Epoch(train) [77][ 620/1879] lr: 2.0000e-03 eta: 4:34:16 time: 0.4165 data_time: 0.2569 memory: 6717 grad_norm: 3.4548 loss: 1.1517 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1517 2023/04/14 12:00:53 - mmengine - INFO - Epoch(train) [77][ 640/1879] lr: 2.0000e-03 eta: 4:34:08 time: 0.3279 data_time: 0.1748 memory: 6717 grad_norm: 3.3372 loss: 1.0412 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0412 2023/04/14 12:01:02 - mmengine - INFO - Epoch(train) [77][ 660/1879] lr: 2.0000e-03 eta: 4:34:01 time: 0.4099 data_time: 0.2449 memory: 6717 grad_norm: 3.3756 loss: 1.1321 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1321 2023/04/14 12:01:08 - mmengine - INFO - Epoch(train) [77][ 680/1879] lr: 2.0000e-03 eta: 4:33:54 time: 0.3438 data_time: 0.1753 memory: 6717 grad_norm: 3.4424 loss: 1.2426 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2426 2023/04/14 12:01:17 - mmengine - INFO - Epoch(train) [77][ 700/1879] lr: 2.0000e-03 eta: 4:33:47 time: 0.4405 data_time: 0.2903 memory: 6717 grad_norm: 3.3495 loss: 1.1388 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1388 2023/04/14 12:01:23 - mmengine - INFO - Epoch(train) [77][ 720/1879] lr: 2.0000e-03 eta: 4:33:39 time: 0.2860 data_time: 0.1424 memory: 6717 grad_norm: 3.2838 loss: 1.2476 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.2476 2023/04/14 12:01:31 - mmengine - INFO - Epoch(train) [77][ 740/1879] lr: 2.0000e-03 eta: 4:33:32 time: 0.4041 data_time: 0.2192 memory: 6717 grad_norm: 3.4308 loss: 1.2889 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.2889 2023/04/14 12:01:38 - mmengine - INFO - Epoch(train) [77][ 760/1879] lr: 2.0000e-03 eta: 4:33:24 time: 0.3517 data_time: 0.1263 memory: 6717 grad_norm: 3.3770 loss: 1.2347 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2347 2023/04/14 12:01:45 - mmengine - INFO - Epoch(train) [77][ 780/1879] lr: 2.0000e-03 eta: 4:33:17 time: 0.3658 data_time: 0.1906 memory: 6717 grad_norm: 3.3636 loss: 1.3367 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3367 2023/04/14 12:01:52 - mmengine - INFO - Epoch(train) [77][ 800/1879] lr: 2.0000e-03 eta: 4:33:09 time: 0.3478 data_time: 0.1330 memory: 6717 grad_norm: 3.3783 loss: 1.1964 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1964 2023/04/14 12:02:00 - mmengine - INFO - Epoch(train) [77][ 820/1879] lr: 2.0000e-03 eta: 4:33:02 time: 0.3757 data_time: 0.1678 memory: 6717 grad_norm: 3.4102 loss: 1.1700 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1700 2023/04/14 12:02:08 - mmengine - INFO - Epoch(train) [77][ 840/1879] lr: 2.0000e-03 eta: 4:32:54 time: 0.3813 data_time: 0.0813 memory: 6717 grad_norm: 3.3422 loss: 1.2422 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2422 2023/04/14 12:02:14 - mmengine - INFO - Epoch(train) [77][ 860/1879] lr: 2.0000e-03 eta: 4:32:47 time: 0.3236 data_time: 0.0660 memory: 6717 grad_norm: 3.3717 loss: 1.1765 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1765 2023/04/14 12:02:22 - mmengine - INFO - Epoch(train) [77][ 880/1879] lr: 2.0000e-03 eta: 4:32:39 time: 0.4065 data_time: 0.0175 memory: 6717 grad_norm: 3.4160 loss: 1.0485 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0485 2023/04/14 12:02:29 - mmengine - INFO - Epoch(train) [77][ 900/1879] lr: 2.0000e-03 eta: 4:32:32 time: 0.3362 data_time: 0.0157 memory: 6717 grad_norm: 3.3548 loss: 1.0978 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0978 2023/04/14 12:02:37 - mmengine - INFO - Epoch(train) [77][ 920/1879] lr: 2.0000e-03 eta: 4:32:25 time: 0.4222 data_time: 0.0124 memory: 6717 grad_norm: 3.4583 loss: 1.2792 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2792 2023/04/14 12:02:44 - mmengine - INFO - Epoch(train) [77][ 940/1879] lr: 2.0000e-03 eta: 4:32:17 time: 0.3227 data_time: 0.0147 memory: 6717 grad_norm: 3.3406 loss: 1.1959 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1959 2023/04/14 12:02:52 - mmengine - INFO - Epoch(train) [77][ 960/1879] lr: 2.0000e-03 eta: 4:32:10 time: 0.3940 data_time: 0.0143 memory: 6717 grad_norm: 3.4404 loss: 1.2861 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2861 2023/04/14 12:02:58 - mmengine - INFO - Epoch(train) [77][ 980/1879] lr: 2.0000e-03 eta: 4:32:02 time: 0.3324 data_time: 0.0152 memory: 6717 grad_norm: 3.1994 loss: 1.0107 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0107 2023/04/14 12:03:06 - mmengine - INFO - Epoch(train) [77][1000/1879] lr: 2.0000e-03 eta: 4:31:55 time: 0.3882 data_time: 0.0137 memory: 6717 grad_norm: 3.3401 loss: 0.9814 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.9814 2023/04/14 12:03:14 - mmengine - INFO - Epoch(train) [77][1020/1879] lr: 2.0000e-03 eta: 4:31:48 time: 0.3906 data_time: 0.0173 memory: 6717 grad_norm: 3.4416 loss: 1.1669 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1669 2023/04/14 12:03:21 - mmengine - INFO - Epoch(train) [77][1040/1879] lr: 2.0000e-03 eta: 4:31:40 time: 0.3514 data_time: 0.0133 memory: 6717 grad_norm: 3.4440 loss: 1.1498 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1498 2023/04/14 12:03:29 - mmengine - INFO - Epoch(train) [77][1060/1879] lr: 2.0000e-03 eta: 4:31:33 time: 0.3934 data_time: 0.0154 memory: 6717 grad_norm: 3.3090 loss: 1.2650 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2650 2023/04/14 12:03:35 - mmengine - INFO - Epoch(train) [77][1080/1879] lr: 2.0000e-03 eta: 4:31:25 time: 0.3261 data_time: 0.0149 memory: 6717 grad_norm: 3.3816 loss: 1.2369 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2369 2023/04/14 12:03:44 - mmengine - INFO - Epoch(train) [77][1100/1879] lr: 2.0000e-03 eta: 4:31:18 time: 0.4315 data_time: 0.0146 memory: 6717 grad_norm: 3.3041 loss: 1.0872 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0872 2023/04/14 12:03:51 - mmengine - INFO - Epoch(train) [77][1120/1879] lr: 2.0000e-03 eta: 4:31:11 time: 0.3311 data_time: 0.0131 memory: 6717 grad_norm: 3.3575 loss: 1.0378 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0378 2023/04/14 12:03:59 - mmengine - INFO - Epoch(train) [77][1140/1879] lr: 2.0000e-03 eta: 4:31:03 time: 0.4106 data_time: 0.0136 memory: 6717 grad_norm: 3.4205 loss: 1.3047 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3047 2023/04/14 12:04:05 - mmengine - INFO - Epoch(train) [77][1160/1879] lr: 2.0000e-03 eta: 4:30:56 time: 0.3182 data_time: 0.0152 memory: 6717 grad_norm: 3.3340 loss: 1.1157 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1157 2023/04/14 12:04:13 - mmengine - INFO - Epoch(train) [77][1180/1879] lr: 2.0000e-03 eta: 4:30:48 time: 0.4003 data_time: 0.0144 memory: 6717 grad_norm: 3.4315 loss: 1.1811 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1811 2023/04/14 12:04:19 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 12:04:20 - mmengine - INFO - Epoch(train) [77][1200/1879] lr: 2.0000e-03 eta: 4:30:41 time: 0.3347 data_time: 0.0523 memory: 6717 grad_norm: 3.3368 loss: 1.1485 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1485 2023/04/14 12:04:28 - mmengine - INFO - Epoch(train) [77][1220/1879] lr: 2.0000e-03 eta: 4:30:34 time: 0.3962 data_time: 0.1319 memory: 6717 grad_norm: 3.3537 loss: 1.2070 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2070 2023/04/14 12:04:35 - mmengine - INFO - Epoch(train) [77][1240/1879] lr: 2.0000e-03 eta: 4:30:26 time: 0.3599 data_time: 0.1337 memory: 6717 grad_norm: 3.4240 loss: 1.1007 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1007 2023/04/14 12:04:42 - mmengine - INFO - Epoch(train) [77][1260/1879] lr: 2.0000e-03 eta: 4:30:19 time: 0.3601 data_time: 0.0790 memory: 6717 grad_norm: 3.3188 loss: 0.9601 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9601 2023/04/14 12:04:50 - mmengine - INFO - Epoch(train) [77][1280/1879] lr: 2.0000e-03 eta: 4:30:12 time: 0.4010 data_time: 0.1960 memory: 6717 grad_norm: 3.5500 loss: 1.3373 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3373 2023/04/14 12:04:57 - mmengine - INFO - Epoch(train) [77][1300/1879] lr: 2.0000e-03 eta: 4:30:04 time: 0.3429 data_time: 0.1079 memory: 6717 grad_norm: 3.2786 loss: 1.2439 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2439 2023/04/14 12:05:04 - mmengine - INFO - Epoch(train) [77][1320/1879] lr: 2.0000e-03 eta: 4:29:56 time: 0.3516 data_time: 0.0702 memory: 6717 grad_norm: 3.3594 loss: 1.2106 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2106 2023/04/14 12:05:12 - mmengine - INFO - Epoch(train) [77][1340/1879] lr: 2.0000e-03 eta: 4:29:49 time: 0.4101 data_time: 0.0120 memory: 6717 grad_norm: 3.3884 loss: 1.0648 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0648 2023/04/14 12:05:19 - mmengine - INFO - Epoch(train) [77][1360/1879] lr: 2.0000e-03 eta: 4:29:42 time: 0.3239 data_time: 0.0161 memory: 6717 grad_norm: 3.4029 loss: 1.1855 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1855 2023/04/14 12:05:27 - mmengine - INFO - Epoch(train) [77][1380/1879] lr: 2.0000e-03 eta: 4:29:34 time: 0.3981 data_time: 0.0137 memory: 6717 grad_norm: 3.3703 loss: 1.2578 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2578 2023/04/14 12:05:34 - mmengine - INFO - Epoch(train) [77][1400/1879] lr: 2.0000e-03 eta: 4:29:27 time: 0.3535 data_time: 0.0146 memory: 6717 grad_norm: 3.4303 loss: 1.1394 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1394 2023/04/14 12:05:42 - mmengine - INFO - Epoch(train) [77][1420/1879] lr: 2.0000e-03 eta: 4:29:20 time: 0.3933 data_time: 0.0129 memory: 6717 grad_norm: 3.3853 loss: 1.1904 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1904 2023/04/14 12:05:48 - mmengine - INFO - Epoch(train) [77][1440/1879] lr: 2.0000e-03 eta: 4:29:12 time: 0.3331 data_time: 0.0161 memory: 6717 grad_norm: 3.3279 loss: 1.1021 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1021 2023/04/14 12:05:56 - mmengine - INFO - Epoch(train) [77][1460/1879] lr: 2.0000e-03 eta: 4:29:05 time: 0.4016 data_time: 0.0127 memory: 6717 grad_norm: 3.4015 loss: 1.1936 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1936 2023/04/14 12:06:03 - mmengine - INFO - Epoch(train) [77][1480/1879] lr: 2.0000e-03 eta: 4:28:57 time: 0.3423 data_time: 0.0158 memory: 6717 grad_norm: 3.3519 loss: 1.2575 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2575 2023/04/14 12:06:11 - mmengine - INFO - Epoch(train) [77][1500/1879] lr: 2.0000e-03 eta: 4:28:50 time: 0.4007 data_time: 0.0120 memory: 6717 grad_norm: 3.2735 loss: 1.1667 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1667 2023/04/14 12:06:18 - mmengine - INFO - Epoch(train) [77][1520/1879] lr: 2.0000e-03 eta: 4:28:42 time: 0.3463 data_time: 0.0165 memory: 6717 grad_norm: 3.2901 loss: 1.1508 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1508 2023/04/14 12:06:26 - mmengine - INFO - Epoch(train) [77][1540/1879] lr: 2.0000e-03 eta: 4:28:35 time: 0.4033 data_time: 0.0130 memory: 6717 grad_norm: 3.3851 loss: 1.1351 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1351 2023/04/14 12:06:33 - mmengine - INFO - Epoch(train) [77][1560/1879] lr: 2.0000e-03 eta: 4:28:28 time: 0.3421 data_time: 0.0159 memory: 6717 grad_norm: 3.3751 loss: 1.1450 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1450 2023/04/14 12:06:41 - mmengine - INFO - Epoch(train) [77][1580/1879] lr: 2.0000e-03 eta: 4:28:20 time: 0.3827 data_time: 0.0132 memory: 6717 grad_norm: 3.3317 loss: 1.1585 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1585 2023/04/14 12:06:48 - mmengine - INFO - Epoch(train) [77][1600/1879] lr: 2.0000e-03 eta: 4:28:13 time: 0.3627 data_time: 0.0158 memory: 6717 grad_norm: 3.3725 loss: 1.3220 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3220 2023/04/14 12:06:56 - mmengine - INFO - Epoch(train) [77][1620/1879] lr: 2.0000e-03 eta: 4:28:06 time: 0.4022 data_time: 0.0142 memory: 6717 grad_norm: 3.4043 loss: 1.2675 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2675 2023/04/14 12:07:05 - mmengine - INFO - Epoch(train) [77][1640/1879] lr: 2.0000e-03 eta: 4:27:59 time: 0.4452 data_time: 0.0187 memory: 6717 grad_norm: 3.4059 loss: 1.0676 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0676 2023/04/14 12:07:14 - mmengine - INFO - Epoch(train) [77][1660/1879] lr: 2.0000e-03 eta: 4:27:52 time: 0.4316 data_time: 0.0131 memory: 6717 grad_norm: 3.2975 loss: 1.0991 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0991 2023/04/14 12:07:20 - mmengine - INFO - Epoch(train) [77][1680/1879] lr: 2.0000e-03 eta: 4:27:44 time: 0.3228 data_time: 0.0146 memory: 6717 grad_norm: 3.3867 loss: 1.2729 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2729 2023/04/14 12:07:29 - mmengine - INFO - Epoch(train) [77][1700/1879] lr: 2.0000e-03 eta: 4:27:37 time: 0.4369 data_time: 0.0130 memory: 6717 grad_norm: 3.4405 loss: 1.2867 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2867 2023/04/14 12:07:36 - mmengine - INFO - Epoch(train) [77][1720/1879] lr: 2.0000e-03 eta: 4:27:30 time: 0.3479 data_time: 0.0145 memory: 6717 grad_norm: 3.3737 loss: 1.0379 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0379 2023/04/14 12:07:45 - mmengine - INFO - Epoch(train) [77][1740/1879] lr: 2.0000e-03 eta: 4:27:22 time: 0.4347 data_time: 0.0131 memory: 6717 grad_norm: 3.2966 loss: 1.2292 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2292 2023/04/14 12:07:51 - mmengine - INFO - Epoch(train) [77][1760/1879] lr: 2.0000e-03 eta: 4:27:15 time: 0.3050 data_time: 0.0148 memory: 6717 grad_norm: 3.4705 loss: 1.0786 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0786 2023/04/14 12:07:58 - mmengine - INFO - Epoch(train) [77][1780/1879] lr: 2.0000e-03 eta: 4:27:07 time: 0.3767 data_time: 0.0139 memory: 6717 grad_norm: 3.3764 loss: 1.1085 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1085 2023/04/14 12:08:06 - mmengine - INFO - Epoch(train) [77][1800/1879] lr: 2.0000e-03 eta: 4:27:00 time: 0.3816 data_time: 0.0153 memory: 6717 grad_norm: 3.3681 loss: 1.2428 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2428 2023/04/14 12:08:14 - mmengine - INFO - Epoch(train) [77][1820/1879] lr: 2.0000e-03 eta: 4:26:53 time: 0.4229 data_time: 0.0143 memory: 6717 grad_norm: 3.3627 loss: 1.2043 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2043 2023/04/14 12:08:20 - mmengine - INFO - Epoch(train) [77][1840/1879] lr: 2.0000e-03 eta: 4:26:45 time: 0.3027 data_time: 0.0140 memory: 6717 grad_norm: 3.3369 loss: 1.1698 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1698 2023/04/14 12:08:29 - mmengine - INFO - Epoch(train) [77][1860/1879] lr: 2.0000e-03 eta: 4:26:38 time: 0.4485 data_time: 0.0136 memory: 6717 grad_norm: 3.3418 loss: 1.3316 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.3316 2023/04/14 12:08:35 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 12:08:35 - mmengine - INFO - Epoch(train) [77][1879/1879] lr: 2.0000e-03 eta: 4:26:31 time: 0.3860 data_time: 0.0132 memory: 6717 grad_norm: 3.4484 loss: 1.3086 top1_acc: 0.4286 top5_acc: 0.7143 loss_cls: 1.3086 2023/04/14 12:08:44 - mmengine - INFO - Epoch(val) [77][ 20/155] eta: 0:00:59 time: 0.4395 data_time: 0.4053 memory: 1391 2023/04/14 12:08:51 - mmengine - INFO - Epoch(val) [77][ 40/155] eta: 0:00:44 time: 0.3430 data_time: 0.3100 memory: 1391 2023/04/14 12:08:58 - mmengine - INFO - Epoch(val) [77][ 60/155] eta: 0:00:36 time: 0.3672 data_time: 0.3329 memory: 1391 2023/04/14 12:09:06 - mmengine - INFO - Epoch(val) [77][ 80/155] eta: 0:00:28 time: 0.3759 data_time: 0.3421 memory: 1391 2023/04/14 12:09:14 - mmengine - INFO - Epoch(val) [77][100/155] eta: 0:00:21 time: 0.4245 data_time: 0.3907 memory: 1391 2023/04/14 12:09:20 - mmengine - INFO - Epoch(val) [77][120/155] eta: 0:00:13 time: 0.3210 data_time: 0.2882 memory: 1391 2023/04/14 12:09:28 - mmengine - INFO - Epoch(val) [77][140/155] eta: 0:00:05 time: 0.3635 data_time: 0.3291 memory: 1391 2023/04/14 12:09:37 - mmengine - INFO - Epoch(val) [77][155/155] acc/top1: 0.6640 acc/top5: 0.8721 acc/mean1: 0.6639 data_time: 0.3266 time: 0.3598 2023/04/14 12:09:47 - mmengine - INFO - Epoch(train) [78][ 20/1879] lr: 2.0000e-03 eta: 4:26:24 time: 0.4961 data_time: 0.2944 memory: 6717 grad_norm: 3.4383 loss: 1.2852 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2852 2023/04/14 12:09:53 - mmengine - INFO - Epoch(train) [78][ 40/1879] lr: 2.0000e-03 eta: 4:26:16 time: 0.3030 data_time: 0.1554 memory: 6717 grad_norm: 3.3750 loss: 1.2811 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2811 2023/04/14 12:10:01 - mmengine - INFO - Epoch(train) [78][ 60/1879] lr: 2.0000e-03 eta: 4:26:09 time: 0.4275 data_time: 0.2258 memory: 6717 grad_norm: 3.3571 loss: 1.0975 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0975 2023/04/14 12:10:08 - mmengine - INFO - Epoch(train) [78][ 80/1879] lr: 2.0000e-03 eta: 4:26:02 time: 0.3153 data_time: 0.1486 memory: 6717 grad_norm: 3.3528 loss: 1.2379 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2379 2023/04/14 12:10:16 - mmengine - INFO - Epoch(train) [78][ 100/1879] lr: 2.0000e-03 eta: 4:25:54 time: 0.4104 data_time: 0.2026 memory: 6717 grad_norm: 3.3456 loss: 1.1612 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1612 2023/04/14 12:10:22 - mmengine - INFO - Epoch(train) [78][ 120/1879] lr: 2.0000e-03 eta: 4:25:47 time: 0.3253 data_time: 0.1418 memory: 6717 grad_norm: 3.3156 loss: 1.2866 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2866 2023/04/14 12:10:31 - mmengine - INFO - Epoch(train) [78][ 140/1879] lr: 2.0000e-03 eta: 4:25:40 time: 0.4330 data_time: 0.2360 memory: 6717 grad_norm: 3.3392 loss: 1.3804 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3804 2023/04/14 12:10:38 - mmengine - INFO - Epoch(train) [78][ 160/1879] lr: 2.0000e-03 eta: 4:25:32 time: 0.3615 data_time: 0.0996 memory: 6717 grad_norm: 3.4489 loss: 1.0455 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0455 2023/04/14 12:10:45 - mmengine - INFO - Epoch(train) [78][ 180/1879] lr: 2.0000e-03 eta: 4:25:25 time: 0.3527 data_time: 0.0907 memory: 6717 grad_norm: 3.3063 loss: 1.0739 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0739 2023/04/14 12:10:53 - mmengine - INFO - Epoch(train) [78][ 200/1879] lr: 2.0000e-03 eta: 4:25:18 time: 0.4046 data_time: 0.0420 memory: 6717 grad_norm: 3.3562 loss: 1.2586 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2586 2023/04/14 12:11:00 - mmengine - INFO - Epoch(train) [78][ 220/1879] lr: 2.0000e-03 eta: 4:25:10 time: 0.3144 data_time: 0.0587 memory: 6717 grad_norm: 3.3728 loss: 1.2557 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2557 2023/04/14 12:11:08 - mmengine - INFO - Epoch(train) [78][ 240/1879] lr: 2.0000e-03 eta: 4:25:03 time: 0.3963 data_time: 0.0550 memory: 6717 grad_norm: 3.4338 loss: 1.2524 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2524 2023/04/14 12:11:15 - mmengine - INFO - Epoch(train) [78][ 260/1879] lr: 2.0000e-03 eta: 4:24:55 time: 0.3554 data_time: 0.0197 memory: 6717 grad_norm: 3.3819 loss: 1.1124 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1124 2023/04/14 12:11:23 - mmengine - INFO - Epoch(train) [78][ 280/1879] lr: 2.0000e-03 eta: 4:24:48 time: 0.4058 data_time: 0.0226 memory: 6717 grad_norm: 3.3568 loss: 1.1865 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1865 2023/04/14 12:11:30 - mmengine - INFO - Epoch(train) [78][ 300/1879] lr: 2.0000e-03 eta: 4:24:40 time: 0.3508 data_time: 0.1030 memory: 6717 grad_norm: 3.3574 loss: 1.1083 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1083 2023/04/14 12:11:38 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 12:11:38 - mmengine - INFO - Epoch(train) [78][ 320/1879] lr: 2.0000e-03 eta: 4:24:33 time: 0.4206 data_time: 0.0149 memory: 6717 grad_norm: 3.3586 loss: 1.0183 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0183 2023/04/14 12:11:45 - mmengine - INFO - Epoch(train) [78][ 340/1879] lr: 2.0000e-03 eta: 4:24:26 time: 0.3346 data_time: 0.0136 memory: 6717 grad_norm: 3.4540 loss: 1.1558 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1558 2023/04/14 12:11:52 - mmengine - INFO - Epoch(train) [78][ 360/1879] lr: 2.0000e-03 eta: 4:24:18 time: 0.3697 data_time: 0.0148 memory: 6717 grad_norm: 3.4200 loss: 1.1618 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1618 2023/04/14 12:11:59 - mmengine - INFO - Epoch(train) [78][ 380/1879] lr: 2.0000e-03 eta: 4:24:11 time: 0.3193 data_time: 0.0136 memory: 6717 grad_norm: 3.3028 loss: 1.1766 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1766 2023/04/14 12:12:07 - mmengine - INFO - Epoch(train) [78][ 400/1879] lr: 2.0000e-03 eta: 4:24:03 time: 0.4198 data_time: 0.0156 memory: 6717 grad_norm: 3.4172 loss: 1.0631 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0631 2023/04/14 12:12:14 - mmengine - INFO - Epoch(train) [78][ 420/1879] lr: 2.0000e-03 eta: 4:23:56 time: 0.3295 data_time: 0.0136 memory: 6717 grad_norm: 3.3528 loss: 1.0394 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0394 2023/04/14 12:12:22 - mmengine - INFO - Epoch(train) [78][ 440/1879] lr: 2.0000e-03 eta: 4:23:49 time: 0.3925 data_time: 0.0154 memory: 6717 grad_norm: 3.4335 loss: 1.1502 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1502 2023/04/14 12:12:29 - mmengine - INFO - Epoch(train) [78][ 460/1879] lr: 2.0000e-03 eta: 4:23:41 time: 0.3520 data_time: 0.0135 memory: 6717 grad_norm: 3.3765 loss: 1.1374 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1374 2023/04/14 12:12:37 - mmengine - INFO - Epoch(train) [78][ 480/1879] lr: 2.0000e-03 eta: 4:23:34 time: 0.3943 data_time: 0.0157 memory: 6717 grad_norm: 3.3950 loss: 1.2074 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2074 2023/04/14 12:12:43 - mmengine - INFO - Epoch(train) [78][ 500/1879] lr: 2.0000e-03 eta: 4:23:26 time: 0.3102 data_time: 0.0247 memory: 6717 grad_norm: 3.3442 loss: 0.9757 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9757 2023/04/14 12:12:51 - mmengine - INFO - Epoch(train) [78][ 520/1879] lr: 2.0000e-03 eta: 4:23:19 time: 0.4076 data_time: 0.0149 memory: 6717 grad_norm: 3.4012 loss: 1.1609 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1609 2023/04/14 12:12:58 - mmengine - INFO - Epoch(train) [78][ 540/1879] lr: 2.0000e-03 eta: 4:23:11 time: 0.3259 data_time: 0.0135 memory: 6717 grad_norm: 3.4991 loss: 1.4136 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.4136 2023/04/14 12:13:06 - mmengine - INFO - Epoch(train) [78][ 560/1879] lr: 2.0000e-03 eta: 4:23:04 time: 0.4219 data_time: 0.0152 memory: 6717 grad_norm: 3.3956 loss: 1.2077 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2077 2023/04/14 12:13:13 - mmengine - INFO - Epoch(train) [78][ 580/1879] lr: 2.0000e-03 eta: 4:22:57 time: 0.3467 data_time: 0.0141 memory: 6717 grad_norm: 3.4235 loss: 1.1772 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1772 2023/04/14 12:13:21 - mmengine - INFO - Epoch(train) [78][ 600/1879] lr: 2.0000e-03 eta: 4:22:49 time: 0.3989 data_time: 0.0134 memory: 6717 grad_norm: 3.2626 loss: 1.0958 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.0958 2023/04/14 12:13:27 - mmengine - INFO - Epoch(train) [78][ 620/1879] lr: 2.0000e-03 eta: 4:22:42 time: 0.3215 data_time: 0.0146 memory: 6717 grad_norm: 3.4045 loss: 1.3796 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3796 2023/04/14 12:13:35 - mmengine - INFO - Epoch(train) [78][ 640/1879] lr: 2.0000e-03 eta: 4:22:34 time: 0.3706 data_time: 0.0168 memory: 6717 grad_norm: 3.3451 loss: 1.1504 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1504 2023/04/14 12:13:42 - mmengine - INFO - Epoch(train) [78][ 660/1879] lr: 2.0000e-03 eta: 4:22:27 time: 0.3528 data_time: 0.0131 memory: 6717 grad_norm: 3.3125 loss: 1.1803 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1803 2023/04/14 12:13:50 - mmengine - INFO - Epoch(train) [78][ 680/1879] lr: 2.0000e-03 eta: 4:22:19 time: 0.3932 data_time: 0.0147 memory: 6717 grad_norm: 3.3971 loss: 1.2912 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2912 2023/04/14 12:13:57 - mmengine - INFO - Epoch(train) [78][ 700/1879] lr: 2.0000e-03 eta: 4:22:12 time: 0.3502 data_time: 0.0146 memory: 6717 grad_norm: 3.3237 loss: 1.1807 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1807 2023/04/14 12:14:05 - mmengine - INFO - Epoch(train) [78][ 720/1879] lr: 2.0000e-03 eta: 4:22:05 time: 0.4054 data_time: 0.0154 memory: 6717 grad_norm: 3.3711 loss: 1.0291 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0291 2023/04/14 12:14:11 - mmengine - INFO - Epoch(train) [78][ 740/1879] lr: 2.0000e-03 eta: 4:21:57 time: 0.3253 data_time: 0.0129 memory: 6717 grad_norm: 3.3038 loss: 1.3057 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3057 2023/04/14 12:14:20 - mmengine - INFO - Epoch(train) [78][ 760/1879] lr: 2.0000e-03 eta: 4:21:50 time: 0.4446 data_time: 0.0158 memory: 6717 grad_norm: 3.3174 loss: 1.1378 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1378 2023/04/14 12:14:27 - mmengine - INFO - Epoch(train) [78][ 780/1879] lr: 2.0000e-03 eta: 4:21:43 time: 0.3443 data_time: 0.0149 memory: 6717 grad_norm: 3.4531 loss: 1.0979 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0979 2023/04/14 12:14:35 - mmengine - INFO - Epoch(train) [78][ 800/1879] lr: 2.0000e-03 eta: 4:21:35 time: 0.4142 data_time: 0.0135 memory: 6717 grad_norm: 3.2766 loss: 1.0566 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0566 2023/04/14 12:14:42 - mmengine - INFO - Epoch(train) [78][ 820/1879] lr: 2.0000e-03 eta: 4:21:28 time: 0.3458 data_time: 0.0158 memory: 6717 grad_norm: 3.3407 loss: 1.0844 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0844 2023/04/14 12:14:51 - mmengine - INFO - Epoch(train) [78][ 840/1879] lr: 2.0000e-03 eta: 4:21:21 time: 0.4304 data_time: 0.0139 memory: 6717 grad_norm: 3.3443 loss: 1.2491 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2491 2023/04/14 12:14:57 - mmengine - INFO - Epoch(train) [78][ 860/1879] lr: 2.0000e-03 eta: 4:21:13 time: 0.3261 data_time: 0.0156 memory: 6717 grad_norm: 3.4201 loss: 1.0865 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0865 2023/04/14 12:15:05 - mmengine - INFO - Epoch(train) [78][ 880/1879] lr: 2.0000e-03 eta: 4:21:06 time: 0.3981 data_time: 0.0125 memory: 6717 grad_norm: 3.3948 loss: 1.2153 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2153 2023/04/14 12:15:12 - mmengine - INFO - Epoch(train) [78][ 900/1879] lr: 2.0000e-03 eta: 4:20:58 time: 0.3134 data_time: 0.0142 memory: 6717 grad_norm: 3.3725 loss: 1.3153 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3153 2023/04/14 12:15:20 - mmengine - INFO - Epoch(train) [78][ 920/1879] lr: 2.0000e-03 eta: 4:20:51 time: 0.3953 data_time: 0.0142 memory: 6717 grad_norm: 3.3486 loss: 1.0846 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0846 2023/04/14 12:15:26 - mmengine - INFO - Epoch(train) [78][ 940/1879] lr: 2.0000e-03 eta: 4:20:43 time: 0.3239 data_time: 0.0150 memory: 6717 grad_norm: 3.4040 loss: 1.0731 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0731 2023/04/14 12:15:34 - mmengine - INFO - Epoch(train) [78][ 960/1879] lr: 2.0000e-03 eta: 4:20:36 time: 0.4147 data_time: 0.0149 memory: 6717 grad_norm: 3.4891 loss: 1.1055 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1055 2023/04/14 12:15:41 - mmengine - INFO - Epoch(train) [78][ 980/1879] lr: 2.0000e-03 eta: 4:20:28 time: 0.3242 data_time: 0.0161 memory: 6717 grad_norm: 3.3860 loss: 1.1404 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1404 2023/04/14 12:15:49 - mmengine - INFO - Epoch(train) [78][1000/1879] lr: 2.0000e-03 eta: 4:20:21 time: 0.4165 data_time: 0.0144 memory: 6717 grad_norm: 3.3252 loss: 0.9310 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9310 2023/04/14 12:15:55 - mmengine - INFO - Epoch(train) [78][1020/1879] lr: 2.0000e-03 eta: 4:20:14 time: 0.3067 data_time: 0.0150 memory: 6717 grad_norm: 3.4003 loss: 1.2043 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2043 2023/04/14 12:16:04 - mmengine - INFO - Epoch(train) [78][1040/1879] lr: 2.0000e-03 eta: 4:20:07 time: 0.4376 data_time: 0.0139 memory: 6717 grad_norm: 3.3071 loss: 1.0971 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0971 2023/04/14 12:16:10 - mmengine - INFO - Epoch(train) [78][1060/1879] lr: 2.0000e-03 eta: 4:19:59 time: 0.3179 data_time: 0.0152 memory: 6717 grad_norm: 3.3018 loss: 1.0777 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0777 2023/04/14 12:16:19 - mmengine - INFO - Epoch(train) [78][1080/1879] lr: 2.0000e-03 eta: 4:19:52 time: 0.4051 data_time: 0.0128 memory: 6717 grad_norm: 3.4103 loss: 1.1518 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1518 2023/04/14 12:16:26 - mmengine - INFO - Epoch(train) [78][1100/1879] lr: 2.0000e-03 eta: 4:19:44 time: 0.3574 data_time: 0.0165 memory: 6717 grad_norm: 3.4591 loss: 1.1670 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1670 2023/04/14 12:16:34 - mmengine - INFO - Epoch(train) [78][1120/1879] lr: 2.0000e-03 eta: 4:19:37 time: 0.4034 data_time: 0.0131 memory: 6717 grad_norm: 3.4632 loss: 1.3082 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.3082 2023/04/14 12:16:41 - mmengine - INFO - Epoch(train) [78][1140/1879] lr: 2.0000e-03 eta: 4:19:29 time: 0.3501 data_time: 0.0146 memory: 6717 grad_norm: 3.4149 loss: 1.1779 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1779 2023/04/14 12:16:49 - mmengine - INFO - Epoch(train) [78][1160/1879] lr: 2.0000e-03 eta: 4:19:22 time: 0.4048 data_time: 0.0135 memory: 6717 grad_norm: 3.3791 loss: 1.0439 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0439 2023/04/14 12:16:56 - mmengine - INFO - Epoch(train) [78][1180/1879] lr: 2.0000e-03 eta: 4:19:15 time: 0.3377 data_time: 0.0142 memory: 6717 grad_norm: 3.5499 loss: 1.2598 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.2598 2023/04/14 12:17:03 - mmengine - INFO - Epoch(train) [78][1200/1879] lr: 2.0000e-03 eta: 4:19:07 time: 0.3507 data_time: 0.0146 memory: 6717 grad_norm: 3.3678 loss: 1.1250 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1250 2023/04/14 12:17:11 - mmengine - INFO - Epoch(train) [78][1220/1879] lr: 2.0000e-03 eta: 4:19:00 time: 0.4009 data_time: 0.0148 memory: 6717 grad_norm: 3.3418 loss: 1.2475 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2475 2023/04/14 12:17:17 - mmengine - INFO - Epoch(train) [78][1240/1879] lr: 2.0000e-03 eta: 4:18:52 time: 0.3327 data_time: 0.0135 memory: 6717 grad_norm: 3.3259 loss: 1.2599 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2599 2023/04/14 12:17:25 - mmengine - INFO - Epoch(train) [78][1260/1879] lr: 2.0000e-03 eta: 4:18:45 time: 0.3939 data_time: 0.0169 memory: 6717 grad_norm: 3.3433 loss: 1.2061 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2061 2023/04/14 12:17:32 - mmengine - INFO - Epoch(train) [78][1280/1879] lr: 2.0000e-03 eta: 4:18:37 time: 0.3363 data_time: 0.0132 memory: 6717 grad_norm: 3.2950 loss: 1.2508 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2508 2023/04/14 12:17:39 - mmengine - INFO - Epoch(train) [78][1300/1879] lr: 2.0000e-03 eta: 4:18:30 time: 0.3572 data_time: 0.0153 memory: 6717 grad_norm: 3.4204 loss: 1.1041 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1041 2023/04/14 12:17:45 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 12:17:46 - mmengine - INFO - Epoch(train) [78][1320/1879] lr: 2.0000e-03 eta: 4:18:22 time: 0.3302 data_time: 0.0134 memory: 6717 grad_norm: 3.3371 loss: 1.1523 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1523 2023/04/14 12:17:55 - mmengine - INFO - Epoch(train) [78][1340/1879] lr: 2.0000e-03 eta: 4:18:15 time: 0.4403 data_time: 0.0158 memory: 6717 grad_norm: 3.3269 loss: 1.1451 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1451 2023/04/14 12:18:01 - mmengine - INFO - Epoch(train) [78][1360/1879] lr: 2.0000e-03 eta: 4:18:08 time: 0.3259 data_time: 0.0129 memory: 6717 grad_norm: 3.4620 loss: 1.2900 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2900 2023/04/14 12:18:09 - mmengine - INFO - Epoch(train) [78][1380/1879] lr: 2.0000e-03 eta: 4:18:01 time: 0.4177 data_time: 0.0159 memory: 6717 grad_norm: 3.3461 loss: 1.3340 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3340 2023/04/14 12:18:16 - mmengine - INFO - Epoch(train) [78][1400/1879] lr: 2.0000e-03 eta: 4:17:53 time: 0.3152 data_time: 0.0130 memory: 6717 grad_norm: 3.4029 loss: 1.3274 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3274 2023/04/14 12:18:24 - mmengine - INFO - Epoch(train) [78][1420/1879] lr: 2.0000e-03 eta: 4:17:46 time: 0.4091 data_time: 0.0157 memory: 6717 grad_norm: 3.3590 loss: 1.1065 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1065 2023/04/14 12:18:31 - mmengine - INFO - Epoch(train) [78][1440/1879] lr: 2.0000e-03 eta: 4:17:38 time: 0.3309 data_time: 0.0123 memory: 6717 grad_norm: 3.3707 loss: 1.2801 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2801 2023/04/14 12:18:38 - mmengine - INFO - Epoch(train) [78][1460/1879] lr: 2.0000e-03 eta: 4:17:31 time: 0.3899 data_time: 0.0170 memory: 6717 grad_norm: 3.2791 loss: 1.1382 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1382 2023/04/14 12:18:46 - mmengine - INFO - Epoch(train) [78][1480/1879] lr: 2.0000e-03 eta: 4:17:23 time: 0.3620 data_time: 0.0136 memory: 6717 grad_norm: 3.3819 loss: 1.1669 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1669 2023/04/14 12:18:53 - mmengine - INFO - Epoch(train) [78][1500/1879] lr: 2.0000e-03 eta: 4:17:16 time: 0.3720 data_time: 0.0170 memory: 6717 grad_norm: 3.3296 loss: 1.2326 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2326 2023/04/14 12:19:01 - mmengine - INFO - Epoch(train) [78][1520/1879] lr: 2.0000e-03 eta: 4:17:09 time: 0.3785 data_time: 0.0130 memory: 6717 grad_norm: 3.3294 loss: 1.1822 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1822 2023/04/14 12:19:08 - mmengine - INFO - Epoch(train) [78][1540/1879] lr: 2.0000e-03 eta: 4:17:01 time: 0.3463 data_time: 0.0163 memory: 6717 grad_norm: 3.2767 loss: 1.2061 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2061 2023/04/14 12:19:16 - mmengine - INFO - Epoch(train) [78][1560/1879] lr: 2.0000e-03 eta: 4:16:54 time: 0.3999 data_time: 0.0131 memory: 6717 grad_norm: 3.3917 loss: 1.3160 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.3160 2023/04/14 12:19:22 - mmengine - INFO - Epoch(train) [78][1580/1879] lr: 2.0000e-03 eta: 4:16:46 time: 0.3177 data_time: 0.0239 memory: 6717 grad_norm: 3.3518 loss: 1.0632 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0632 2023/04/14 12:19:30 - mmengine - INFO - Epoch(train) [78][1600/1879] lr: 2.0000e-03 eta: 4:16:39 time: 0.4228 data_time: 0.0328 memory: 6717 grad_norm: 3.3520 loss: 1.1677 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1677 2023/04/14 12:19:37 - mmengine - INFO - Epoch(train) [78][1620/1879] lr: 2.0000e-03 eta: 4:16:31 time: 0.3407 data_time: 0.0136 memory: 6717 grad_norm: 3.3953 loss: 1.1284 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1284 2023/04/14 12:19:46 - mmengine - INFO - Epoch(train) [78][1640/1879] lr: 2.0000e-03 eta: 4:16:24 time: 0.4278 data_time: 0.0134 memory: 6717 grad_norm: 3.3914 loss: 1.2091 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2091 2023/04/14 12:19:52 - mmengine - INFO - Epoch(train) [78][1660/1879] lr: 2.0000e-03 eta: 4:16:17 time: 0.3123 data_time: 0.0167 memory: 6717 grad_norm: 3.2998 loss: 1.0407 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0407 2023/04/14 12:20:01 - mmengine - INFO - Epoch(train) [78][1680/1879] lr: 2.0000e-03 eta: 4:16:10 time: 0.4297 data_time: 0.0121 memory: 6717 grad_norm: 3.3511 loss: 1.3816 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3816 2023/04/14 12:20:07 - mmengine - INFO - Epoch(train) [78][1700/1879] lr: 2.0000e-03 eta: 4:16:02 time: 0.3288 data_time: 0.0175 memory: 6717 grad_norm: 3.4178 loss: 1.2104 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2104 2023/04/14 12:20:15 - mmengine - INFO - Epoch(train) [78][1720/1879] lr: 2.0000e-03 eta: 4:15:55 time: 0.3770 data_time: 0.0140 memory: 6717 grad_norm: 3.4745 loss: 1.1355 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1355 2023/04/14 12:20:22 - mmengine - INFO - Epoch(train) [78][1740/1879] lr: 2.0000e-03 eta: 4:15:47 time: 0.3413 data_time: 0.0150 memory: 6717 grad_norm: 3.3972 loss: 1.1046 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1046 2023/04/14 12:20:29 - mmengine - INFO - Epoch(train) [78][1760/1879] lr: 2.0000e-03 eta: 4:15:40 time: 0.3499 data_time: 0.0148 memory: 6717 grad_norm: 3.3880 loss: 1.1507 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1507 2023/04/14 12:20:36 - mmengine - INFO - Epoch(train) [78][1780/1879] lr: 2.0000e-03 eta: 4:15:32 time: 0.3474 data_time: 0.0508 memory: 6717 grad_norm: 3.3700 loss: 1.1822 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1822 2023/04/14 12:20:43 - mmengine - INFO - Epoch(train) [78][1800/1879] lr: 2.0000e-03 eta: 4:15:25 time: 0.3771 data_time: 0.1243 memory: 6717 grad_norm: 3.4171 loss: 1.0544 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0544 2023/04/14 12:20:51 - mmengine - INFO - Epoch(train) [78][1820/1879] lr: 2.0000e-03 eta: 4:15:17 time: 0.3843 data_time: 0.0853 memory: 6717 grad_norm: 3.4684 loss: 1.1488 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1488 2023/04/14 12:20:59 - mmengine - INFO - Epoch(train) [78][1840/1879] lr: 2.0000e-03 eta: 4:15:10 time: 0.3940 data_time: 0.1597 memory: 6717 grad_norm: 3.4294 loss: 1.2539 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.2539 2023/04/14 12:21:06 - mmengine - INFO - Epoch(train) [78][1860/1879] lr: 2.0000e-03 eta: 4:15:03 time: 0.3494 data_time: 0.0933 memory: 6717 grad_norm: 3.4131 loss: 1.2412 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2412 2023/04/14 12:21:12 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 12:21:12 - mmengine - INFO - Epoch(train) [78][1879/1879] lr: 2.0000e-03 eta: 4:14:55 time: 0.3381 data_time: 0.1232 memory: 6717 grad_norm: 3.4764 loss: 1.3959 top1_acc: 0.4286 top5_acc: 0.8571 loss_cls: 1.3959 2023/04/14 12:21:12 - mmengine - INFO - Saving checkpoint at 78 epochs 2023/04/14 12:21:22 - mmengine - INFO - Epoch(val) [78][ 20/155] eta: 0:01:00 time: 0.4478 data_time: 0.4142 memory: 1391 2023/04/14 12:21:28 - mmengine - INFO - Epoch(val) [78][ 40/155] eta: 0:00:44 time: 0.3270 data_time: 0.2936 memory: 1391 2023/04/14 12:21:37 - mmengine - INFO - Epoch(val) [78][ 60/155] eta: 0:00:38 time: 0.4341 data_time: 0.4012 memory: 1391 2023/04/14 12:21:43 - mmengine - INFO - Epoch(val) [78][ 80/155] eta: 0:00:28 time: 0.3220 data_time: 0.2886 memory: 1391 2023/04/14 12:21:52 - mmengine - INFO - Epoch(val) [78][100/155] eta: 0:00:21 time: 0.4499 data_time: 0.4163 memory: 1391 2023/04/14 12:21:58 - mmengine - INFO - Epoch(val) [78][120/155] eta: 0:00:13 time: 0.2988 data_time: 0.2655 memory: 1391 2023/04/14 12:22:08 - mmengine - INFO - Epoch(val) [78][140/155] eta: 0:00:05 time: 0.4856 data_time: 0.4529 memory: 1391 2023/04/14 12:22:15 - mmengine - INFO - Epoch(val) [78][155/155] acc/top1: 0.6635 acc/top5: 0.8720 acc/mean1: 0.6634 data_time: 0.4174 time: 0.4491 2023/04/14 12:22:25 - mmengine - INFO - Epoch(train) [79][ 20/1879] lr: 2.0000e-03 eta: 4:14:49 time: 0.4966 data_time: 0.2122 memory: 6717 grad_norm: 3.3341 loss: 1.1442 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1442 2023/04/14 12:22:32 - mmengine - INFO - Epoch(train) [79][ 40/1879] lr: 2.0000e-03 eta: 4:14:41 time: 0.3347 data_time: 0.0353 memory: 6717 grad_norm: 3.3261 loss: 1.1807 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1807 2023/04/14 12:22:40 - mmengine - INFO - Epoch(train) [79][ 60/1879] lr: 2.0000e-03 eta: 4:14:34 time: 0.4244 data_time: 0.0144 memory: 6717 grad_norm: 3.3823 loss: 1.1101 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1101 2023/04/14 12:22:47 - mmengine - INFO - Epoch(train) [79][ 80/1879] lr: 2.0000e-03 eta: 4:14:26 time: 0.3193 data_time: 0.0140 memory: 6717 grad_norm: 3.3810 loss: 1.0776 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0776 2023/04/14 12:22:55 - mmengine - INFO - Epoch(train) [79][ 100/1879] lr: 2.0000e-03 eta: 4:14:19 time: 0.4136 data_time: 0.0149 memory: 6717 grad_norm: 3.3694 loss: 1.1704 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1704 2023/04/14 12:23:02 - mmengine - INFO - Epoch(train) [79][ 120/1879] lr: 2.0000e-03 eta: 4:14:12 time: 0.3294 data_time: 0.0144 memory: 6717 grad_norm: 3.3487 loss: 1.3131 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3131 2023/04/14 12:23:10 - mmengine - INFO - Epoch(train) [79][ 140/1879] lr: 2.0000e-03 eta: 4:14:05 time: 0.4434 data_time: 0.0151 memory: 6717 grad_norm: 3.4627 loss: 1.2631 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2631 2023/04/14 12:23:17 - mmengine - INFO - Epoch(train) [79][ 160/1879] lr: 2.0000e-03 eta: 4:13:57 time: 0.3063 data_time: 0.0142 memory: 6717 grad_norm: 3.4310 loss: 1.1898 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1898 2023/04/14 12:23:25 - mmengine - INFO - Epoch(train) [79][ 180/1879] lr: 2.0000e-03 eta: 4:13:50 time: 0.4205 data_time: 0.0148 memory: 6717 grad_norm: 3.3495 loss: 1.0665 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0665 2023/04/14 12:23:32 - mmengine - INFO - Epoch(train) [79][ 200/1879] lr: 2.0000e-03 eta: 4:13:42 time: 0.3276 data_time: 0.0135 memory: 6717 grad_norm: 3.3662 loss: 1.1230 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1230 2023/04/14 12:23:40 - mmengine - INFO - Epoch(train) [79][ 220/1879] lr: 2.0000e-03 eta: 4:13:35 time: 0.4251 data_time: 0.0156 memory: 6717 grad_norm: 3.3269 loss: 1.0960 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0960 2023/04/14 12:23:46 - mmengine - INFO - Epoch(train) [79][ 240/1879] lr: 2.0000e-03 eta: 4:13:27 time: 0.3035 data_time: 0.0132 memory: 6717 grad_norm: 3.3232 loss: 1.3563 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3563 2023/04/14 12:23:55 - mmengine - INFO - Epoch(train) [79][ 260/1879] lr: 2.0000e-03 eta: 4:13:20 time: 0.4244 data_time: 0.0144 memory: 6717 grad_norm: 3.3494 loss: 1.1962 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1962 2023/04/14 12:24:01 - mmengine - INFO - Epoch(train) [79][ 280/1879] lr: 2.0000e-03 eta: 4:13:12 time: 0.2991 data_time: 0.0146 memory: 6717 grad_norm: 3.3202 loss: 1.1890 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1890 2023/04/14 12:24:09 - mmengine - INFO - Epoch(train) [79][ 300/1879] lr: 2.0000e-03 eta: 4:13:05 time: 0.4056 data_time: 0.0147 memory: 6717 grad_norm: 3.3382 loss: 1.0326 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0326 2023/04/14 12:24:15 - mmengine - INFO - Epoch(train) [79][ 320/1879] lr: 2.0000e-03 eta: 4:12:57 time: 0.3052 data_time: 0.0147 memory: 6717 grad_norm: 3.4280 loss: 1.2767 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2767 2023/04/14 12:24:23 - mmengine - INFO - Epoch(train) [79][ 340/1879] lr: 2.0000e-03 eta: 4:12:50 time: 0.4040 data_time: 0.0147 memory: 6717 grad_norm: 3.4395 loss: 1.1266 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1266 2023/04/14 12:24:30 - mmengine - INFO - Epoch(train) [79][ 360/1879] lr: 2.0000e-03 eta: 4:12:43 time: 0.3374 data_time: 0.0145 memory: 6717 grad_norm: 3.3676 loss: 1.1409 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1409 2023/04/14 12:24:38 - mmengine - INFO - Epoch(train) [79][ 380/1879] lr: 2.0000e-03 eta: 4:12:35 time: 0.4141 data_time: 0.0145 memory: 6717 grad_norm: 3.3498 loss: 1.2120 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2120 2023/04/14 12:24:45 - mmengine - INFO - Epoch(train) [79][ 400/1879] lr: 2.0000e-03 eta: 4:12:28 time: 0.3434 data_time: 0.0144 memory: 6717 grad_norm: 3.3521 loss: 1.2802 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2802 2023/04/14 12:24:53 - mmengine - INFO - Epoch(train) [79][ 420/1879] lr: 2.0000e-03 eta: 4:12:21 time: 0.4224 data_time: 0.0150 memory: 6717 grad_norm: 3.3552 loss: 1.1281 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1281 2023/04/14 12:25:00 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 12:25:00 - mmengine - INFO - Epoch(train) [79][ 440/1879] lr: 2.0000e-03 eta: 4:12:13 time: 0.3399 data_time: 0.0238 memory: 6717 grad_norm: 3.3401 loss: 1.0835 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0835 2023/04/14 12:25:07 - mmengine - INFO - Epoch(train) [79][ 460/1879] lr: 2.0000e-03 eta: 4:12:06 time: 0.3638 data_time: 0.0340 memory: 6717 grad_norm: 3.4908 loss: 1.2159 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2159 2023/04/14 12:25:14 - mmengine - INFO - Epoch(train) [79][ 480/1879] lr: 2.0000e-03 eta: 4:11:58 time: 0.3408 data_time: 0.0241 memory: 6717 grad_norm: 3.3756 loss: 1.1847 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1847 2023/04/14 12:25:22 - mmengine - INFO - Epoch(train) [79][ 500/1879] lr: 2.0000e-03 eta: 4:11:51 time: 0.3666 data_time: 0.1317 memory: 6717 grad_norm: 3.4220 loss: 1.1985 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1985 2023/04/14 12:25:28 - mmengine - INFO - Epoch(train) [79][ 520/1879] lr: 2.0000e-03 eta: 4:11:43 time: 0.3451 data_time: 0.1540 memory: 6717 grad_norm: 3.3895 loss: 1.1151 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1151 2023/04/14 12:25:36 - mmengine - INFO - Epoch(train) [79][ 540/1879] lr: 2.0000e-03 eta: 4:11:36 time: 0.3763 data_time: 0.1663 memory: 6717 grad_norm: 3.4014 loss: 1.1888 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1888 2023/04/14 12:25:44 - mmengine - INFO - Epoch(train) [79][ 560/1879] lr: 2.0000e-03 eta: 4:11:28 time: 0.3822 data_time: 0.0279 memory: 6717 grad_norm: 3.4188 loss: 1.2791 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.2791 2023/04/14 12:25:51 - mmengine - INFO - Epoch(train) [79][ 580/1879] lr: 2.0000e-03 eta: 4:11:21 time: 0.3734 data_time: 0.0729 memory: 6717 grad_norm: 3.3052 loss: 1.0063 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0063 2023/04/14 12:25:58 - mmengine - INFO - Epoch(train) [79][ 600/1879] lr: 2.0000e-03 eta: 4:11:13 time: 0.3280 data_time: 0.0445 memory: 6717 grad_norm: 3.4644 loss: 1.0608 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0608 2023/04/14 12:26:06 - mmengine - INFO - Epoch(train) [79][ 620/1879] lr: 2.0000e-03 eta: 4:11:06 time: 0.4229 data_time: 0.1174 memory: 6717 grad_norm: 3.3621 loss: 1.1423 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1423 2023/04/14 12:26:13 - mmengine - INFO - Epoch(train) [79][ 640/1879] lr: 2.0000e-03 eta: 4:10:59 time: 0.3406 data_time: 0.0839 memory: 6717 grad_norm: 3.4589 loss: 1.2838 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2838 2023/04/14 12:26:21 - mmengine - INFO - Epoch(train) [79][ 660/1879] lr: 2.0000e-03 eta: 4:10:51 time: 0.3839 data_time: 0.1376 memory: 6717 grad_norm: 3.3255 loss: 0.9981 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9981 2023/04/14 12:26:28 - mmengine - INFO - Epoch(train) [79][ 680/1879] lr: 2.0000e-03 eta: 4:10:44 time: 0.3790 data_time: 0.2232 memory: 6717 grad_norm: 3.4348 loss: 1.2137 top1_acc: 0.5625 top5_acc: 0.6250 loss_cls: 1.2137 2023/04/14 12:26:35 - mmengine - INFO - Epoch(train) [79][ 700/1879] lr: 2.0000e-03 eta: 4:10:36 time: 0.3309 data_time: 0.1813 memory: 6717 grad_norm: 3.2816 loss: 1.1977 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1977 2023/04/14 12:26:43 - mmengine - INFO - Epoch(train) [79][ 720/1879] lr: 2.0000e-03 eta: 4:10:29 time: 0.3981 data_time: 0.2269 memory: 6717 grad_norm: 3.3668 loss: 1.0713 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0713 2023/04/14 12:26:50 - mmengine - INFO - Epoch(train) [79][ 740/1879] lr: 2.0000e-03 eta: 4:10:22 time: 0.3624 data_time: 0.1536 memory: 6717 grad_norm: 3.4039 loss: 1.2595 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2595 2023/04/14 12:26:58 - mmengine - INFO - Epoch(train) [79][ 760/1879] lr: 2.0000e-03 eta: 4:10:15 time: 0.3992 data_time: 0.2064 memory: 6717 grad_norm: 3.4554 loss: 1.1876 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1876 2023/04/14 12:27:05 - mmengine - INFO - Epoch(train) [79][ 780/1879] lr: 2.0000e-03 eta: 4:10:07 time: 0.3259 data_time: 0.1862 memory: 6717 grad_norm: 3.3337 loss: 1.2091 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2091 2023/04/14 12:27:13 - mmengine - INFO - Epoch(train) [79][ 800/1879] lr: 2.0000e-03 eta: 4:10:00 time: 0.4184 data_time: 0.2755 memory: 6717 grad_norm: 3.6026 loss: 1.0618 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0618 2023/04/14 12:27:19 - mmengine - INFO - Epoch(train) [79][ 820/1879] lr: 2.0000e-03 eta: 4:09:52 time: 0.3053 data_time: 0.1627 memory: 6717 grad_norm: 3.3230 loss: 1.0502 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0502 2023/04/14 12:27:27 - mmengine - INFO - Epoch(train) [79][ 840/1879] lr: 2.0000e-03 eta: 4:09:45 time: 0.4121 data_time: 0.2729 memory: 6717 grad_norm: 3.4207 loss: 1.2140 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2140 2023/04/14 12:27:34 - mmengine - INFO - Epoch(train) [79][ 860/1879] lr: 2.0000e-03 eta: 4:09:37 time: 0.3449 data_time: 0.2024 memory: 6717 grad_norm: 3.4449 loss: 1.1330 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1330 2023/04/14 12:27:42 - mmengine - INFO - Epoch(train) [79][ 880/1879] lr: 2.0000e-03 eta: 4:09:30 time: 0.4091 data_time: 0.2676 memory: 6717 grad_norm: 3.3534 loss: 1.0741 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0741 2023/04/14 12:27:49 - mmengine - INFO - Epoch(train) [79][ 900/1879] lr: 2.0000e-03 eta: 4:09:22 time: 0.3156 data_time: 0.1742 memory: 6717 grad_norm: 3.4311 loss: 1.1168 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1168 2023/04/14 12:27:57 - mmengine - INFO - Epoch(train) [79][ 920/1879] lr: 2.0000e-03 eta: 4:09:15 time: 0.4295 data_time: 0.2915 memory: 6717 grad_norm: 3.3616 loss: 1.1937 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1937 2023/04/14 12:28:04 - mmengine - INFO - Epoch(train) [79][ 940/1879] lr: 2.0000e-03 eta: 4:09:08 time: 0.3235 data_time: 0.1793 memory: 6717 grad_norm: 3.4181 loss: 1.1360 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1360 2023/04/14 12:28:11 - mmengine - INFO - Epoch(train) [79][ 960/1879] lr: 2.0000e-03 eta: 4:09:00 time: 0.3831 data_time: 0.1973 memory: 6717 grad_norm: 3.3889 loss: 1.2463 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2463 2023/04/14 12:28:19 - mmengine - INFO - Epoch(train) [79][ 980/1879] lr: 2.0000e-03 eta: 4:08:53 time: 0.3758 data_time: 0.0818 memory: 6717 grad_norm: 3.3755 loss: 1.2199 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2199 2023/04/14 12:28:26 - mmengine - INFO - Epoch(train) [79][1000/1879] lr: 2.0000e-03 eta: 4:08:45 time: 0.3428 data_time: 0.0514 memory: 6717 grad_norm: 3.3978 loss: 1.0920 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0920 2023/04/14 12:28:34 - mmengine - INFO - Epoch(train) [79][1020/1879] lr: 2.0000e-03 eta: 4:08:38 time: 0.3861 data_time: 0.1091 memory: 6717 grad_norm: 3.4655 loss: 1.1882 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1882 2023/04/14 12:28:41 - mmengine - INFO - Epoch(train) [79][1040/1879] lr: 2.0000e-03 eta: 4:08:31 time: 0.3638 data_time: 0.1156 memory: 6717 grad_norm: 3.3654 loss: 1.1580 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1580 2023/04/14 12:28:48 - mmengine - INFO - Epoch(train) [79][1060/1879] lr: 2.0000e-03 eta: 4:08:23 time: 0.3539 data_time: 0.0217 memory: 6717 grad_norm: 3.3614 loss: 1.0817 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0817 2023/04/14 12:28:56 - mmengine - INFO - Epoch(train) [79][1080/1879] lr: 2.0000e-03 eta: 4:08:16 time: 0.4100 data_time: 0.0706 memory: 6717 grad_norm: 3.4264 loss: 1.1923 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1923 2023/04/14 12:29:03 - mmengine - INFO - Epoch(train) [79][1100/1879] lr: 2.0000e-03 eta: 4:08:08 time: 0.3398 data_time: 0.0331 memory: 6717 grad_norm: 3.3802 loss: 1.0871 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0871 2023/04/14 12:29:11 - mmengine - INFO - Epoch(train) [79][1120/1879] lr: 2.0000e-03 eta: 4:08:01 time: 0.3902 data_time: 0.0533 memory: 6717 grad_norm: 3.3886 loss: 1.2893 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2893 2023/04/14 12:29:18 - mmengine - INFO - Epoch(train) [79][1140/1879] lr: 2.0000e-03 eta: 4:07:54 time: 0.3422 data_time: 0.0663 memory: 6717 grad_norm: 3.3253 loss: 1.0500 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0500 2023/04/14 12:29:25 - mmengine - INFO - Epoch(train) [79][1160/1879] lr: 2.0000e-03 eta: 4:07:46 time: 0.3734 data_time: 0.0777 memory: 6717 grad_norm: 3.3595 loss: 1.1027 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1027 2023/04/14 12:29:32 - mmengine - INFO - Epoch(train) [79][1180/1879] lr: 2.0000e-03 eta: 4:07:39 time: 0.3541 data_time: 0.0916 memory: 6717 grad_norm: 3.4779 loss: 1.1842 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1842 2023/04/14 12:29:40 - mmengine - INFO - Epoch(train) [79][1200/1879] lr: 2.0000e-03 eta: 4:07:31 time: 0.3780 data_time: 0.1637 memory: 6717 grad_norm: 3.3682 loss: 1.2108 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2108 2023/04/14 12:29:47 - mmengine - INFO - Epoch(train) [79][1220/1879] lr: 2.0000e-03 eta: 4:07:24 time: 0.3722 data_time: 0.0929 memory: 6717 grad_norm: 3.3604 loss: 1.1132 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1132 2023/04/14 12:29:54 - mmengine - INFO - Epoch(train) [79][1240/1879] lr: 2.0000e-03 eta: 4:07:17 time: 0.3542 data_time: 0.1097 memory: 6717 grad_norm: 3.4105 loss: 0.9658 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9658 2023/04/14 12:30:02 - mmengine - INFO - Epoch(train) [79][1260/1879] lr: 2.0000e-03 eta: 4:07:09 time: 0.3954 data_time: 0.1292 memory: 6717 grad_norm: 3.3739 loss: 1.1628 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1628 2023/04/14 12:30:09 - mmengine - INFO - Epoch(train) [79][1280/1879] lr: 2.0000e-03 eta: 4:07:02 time: 0.3438 data_time: 0.0903 memory: 6717 grad_norm: 3.3937 loss: 1.1173 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1173 2023/04/14 12:30:18 - mmengine - INFO - Epoch(train) [79][1300/1879] lr: 2.0000e-03 eta: 4:06:55 time: 0.4274 data_time: 0.0130 memory: 6717 grad_norm: 3.3423 loss: 1.1484 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1484 2023/04/14 12:30:24 - mmengine - INFO - Epoch(train) [79][1320/1879] lr: 2.0000e-03 eta: 4:06:47 time: 0.3226 data_time: 0.0160 memory: 6717 grad_norm: 3.2868 loss: 1.0564 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0564 2023/04/14 12:30:32 - mmengine - INFO - Epoch(train) [79][1340/1879] lr: 2.0000e-03 eta: 4:06:40 time: 0.4091 data_time: 0.0126 memory: 6717 grad_norm: 3.3486 loss: 1.2481 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2481 2023/04/14 12:30:39 - mmengine - INFO - Epoch(train) [79][1360/1879] lr: 2.0000e-03 eta: 4:06:32 time: 0.3305 data_time: 0.0157 memory: 6717 grad_norm: 3.4370 loss: 1.2530 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2530 2023/04/14 12:30:47 - mmengine - INFO - Epoch(train) [79][1380/1879] lr: 2.0000e-03 eta: 4:06:25 time: 0.3807 data_time: 0.0254 memory: 6717 grad_norm: 3.3193 loss: 1.1227 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1227 2023/04/14 12:30:53 - mmengine - INFO - Epoch(train) [79][1400/1879] lr: 2.0000e-03 eta: 4:06:17 time: 0.3254 data_time: 0.0519 memory: 6717 grad_norm: 3.4084 loss: 1.1595 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1595 2023/04/14 12:31:01 - mmengine - INFO - Epoch(train) [79][1420/1879] lr: 2.0000e-03 eta: 4:06:10 time: 0.4009 data_time: 0.1120 memory: 6717 grad_norm: 3.3564 loss: 1.0219 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0219 2023/04/14 12:31:07 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 12:31:08 - mmengine - INFO - Epoch(train) [79][1440/1879] lr: 2.0000e-03 eta: 4:06:02 time: 0.3468 data_time: 0.1532 memory: 6717 grad_norm: 3.3740 loss: 1.1533 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1533 2023/04/14 12:31:16 - mmengine - INFO - Epoch(train) [79][1460/1879] lr: 2.0000e-03 eta: 4:05:55 time: 0.3767 data_time: 0.1939 memory: 6717 grad_norm: 3.4701 loss: 1.1360 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1360 2023/04/14 12:31:23 - mmengine - INFO - Epoch(train) [79][1480/1879] lr: 2.0000e-03 eta: 4:05:48 time: 0.3624 data_time: 0.1023 memory: 6717 grad_norm: 3.3765 loss: 1.1507 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1507 2023/04/14 12:31:30 - mmengine - INFO - Epoch(train) [79][1500/1879] lr: 2.0000e-03 eta: 4:05:40 time: 0.3627 data_time: 0.1107 memory: 6717 grad_norm: 3.2705 loss: 1.1625 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1625 2023/04/14 12:31:38 - mmengine - INFO - Epoch(train) [79][1520/1879] lr: 2.0000e-03 eta: 4:05:33 time: 0.3922 data_time: 0.0601 memory: 6717 grad_norm: 3.3422 loss: 1.0893 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.0893 2023/04/14 12:31:45 - mmengine - INFO - Epoch(train) [79][1540/1879] lr: 2.0000e-03 eta: 4:05:26 time: 0.3770 data_time: 0.0334 memory: 6717 grad_norm: 3.4048 loss: 1.3298 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3298 2023/04/14 12:31:52 - mmengine - INFO - Epoch(train) [79][1560/1879] lr: 2.0000e-03 eta: 4:05:18 time: 0.3095 data_time: 0.0355 memory: 6717 grad_norm: 3.3536 loss: 1.1931 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1931 2023/04/14 12:32:00 - mmengine - INFO - Epoch(train) [79][1580/1879] lr: 2.0000e-03 eta: 4:05:11 time: 0.4314 data_time: 0.0967 memory: 6717 grad_norm: 3.4768 loss: 1.2200 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2200 2023/04/14 12:32:07 - mmengine - INFO - Epoch(train) [79][1600/1879] lr: 2.0000e-03 eta: 4:05:03 time: 0.3263 data_time: 0.0403 memory: 6717 grad_norm: 3.3398 loss: 0.9963 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9963 2023/04/14 12:32:14 - mmengine - INFO - Epoch(train) [79][1620/1879] lr: 2.0000e-03 eta: 4:04:56 time: 0.3686 data_time: 0.0525 memory: 6717 grad_norm: 3.5136 loss: 1.2566 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2566 2023/04/14 12:32:22 - mmengine - INFO - Epoch(train) [79][1640/1879] lr: 2.0000e-03 eta: 4:04:48 time: 0.3737 data_time: 0.0138 memory: 6717 grad_norm: 3.3817 loss: 1.0235 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0235 2023/04/14 12:32:29 - mmengine - INFO - Epoch(train) [79][1660/1879] lr: 2.0000e-03 eta: 4:04:41 time: 0.3551 data_time: 0.0140 memory: 6717 grad_norm: 3.3750 loss: 1.2259 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2259 2023/04/14 12:32:37 - mmengine - INFO - Epoch(train) [79][1680/1879] lr: 2.0000e-03 eta: 4:04:34 time: 0.4050 data_time: 0.0149 memory: 6717 grad_norm: 3.4073 loss: 1.0382 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0382 2023/04/14 12:32:43 - mmengine - INFO - Epoch(train) [79][1700/1879] lr: 2.0000e-03 eta: 4:04:26 time: 0.3278 data_time: 0.0141 memory: 6717 grad_norm: 3.4575 loss: 1.1860 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1860 2023/04/14 12:32:52 - mmengine - INFO - Epoch(train) [79][1720/1879] lr: 2.0000e-03 eta: 4:04:19 time: 0.4078 data_time: 0.0334 memory: 6717 grad_norm: 3.3398 loss: 1.1682 top1_acc: 0.3750 top5_acc: 0.8125 loss_cls: 1.1682 2023/04/14 12:32:58 - mmengine - INFO - Epoch(train) [79][1740/1879] lr: 2.0000e-03 eta: 4:04:11 time: 0.3085 data_time: 0.0686 memory: 6717 grad_norm: 3.4318 loss: 1.0829 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0829 2023/04/14 12:33:06 - mmengine - INFO - Epoch(train) [79][1760/1879] lr: 2.0000e-03 eta: 4:04:04 time: 0.4346 data_time: 0.0458 memory: 6717 grad_norm: 3.4031 loss: 1.2343 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 1.2343 2023/04/14 12:33:13 - mmengine - INFO - Epoch(train) [79][1780/1879] lr: 2.0000e-03 eta: 4:03:56 time: 0.3268 data_time: 0.0137 memory: 6717 grad_norm: 3.4449 loss: 1.0568 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0568 2023/04/14 12:33:22 - mmengine - INFO - Epoch(train) [79][1800/1879] lr: 2.0000e-03 eta: 4:03:49 time: 0.4349 data_time: 0.0137 memory: 6717 grad_norm: 3.3904 loss: 1.2661 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2661 2023/04/14 12:33:28 - mmengine - INFO - Epoch(train) [79][1820/1879] lr: 2.0000e-03 eta: 4:03:42 time: 0.3122 data_time: 0.0147 memory: 6717 grad_norm: 3.4338 loss: 1.2845 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2845 2023/04/14 12:33:36 - mmengine - INFO - Epoch(train) [79][1840/1879] lr: 2.0000e-03 eta: 4:03:35 time: 0.4209 data_time: 0.0126 memory: 6717 grad_norm: 3.4327 loss: 1.1236 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1236 2023/04/14 12:33:43 - mmengine - INFO - Epoch(train) [79][1860/1879] lr: 2.0000e-03 eta: 4:03:27 time: 0.3304 data_time: 0.0162 memory: 6717 grad_norm: 3.3455 loss: 1.0367 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0367 2023/04/14 12:33:49 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 12:33:49 - mmengine - INFO - Epoch(train) [79][1879/1879] lr: 2.0000e-03 eta: 4:03:20 time: 0.3045 data_time: 0.0117 memory: 6717 grad_norm: 3.5113 loss: 1.1843 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 1.1843 2023/04/14 12:33:58 - mmengine - INFO - Epoch(val) [79][ 20/155] eta: 0:01:03 time: 0.4682 data_time: 0.4351 memory: 1391 2023/04/14 12:34:04 - mmengine - INFO - Epoch(val) [79][ 40/155] eta: 0:00:44 time: 0.3051 data_time: 0.2722 memory: 1391 2023/04/14 12:34:13 - mmengine - INFO - Epoch(val) [79][ 60/155] eta: 0:00:38 time: 0.4318 data_time: 0.3982 memory: 1391 2023/04/14 12:34:19 - mmengine - INFO - Epoch(val) [79][ 80/155] eta: 0:00:28 time: 0.3208 data_time: 0.2878 memory: 1391 2023/04/14 12:34:28 - mmengine - INFO - Epoch(val) [79][100/155] eta: 0:00:21 time: 0.4175 data_time: 0.3847 memory: 1391 2023/04/14 12:34:35 - mmengine - INFO - Epoch(val) [79][120/155] eta: 0:00:13 time: 0.3370 data_time: 0.3042 memory: 1391 2023/04/14 12:34:44 - mmengine - INFO - Epoch(val) [79][140/155] eta: 0:00:05 time: 0.4846 data_time: 0.4515 memory: 1391 2023/04/14 12:34:52 - mmengine - INFO - Epoch(val) [79][155/155] acc/top1: 0.6638 acc/top5: 0.8724 acc/mean1: 0.6637 data_time: 0.4193 time: 0.4516 2023/04/14 12:35:02 - mmengine - INFO - Epoch(train) [80][ 20/1879] lr: 2.0000e-03 eta: 4:03:13 time: 0.5157 data_time: 0.2660 memory: 6717 grad_norm: 3.2663 loss: 1.1558 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1558 2023/04/14 12:35:08 - mmengine - INFO - Epoch(train) [80][ 40/1879] lr: 2.0000e-03 eta: 4:03:05 time: 0.3277 data_time: 0.0261 memory: 6717 grad_norm: 3.3369 loss: 1.2089 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2089 2023/04/14 12:35:17 - mmengine - INFO - Epoch(train) [80][ 60/1879] lr: 2.0000e-03 eta: 4:02:58 time: 0.4441 data_time: 0.0248 memory: 6717 grad_norm: 3.3977 loss: 1.1228 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1228 2023/04/14 12:35:23 - mmengine - INFO - Epoch(train) [80][ 80/1879] lr: 2.0000e-03 eta: 4:02:50 time: 0.2826 data_time: 0.0138 memory: 6717 grad_norm: 3.4122 loss: 1.2493 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2493 2023/04/14 12:35:31 - mmengine - INFO - Epoch(train) [80][ 100/1879] lr: 2.0000e-03 eta: 4:02:43 time: 0.4233 data_time: 0.0196 memory: 6717 grad_norm: 3.3555 loss: 1.0345 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0345 2023/04/14 12:35:38 - mmengine - INFO - Epoch(train) [80][ 120/1879] lr: 2.0000e-03 eta: 4:02:36 time: 0.3142 data_time: 0.0135 memory: 6717 grad_norm: 3.3904 loss: 1.1156 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1156 2023/04/14 12:35:47 - mmengine - INFO - Epoch(train) [80][ 140/1879] lr: 2.0000e-03 eta: 4:02:29 time: 0.4523 data_time: 0.0144 memory: 6717 grad_norm: 3.4331 loss: 1.1976 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1976 2023/04/14 12:35:53 - mmengine - INFO - Epoch(train) [80][ 160/1879] lr: 2.0000e-03 eta: 4:02:21 time: 0.3238 data_time: 0.0145 memory: 6717 grad_norm: 3.3211 loss: 1.0343 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0343 2023/04/14 12:36:01 - mmengine - INFO - Epoch(train) [80][ 180/1879] lr: 2.0000e-03 eta: 4:02:14 time: 0.4117 data_time: 0.0152 memory: 6717 grad_norm: 3.3971 loss: 1.1256 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1256 2023/04/14 12:36:08 - mmengine - INFO - Epoch(train) [80][ 200/1879] lr: 2.0000e-03 eta: 4:02:06 time: 0.3266 data_time: 0.0141 memory: 6717 grad_norm: 3.3846 loss: 1.2555 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2555 2023/04/14 12:36:16 - mmengine - INFO - Epoch(train) [80][ 220/1879] lr: 2.0000e-03 eta: 4:01:59 time: 0.3974 data_time: 0.0154 memory: 6717 grad_norm: 3.4582 loss: 1.2749 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2749 2023/04/14 12:36:22 - mmengine - INFO - Epoch(train) [80][ 240/1879] lr: 2.0000e-03 eta: 4:01:51 time: 0.3155 data_time: 0.0139 memory: 6717 grad_norm: 3.2781 loss: 1.2412 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2412 2023/04/14 12:36:30 - mmengine - INFO - Epoch(train) [80][ 260/1879] lr: 2.0000e-03 eta: 4:01:44 time: 0.4000 data_time: 0.0152 memory: 6717 grad_norm: 3.3635 loss: 1.1633 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1633 2023/04/14 12:36:37 - mmengine - INFO - Epoch(train) [80][ 280/1879] lr: 2.0000e-03 eta: 4:01:36 time: 0.3321 data_time: 0.0133 memory: 6717 grad_norm: 3.4309 loss: 1.0362 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0362 2023/04/14 12:36:45 - mmengine - INFO - Epoch(train) [80][ 300/1879] lr: 2.0000e-03 eta: 4:01:29 time: 0.4057 data_time: 0.0142 memory: 6717 grad_norm: 3.3899 loss: 1.0234 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0234 2023/04/14 12:36:51 - mmengine - INFO - Epoch(train) [80][ 320/1879] lr: 2.0000e-03 eta: 4:01:22 time: 0.3161 data_time: 0.0146 memory: 6717 grad_norm: 3.3959 loss: 1.3239 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3239 2023/04/14 12:37:00 - mmengine - INFO - Epoch(train) [80][ 340/1879] lr: 2.0000e-03 eta: 4:01:14 time: 0.4338 data_time: 0.0157 memory: 6717 grad_norm: 3.4189 loss: 1.0884 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0884 2023/04/14 12:37:07 - mmengine - INFO - Epoch(train) [80][ 360/1879] lr: 2.0000e-03 eta: 4:01:07 time: 0.3430 data_time: 0.0141 memory: 6717 grad_norm: 3.3461 loss: 1.1045 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1045 2023/04/14 12:37:15 - mmengine - INFO - Epoch(train) [80][ 380/1879] lr: 2.0000e-03 eta: 4:01:00 time: 0.4108 data_time: 0.0153 memory: 6717 grad_norm: 3.4402 loss: 1.2538 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2538 2023/04/14 12:37:21 - mmengine - INFO - Epoch(train) [80][ 400/1879] lr: 2.0000e-03 eta: 4:00:52 time: 0.2962 data_time: 0.0164 memory: 6717 grad_norm: 3.3356 loss: 1.0628 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0628 2023/04/14 12:37:30 - mmengine - INFO - Epoch(train) [80][ 420/1879] lr: 2.0000e-03 eta: 4:00:45 time: 0.4492 data_time: 0.0139 memory: 6717 grad_norm: 3.4061 loss: 1.1640 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1640 2023/04/14 12:37:36 - mmengine - INFO - Epoch(train) [80][ 440/1879] lr: 2.0000e-03 eta: 4:00:37 time: 0.3203 data_time: 0.0148 memory: 6717 grad_norm: 3.4542 loss: 1.2933 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2933 2023/04/14 12:37:45 - mmengine - INFO - Epoch(train) [80][ 460/1879] lr: 2.0000e-03 eta: 4:00:30 time: 0.4007 data_time: 0.0135 memory: 6717 grad_norm: 3.4502 loss: 1.0012 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0012 2023/04/14 12:37:51 - mmengine - INFO - Epoch(train) [80][ 480/1879] lr: 2.0000e-03 eta: 4:00:22 time: 0.3319 data_time: 0.0150 memory: 6717 grad_norm: 3.3993 loss: 0.9176 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9176 2023/04/14 12:38:00 - mmengine - INFO - Epoch(train) [80][ 500/1879] lr: 2.0000e-03 eta: 4:00:15 time: 0.4285 data_time: 0.0134 memory: 6717 grad_norm: 3.4566 loss: 1.2615 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2615 2023/04/14 12:38:06 - mmengine - INFO - Epoch(train) [80][ 520/1879] lr: 2.0000e-03 eta: 4:00:08 time: 0.3309 data_time: 0.0145 memory: 6717 grad_norm: 3.5255 loss: 1.1774 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1774 2023/04/14 12:38:15 - mmengine - INFO - Epoch(train) [80][ 540/1879] lr: 2.0000e-03 eta: 4:00:01 time: 0.4440 data_time: 0.0162 memory: 6717 grad_norm: 3.4707 loss: 1.1443 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1443 2023/04/14 12:38:21 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 12:38:21 - mmengine - INFO - Epoch(train) [80][ 560/1879] lr: 2.0000e-03 eta: 3:59:53 time: 0.3056 data_time: 0.0135 memory: 6717 grad_norm: 3.4067 loss: 1.2625 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2625 2023/04/14 12:38:29 - mmengine - INFO - Epoch(train) [80][ 580/1879] lr: 2.0000e-03 eta: 3:59:46 time: 0.4023 data_time: 0.0145 memory: 6717 grad_norm: 3.3802 loss: 1.2693 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2693 2023/04/14 12:38:35 - mmengine - INFO - Epoch(train) [80][ 600/1879] lr: 2.0000e-03 eta: 3:59:38 time: 0.2830 data_time: 0.0150 memory: 6717 grad_norm: 3.3419 loss: 1.1756 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1756 2023/04/14 12:38:43 - mmengine - INFO - Epoch(train) [80][ 620/1879] lr: 2.0000e-03 eta: 3:59:31 time: 0.4156 data_time: 0.0152 memory: 6717 grad_norm: 3.4338 loss: 1.2257 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2257 2023/04/14 12:38:50 - mmengine - INFO - Epoch(train) [80][ 640/1879] lr: 2.0000e-03 eta: 3:59:23 time: 0.3503 data_time: 0.0136 memory: 6717 grad_norm: 3.4261 loss: 1.4254 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.4254 2023/04/14 12:38:59 - mmengine - INFO - Epoch(train) [80][ 660/1879] lr: 2.0000e-03 eta: 3:59:16 time: 0.4122 data_time: 0.0168 memory: 6717 grad_norm: 3.4257 loss: 1.1089 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1089 2023/04/14 12:39:06 - mmengine - INFO - Epoch(train) [80][ 680/1879] lr: 2.0000e-03 eta: 3:59:09 time: 0.3429 data_time: 0.0132 memory: 6717 grad_norm: 3.4362 loss: 1.2089 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2089 2023/04/14 12:39:14 - mmengine - INFO - Epoch(train) [80][ 700/1879] lr: 2.0000e-03 eta: 3:59:02 time: 0.4410 data_time: 0.0159 memory: 6717 grad_norm: 3.3759 loss: 1.1620 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1620 2023/04/14 12:39:20 - mmengine - INFO - Epoch(train) [80][ 720/1879] lr: 2.0000e-03 eta: 3:58:54 time: 0.3005 data_time: 0.0136 memory: 6717 grad_norm: 3.4076 loss: 1.1032 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1032 2023/04/14 12:39:29 - mmengine - INFO - Epoch(train) [80][ 740/1879] lr: 2.0000e-03 eta: 3:58:47 time: 0.4135 data_time: 0.0134 memory: 6717 grad_norm: 3.3505 loss: 1.1659 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1659 2023/04/14 12:39:35 - mmengine - INFO - Epoch(train) [80][ 760/1879] lr: 2.0000e-03 eta: 3:58:39 time: 0.3169 data_time: 0.0157 memory: 6717 grad_norm: 3.4074 loss: 1.1184 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1184 2023/04/14 12:39:43 - mmengine - INFO - Epoch(train) [80][ 780/1879] lr: 2.0000e-03 eta: 3:58:32 time: 0.4131 data_time: 0.0137 memory: 6717 grad_norm: 3.4167 loss: 1.3195 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3195 2023/04/14 12:39:50 - mmengine - INFO - Epoch(train) [80][ 800/1879] lr: 2.0000e-03 eta: 3:58:24 time: 0.3470 data_time: 0.0141 memory: 6717 grad_norm: 3.3348 loss: 1.1652 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1652 2023/04/14 12:39:57 - mmengine - INFO - Epoch(train) [80][ 820/1879] lr: 2.0000e-03 eta: 3:58:17 time: 0.3647 data_time: 0.0146 memory: 6717 grad_norm: 3.3579 loss: 0.9314 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9314 2023/04/14 12:40:05 - mmengine - INFO - Epoch(train) [80][ 840/1879] lr: 2.0000e-03 eta: 3:58:09 time: 0.3728 data_time: 0.0151 memory: 6717 grad_norm: 3.3021 loss: 1.2174 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2174 2023/04/14 12:40:12 - mmengine - INFO - Epoch(train) [80][ 860/1879] lr: 2.0000e-03 eta: 3:58:02 time: 0.3556 data_time: 0.0144 memory: 6717 grad_norm: 3.3479 loss: 1.0897 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0897 2023/04/14 12:40:20 - mmengine - INFO - Epoch(train) [80][ 880/1879] lr: 2.0000e-03 eta: 3:57:55 time: 0.3841 data_time: 0.0152 memory: 6717 grad_norm: 3.4561 loss: 1.1399 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1399 2023/04/14 12:40:27 - mmengine - INFO - Epoch(train) [80][ 900/1879] lr: 2.0000e-03 eta: 3:57:47 time: 0.3796 data_time: 0.0131 memory: 6717 grad_norm: 3.4994 loss: 1.0189 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0189 2023/04/14 12:40:34 - mmengine - INFO - Epoch(train) [80][ 920/1879] lr: 2.0000e-03 eta: 3:57:40 time: 0.3327 data_time: 0.0155 memory: 6717 grad_norm: 3.4129 loss: 1.1258 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1258 2023/04/14 12:40:42 - mmengine - INFO - Epoch(train) [80][ 940/1879] lr: 2.0000e-03 eta: 3:57:32 time: 0.4145 data_time: 0.0135 memory: 6717 grad_norm: 3.3609 loss: 1.3500 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3500 2023/04/14 12:40:49 - mmengine - INFO - Epoch(train) [80][ 960/1879] lr: 2.0000e-03 eta: 3:57:25 time: 0.3174 data_time: 0.0191 memory: 6717 grad_norm: 3.4120 loss: 1.2913 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2913 2023/04/14 12:40:57 - mmengine - INFO - Epoch(train) [80][ 980/1879] lr: 2.0000e-03 eta: 3:57:18 time: 0.3988 data_time: 0.0216 memory: 6717 grad_norm: 3.4461 loss: 1.1796 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1796 2023/04/14 12:41:04 - mmengine - INFO - Epoch(train) [80][1000/1879] lr: 2.0000e-03 eta: 3:57:10 time: 0.3705 data_time: 0.0796 memory: 6717 grad_norm: 3.3926 loss: 1.1156 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1156 2023/04/14 12:41:12 - mmengine - INFO - Epoch(train) [80][1020/1879] lr: 2.0000e-03 eta: 3:57:03 time: 0.4084 data_time: 0.0528 memory: 6717 grad_norm: 3.4313 loss: 1.3922 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.3922 2023/04/14 12:41:19 - mmengine - INFO - Epoch(train) [80][1040/1879] lr: 2.0000e-03 eta: 3:56:55 time: 0.3565 data_time: 0.0496 memory: 6717 grad_norm: 3.3415 loss: 1.1554 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1554 2023/04/14 12:41:27 - mmengine - INFO - Epoch(train) [80][1060/1879] lr: 2.0000e-03 eta: 3:56:48 time: 0.3896 data_time: 0.0326 memory: 6717 grad_norm: 3.4510 loss: 1.0779 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0779 2023/04/14 12:41:34 - mmengine - INFO - Epoch(train) [80][1080/1879] lr: 2.0000e-03 eta: 3:56:41 time: 0.3410 data_time: 0.0152 memory: 6717 grad_norm: 3.3989 loss: 1.2233 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2233 2023/04/14 12:41:41 - mmengine - INFO - Epoch(train) [80][1100/1879] lr: 2.0000e-03 eta: 3:56:33 time: 0.3632 data_time: 0.0130 memory: 6717 grad_norm: 3.3479 loss: 1.1475 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1475 2023/04/14 12:41:49 - mmengine - INFO - Epoch(train) [80][1120/1879] lr: 2.0000e-03 eta: 3:56:26 time: 0.3809 data_time: 0.0163 memory: 6717 grad_norm: 3.5038 loss: 1.2473 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2473 2023/04/14 12:41:56 - mmengine - INFO - Epoch(train) [80][1140/1879] lr: 2.0000e-03 eta: 3:56:18 time: 0.3462 data_time: 0.0131 memory: 6717 grad_norm: 3.4241 loss: 1.1866 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1866 2023/04/14 12:42:04 - mmengine - INFO - Epoch(train) [80][1160/1879] lr: 2.0000e-03 eta: 3:56:11 time: 0.4003 data_time: 0.0155 memory: 6717 grad_norm: 3.3970 loss: 1.0726 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0726 2023/04/14 12:42:11 - mmengine - INFO - Epoch(train) [80][1180/1879] lr: 2.0000e-03 eta: 3:56:04 time: 0.3683 data_time: 0.0126 memory: 6717 grad_norm: 3.5199 loss: 1.0293 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0293 2023/04/14 12:42:20 - mmengine - INFO - Epoch(train) [80][1200/1879] lr: 2.0000e-03 eta: 3:55:56 time: 0.4160 data_time: 0.0136 memory: 6717 grad_norm: 3.3693 loss: 1.1210 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1210 2023/04/14 12:42:25 - mmengine - INFO - Epoch(train) [80][1220/1879] lr: 2.0000e-03 eta: 3:55:49 time: 0.2927 data_time: 0.0154 memory: 6717 grad_norm: 3.4113 loss: 1.3903 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3903 2023/04/14 12:42:33 - mmengine - INFO - Epoch(train) [80][1240/1879] lr: 2.0000e-03 eta: 3:55:41 time: 0.4024 data_time: 0.0153 memory: 6717 grad_norm: 3.3769 loss: 1.1177 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1177 2023/04/14 12:42:40 - mmengine - INFO - Epoch(train) [80][1260/1879] lr: 2.0000e-03 eta: 3:55:34 time: 0.3241 data_time: 0.0140 memory: 6717 grad_norm: 3.3736 loss: 1.0603 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0603 2023/04/14 12:42:49 - mmengine - INFO - Epoch(train) [80][1280/1879] lr: 2.0000e-03 eta: 3:55:27 time: 0.4344 data_time: 0.0152 memory: 6717 grad_norm: 3.4101 loss: 1.1409 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1409 2023/04/14 12:42:55 - mmengine - INFO - Epoch(train) [80][1300/1879] lr: 2.0000e-03 eta: 3:55:19 time: 0.3088 data_time: 0.0148 memory: 6717 grad_norm: 3.4228 loss: 1.1980 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1980 2023/04/14 12:43:03 - mmengine - INFO - Epoch(train) [80][1320/1879] lr: 2.0000e-03 eta: 3:55:12 time: 0.4040 data_time: 0.0151 memory: 6717 grad_norm: 3.3773 loss: 1.0168 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0168 2023/04/14 12:43:09 - mmengine - INFO - Epoch(train) [80][1340/1879] lr: 2.0000e-03 eta: 3:55:04 time: 0.3260 data_time: 0.0139 memory: 6717 grad_norm: 3.4600 loss: 1.0843 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0843 2023/04/14 12:43:17 - mmengine - INFO - Epoch(train) [80][1360/1879] lr: 2.0000e-03 eta: 3:54:57 time: 0.3991 data_time: 0.0152 memory: 6717 grad_norm: 3.4295 loss: 1.2227 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2227 2023/04/14 12:43:24 - mmengine - INFO - Epoch(train) [80][1380/1879] lr: 2.0000e-03 eta: 3:54:49 time: 0.3391 data_time: 0.0140 memory: 6717 grad_norm: 3.4292 loss: 1.2538 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2538 2023/04/14 12:43:34 - mmengine - INFO - Epoch(train) [80][1400/1879] lr: 2.0000e-03 eta: 3:54:42 time: 0.4675 data_time: 0.0144 memory: 6717 grad_norm: 3.4488 loss: 1.1741 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1741 2023/04/14 12:43:40 - mmengine - INFO - Epoch(train) [80][1420/1879] lr: 2.0000e-03 eta: 3:54:35 time: 0.3154 data_time: 0.0157 memory: 6717 grad_norm: 3.3901 loss: 1.0380 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0380 2023/04/14 12:43:48 - mmengine - INFO - Epoch(train) [80][1440/1879] lr: 2.0000e-03 eta: 3:54:28 time: 0.3970 data_time: 0.0169 memory: 6717 grad_norm: 3.3707 loss: 1.2240 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2240 2023/04/14 12:43:55 - mmengine - INFO - Epoch(train) [80][1460/1879] lr: 2.0000e-03 eta: 3:54:20 time: 0.3477 data_time: 0.0136 memory: 6717 grad_norm: 3.3471 loss: 1.2048 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2048 2023/04/14 12:44:03 - mmengine - INFO - Epoch(train) [80][1480/1879] lr: 2.0000e-03 eta: 3:54:13 time: 0.3966 data_time: 0.0135 memory: 6717 grad_norm: 3.3751 loss: 1.2192 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2192 2023/04/14 12:44:09 - mmengine - INFO - Epoch(train) [80][1500/1879] lr: 2.0000e-03 eta: 3:54:05 time: 0.3185 data_time: 0.0142 memory: 6717 grad_norm: 3.4012 loss: 1.0113 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0113 2023/04/14 12:44:17 - mmengine - INFO - Epoch(train) [80][1520/1879] lr: 2.0000e-03 eta: 3:53:58 time: 0.3900 data_time: 0.0151 memory: 6717 grad_norm: 3.4207 loss: 1.2527 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2527 2023/04/14 12:44:23 - mmengine - INFO - Epoch(train) [80][1540/1879] lr: 2.0000e-03 eta: 3:53:50 time: 0.3201 data_time: 0.0138 memory: 6717 grad_norm: 3.4237 loss: 1.0818 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0818 2023/04/14 12:44:31 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 12:44:31 - mmengine - INFO - Epoch(train) [80][1560/1879] lr: 2.0000e-03 eta: 3:53:43 time: 0.3781 data_time: 0.0145 memory: 6717 grad_norm: 3.3557 loss: 1.1485 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1485 2023/04/14 12:44:37 - mmengine - INFO - Epoch(train) [80][1580/1879] lr: 2.0000e-03 eta: 3:53:35 time: 0.3193 data_time: 0.0148 memory: 6717 grad_norm: 3.4304 loss: 1.1477 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1477 2023/04/14 12:44:45 - mmengine - INFO - Epoch(train) [80][1600/1879] lr: 2.0000e-03 eta: 3:53:28 time: 0.4112 data_time: 0.0156 memory: 6717 grad_norm: 3.3473 loss: 1.1418 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1418 2023/04/14 12:44:52 - mmengine - INFO - Epoch(train) [80][1620/1879] lr: 2.0000e-03 eta: 3:53:20 time: 0.3468 data_time: 0.0142 memory: 6717 grad_norm: 3.4351 loss: 1.1384 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1384 2023/04/14 12:45:00 - mmengine - INFO - Epoch(train) [80][1640/1879] lr: 2.0000e-03 eta: 3:53:13 time: 0.3938 data_time: 0.1112 memory: 6717 grad_norm: 3.3884 loss: 1.2611 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2611 2023/04/14 12:45:07 - mmengine - INFO - Epoch(train) [80][1660/1879] lr: 2.0000e-03 eta: 3:53:06 time: 0.3439 data_time: 0.1629 memory: 6717 grad_norm: 3.4240 loss: 1.2160 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2160 2023/04/14 12:45:15 - mmengine - INFO - Epoch(train) [80][1680/1879] lr: 2.0000e-03 eta: 3:52:58 time: 0.3996 data_time: 0.1647 memory: 6717 grad_norm: 3.4067 loss: 1.2935 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2935 2023/04/14 12:45:22 - mmengine - INFO - Epoch(train) [80][1700/1879] lr: 2.0000e-03 eta: 3:52:51 time: 0.3593 data_time: 0.0689 memory: 6717 grad_norm: 3.4396 loss: 1.1234 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1234 2023/04/14 12:45:30 - mmengine - INFO - Epoch(train) [80][1720/1879] lr: 2.0000e-03 eta: 3:52:44 time: 0.3882 data_time: 0.1326 memory: 6717 grad_norm: 3.3818 loss: 1.2543 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2543 2023/04/14 12:45:37 - mmengine - INFO - Epoch(train) [80][1740/1879] lr: 2.0000e-03 eta: 3:52:36 time: 0.3440 data_time: 0.0806 memory: 6717 grad_norm: 3.4370 loss: 1.1130 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1130 2023/04/14 12:45:45 - mmengine - INFO - Epoch(train) [80][1760/1879] lr: 2.0000e-03 eta: 3:52:29 time: 0.4023 data_time: 0.0898 memory: 6717 grad_norm: 3.3385 loss: 1.0357 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0357 2023/04/14 12:45:52 - mmengine - INFO - Epoch(train) [80][1780/1879] lr: 2.0000e-03 eta: 3:52:21 time: 0.3479 data_time: 0.0200 memory: 6717 grad_norm: 3.3201 loss: 1.0765 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0765 2023/04/14 12:46:00 - mmengine - INFO - Epoch(train) [80][1800/1879] lr: 2.0000e-03 eta: 3:52:14 time: 0.3912 data_time: 0.0152 memory: 6717 grad_norm: 3.3874 loss: 1.1929 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1929 2023/04/14 12:46:06 - mmengine - INFO - Epoch(train) [80][1820/1879] lr: 2.0000e-03 eta: 3:52:06 time: 0.3273 data_time: 0.0137 memory: 6717 grad_norm: 3.4595 loss: 1.1780 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1780 2023/04/14 12:46:15 - mmengine - INFO - Epoch(train) [80][1840/1879] lr: 2.0000e-03 eta: 3:51:59 time: 0.4307 data_time: 0.0143 memory: 6717 grad_norm: 3.3678 loss: 1.1455 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1455 2023/04/14 12:46:22 - mmengine - INFO - Epoch(train) [80][1860/1879] lr: 2.0000e-03 eta: 3:51:52 time: 0.3505 data_time: 0.0149 memory: 6717 grad_norm: 3.4566 loss: 1.1431 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1431 2023/04/14 12:46:29 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 12:46:29 - mmengine - INFO - Epoch(train) [80][1879/1879] lr: 2.0000e-03 eta: 3:51:45 time: 0.3333 data_time: 0.0119 memory: 6717 grad_norm: 3.4308 loss: 1.1825 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 1.1825 2023/04/14 12:46:38 - mmengine - INFO - Epoch(val) [80][ 20/155] eta: 0:01:00 time: 0.4493 data_time: 0.4158 memory: 1391 2023/04/14 12:46:44 - mmengine - INFO - Epoch(val) [80][ 40/155] eta: 0:00:44 time: 0.3266 data_time: 0.2933 memory: 1391 2023/04/14 12:46:53 - mmengine - INFO - Epoch(val) [80][ 60/155] eta: 0:00:38 time: 0.4336 data_time: 0.3970 memory: 1391 2023/04/14 12:46:59 - mmengine - INFO - Epoch(val) [80][ 80/155] eta: 0:00:28 time: 0.3122 data_time: 0.2789 memory: 1391 2023/04/14 12:47:08 - mmengine - INFO - Epoch(val) [80][100/155] eta: 0:00:21 time: 0.4567 data_time: 0.4232 memory: 1391 2023/04/14 12:47:14 - mmengine - INFO - Epoch(val) [80][120/155] eta: 0:00:13 time: 0.3031 data_time: 0.2696 memory: 1391 2023/04/14 12:47:24 - mmengine - INFO - Epoch(val) [80][140/155] eta: 0:00:05 time: 0.4818 data_time: 0.4488 memory: 1391 2023/04/14 12:47:31 - mmengine - INFO - Epoch(val) [80][155/155] acc/top1: 0.6653 acc/top5: 0.8725 acc/mean1: 0.6652 data_time: 0.4179 time: 0.4504 2023/04/14 12:47:31 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_69.pth is removed 2023/04/14 12:47:32 - mmengine - INFO - The best checkpoint with 0.6653 acc/top1 at 80 epoch is saved to best_acc_top1_epoch_80.pth. 2023/04/14 12:47:41 - mmengine - INFO - Epoch(train) [81][ 20/1879] lr: 2.0000e-04 eta: 3:51:38 time: 0.4772 data_time: 0.3434 memory: 6717 grad_norm: 3.2870 loss: 1.0047 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0047 2023/04/14 12:47:47 - mmengine - INFO - Epoch(train) [81][ 40/1879] lr: 2.0000e-04 eta: 3:51:30 time: 0.3153 data_time: 0.1694 memory: 6717 grad_norm: 3.3794 loss: 1.2416 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2416 2023/04/14 12:47:56 - mmengine - INFO - Epoch(train) [81][ 60/1879] lr: 2.0000e-04 eta: 3:51:23 time: 0.4136 data_time: 0.1995 memory: 6717 grad_norm: 3.3111 loss: 1.0783 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0783 2023/04/14 12:48:02 - mmengine - INFO - Epoch(train) [81][ 80/1879] lr: 2.0000e-04 eta: 3:51:15 time: 0.3333 data_time: 0.0822 memory: 6717 grad_norm: 3.3329 loss: 1.1456 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1456 2023/04/14 12:48:11 - mmengine - INFO - Epoch(train) [81][ 100/1879] lr: 2.0000e-04 eta: 3:51:08 time: 0.4192 data_time: 0.1920 memory: 6717 grad_norm: 3.4206 loss: 1.1868 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1868 2023/04/14 12:48:17 - mmengine - INFO - Epoch(train) [81][ 120/1879] lr: 2.0000e-04 eta: 3:51:01 time: 0.3274 data_time: 0.1846 memory: 6717 grad_norm: 3.3804 loss: 1.0750 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0750 2023/04/14 12:48:26 - mmengine - INFO - Epoch(train) [81][ 140/1879] lr: 2.0000e-04 eta: 3:50:54 time: 0.4462 data_time: 0.2428 memory: 6717 grad_norm: 3.3536 loss: 1.2462 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2462 2023/04/14 12:48:32 - mmengine - INFO - Epoch(train) [81][ 160/1879] lr: 2.0000e-04 eta: 3:50:46 time: 0.2998 data_time: 0.1112 memory: 6717 grad_norm: 3.4773 loss: 1.3173 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3173 2023/04/14 12:48:40 - mmengine - INFO - Epoch(train) [81][ 180/1879] lr: 2.0000e-04 eta: 3:50:39 time: 0.4014 data_time: 0.1734 memory: 6717 grad_norm: 3.4341 loss: 1.2457 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2457 2023/04/14 12:48:47 - mmengine - INFO - Epoch(train) [81][ 200/1879] lr: 2.0000e-04 eta: 3:50:31 time: 0.3350 data_time: 0.0990 memory: 6717 grad_norm: 3.4572 loss: 1.2077 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2077 2023/04/14 12:48:55 - mmengine - INFO - Epoch(train) [81][ 220/1879] lr: 2.0000e-04 eta: 3:50:24 time: 0.3921 data_time: 0.0274 memory: 6717 grad_norm: 3.3229 loss: 1.0118 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0118 2023/04/14 12:49:01 - mmengine - INFO - Epoch(train) [81][ 240/1879] lr: 2.0000e-04 eta: 3:50:16 time: 0.3273 data_time: 0.0133 memory: 6717 grad_norm: 3.3425 loss: 1.1654 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1654 2023/04/14 12:49:09 - mmengine - INFO - Epoch(train) [81][ 260/1879] lr: 2.0000e-04 eta: 3:50:09 time: 0.3804 data_time: 0.0155 memory: 6717 grad_norm: 3.3295 loss: 1.0742 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0742 2023/04/14 12:49:17 - mmengine - INFO - Epoch(train) [81][ 280/1879] lr: 2.0000e-04 eta: 3:50:01 time: 0.3927 data_time: 0.0135 memory: 6717 grad_norm: 3.4276 loss: 1.1361 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1361 2023/04/14 12:49:24 - mmengine - INFO - Epoch(train) [81][ 300/1879] lr: 2.0000e-04 eta: 3:49:54 time: 0.3498 data_time: 0.0174 memory: 6717 grad_norm: 3.3772 loss: 1.4214 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.4214 2023/04/14 12:49:32 - mmengine - INFO - Epoch(train) [81][ 320/1879] lr: 2.0000e-04 eta: 3:49:47 time: 0.3872 data_time: 0.0137 memory: 6717 grad_norm: 3.4350 loss: 1.3179 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3179 2023/04/14 12:49:39 - mmengine - INFO - Epoch(train) [81][ 340/1879] lr: 2.0000e-04 eta: 3:49:39 time: 0.3631 data_time: 0.0154 memory: 6717 grad_norm: 3.2664 loss: 1.1265 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1265 2023/04/14 12:49:46 - mmengine - INFO - Epoch(train) [81][ 360/1879] lr: 2.0000e-04 eta: 3:49:32 time: 0.3652 data_time: 0.0143 memory: 6717 grad_norm: 3.3798 loss: 1.0343 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0343 2023/04/14 12:49:54 - mmengine - INFO - Epoch(train) [81][ 380/1879] lr: 2.0000e-04 eta: 3:49:24 time: 0.3957 data_time: 0.0143 memory: 6717 grad_norm: 3.3883 loss: 1.2688 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2688 2023/04/14 12:50:01 - mmengine - INFO - Epoch(train) [81][ 400/1879] lr: 2.0000e-04 eta: 3:49:17 time: 0.3283 data_time: 0.0139 memory: 6717 grad_norm: 3.3249 loss: 0.9479 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9479 2023/04/14 12:50:09 - mmengine - INFO - Epoch(train) [81][ 420/1879] lr: 2.0000e-04 eta: 3:49:10 time: 0.3978 data_time: 0.0157 memory: 6717 grad_norm: 3.3038 loss: 1.1747 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1747 2023/04/14 12:50:15 - mmengine - INFO - Epoch(train) [81][ 440/1879] lr: 2.0000e-04 eta: 3:49:02 time: 0.3273 data_time: 0.0181 memory: 6717 grad_norm: 3.3267 loss: 1.2136 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2136 2023/04/14 12:50:23 - mmengine - INFO - Epoch(train) [81][ 460/1879] lr: 2.0000e-04 eta: 3:48:55 time: 0.4082 data_time: 0.0389 memory: 6717 grad_norm: 3.3832 loss: 1.1312 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1312 2023/04/14 12:50:30 - mmengine - INFO - Epoch(train) [81][ 480/1879] lr: 2.0000e-04 eta: 3:48:47 time: 0.3308 data_time: 0.0127 memory: 6717 grad_norm: 3.3681 loss: 1.0977 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.0977 2023/04/14 12:50:38 - mmengine - INFO - Epoch(train) [81][ 500/1879] lr: 2.0000e-04 eta: 3:48:40 time: 0.3923 data_time: 0.0314 memory: 6717 grad_norm: 3.5035 loss: 1.3617 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3617 2023/04/14 12:50:45 - mmengine - INFO - Epoch(train) [81][ 520/1879] lr: 2.0000e-04 eta: 3:48:32 time: 0.3500 data_time: 0.0399 memory: 6717 grad_norm: 3.3623 loss: 0.9795 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9795 2023/04/14 12:50:53 - mmengine - INFO - Epoch(train) [81][ 540/1879] lr: 2.0000e-04 eta: 3:48:25 time: 0.3877 data_time: 0.0352 memory: 6717 grad_norm: 3.3106 loss: 1.1124 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1124 2023/04/14 12:51:00 - mmengine - INFO - Epoch(train) [81][ 560/1879] lr: 2.0000e-04 eta: 3:48:18 time: 0.3618 data_time: 0.0724 memory: 6717 grad_norm: 3.3778 loss: 1.0667 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0667 2023/04/14 12:51:08 - mmengine - INFO - Epoch(train) [81][ 580/1879] lr: 2.0000e-04 eta: 3:48:10 time: 0.3995 data_time: 0.0319 memory: 6717 grad_norm: 3.2922 loss: 1.2309 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2309 2023/04/14 12:51:14 - mmengine - INFO - Epoch(train) [81][ 600/1879] lr: 2.0000e-04 eta: 3:48:03 time: 0.3304 data_time: 0.0129 memory: 6717 grad_norm: 3.5096 loss: 1.3366 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3366 2023/04/14 12:51:22 - mmengine - INFO - Epoch(train) [81][ 620/1879] lr: 2.0000e-04 eta: 3:47:56 time: 0.3883 data_time: 0.0194 memory: 6717 grad_norm: 3.4453 loss: 1.1071 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1071 2023/04/14 12:51:29 - mmengine - INFO - Epoch(train) [81][ 640/1879] lr: 2.0000e-04 eta: 3:47:48 time: 0.3429 data_time: 0.0291 memory: 6717 grad_norm: 3.2985 loss: 1.0937 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0937 2023/04/14 12:51:37 - mmengine - INFO - Epoch(train) [81][ 660/1879] lr: 2.0000e-04 eta: 3:47:41 time: 0.4181 data_time: 0.0459 memory: 6717 grad_norm: 3.3388 loss: 1.0665 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0665 2023/04/14 12:51:44 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 12:51:44 - mmengine - INFO - Epoch(train) [81][ 680/1879] lr: 2.0000e-04 eta: 3:47:33 time: 0.3296 data_time: 0.1288 memory: 6717 grad_norm: 3.3170 loss: 0.9743 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9743 2023/04/14 12:51:52 - mmengine - INFO - Epoch(train) [81][ 700/1879] lr: 2.0000e-04 eta: 3:47:26 time: 0.4149 data_time: 0.1297 memory: 6717 grad_norm: 3.3226 loss: 1.0932 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0932 2023/04/14 12:51:59 - mmengine - INFO - Epoch(train) [81][ 720/1879] lr: 2.0000e-04 eta: 3:47:18 time: 0.3227 data_time: 0.0858 memory: 6717 grad_norm: 3.3453 loss: 0.9633 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9633 2023/04/14 12:52:07 - mmengine - INFO - Epoch(train) [81][ 740/1879] lr: 2.0000e-04 eta: 3:47:11 time: 0.4086 data_time: 0.0309 memory: 6717 grad_norm: 3.4246 loss: 1.0451 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0451 2023/04/14 12:52:13 - mmengine - INFO - Epoch(train) [81][ 760/1879] lr: 2.0000e-04 eta: 3:47:03 time: 0.2994 data_time: 0.0215 memory: 6717 grad_norm: 3.4626 loss: 1.3179 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.3179 2023/04/14 12:52:21 - mmengine - INFO - Epoch(train) [81][ 780/1879] lr: 2.0000e-04 eta: 3:46:56 time: 0.4019 data_time: 0.0249 memory: 6717 grad_norm: 3.4074 loss: 1.0950 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0950 2023/04/14 12:52:28 - mmengine - INFO - Epoch(train) [81][ 800/1879] lr: 2.0000e-04 eta: 3:46:49 time: 0.3345 data_time: 0.0375 memory: 6717 grad_norm: 3.3607 loss: 1.1737 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1737 2023/04/14 12:52:36 - mmengine - INFO - Epoch(train) [81][ 820/1879] lr: 2.0000e-04 eta: 3:46:42 time: 0.4330 data_time: 0.1047 memory: 6717 grad_norm: 3.3076 loss: 1.1764 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1764 2023/04/14 12:52:43 - mmengine - INFO - Epoch(train) [81][ 840/1879] lr: 2.0000e-04 eta: 3:46:34 time: 0.3221 data_time: 0.0541 memory: 6717 grad_norm: 3.4291 loss: 1.1487 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1487 2023/04/14 12:52:51 - mmengine - INFO - Epoch(train) [81][ 860/1879] lr: 2.0000e-04 eta: 3:46:27 time: 0.4292 data_time: 0.1125 memory: 6717 grad_norm: 3.3814 loss: 1.0419 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.0419 2023/04/14 12:52:59 - mmengine - INFO - Epoch(train) [81][ 880/1879] lr: 2.0000e-04 eta: 3:46:19 time: 0.3658 data_time: 0.1790 memory: 6717 grad_norm: 3.3725 loss: 1.1220 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1220 2023/04/14 12:53:06 - mmengine - INFO - Epoch(train) [81][ 900/1879] lr: 2.0000e-04 eta: 3:46:12 time: 0.3809 data_time: 0.2187 memory: 6717 grad_norm: 3.3563 loss: 1.0519 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0519 2023/04/14 12:53:13 - mmengine - INFO - Epoch(train) [81][ 920/1879] lr: 2.0000e-04 eta: 3:46:04 time: 0.3406 data_time: 0.1750 memory: 6717 grad_norm: 3.3264 loss: 0.9821 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9821 2023/04/14 12:53:22 - mmengine - INFO - Epoch(train) [81][ 940/1879] lr: 2.0000e-04 eta: 3:45:57 time: 0.4277 data_time: 0.2824 memory: 6717 grad_norm: 3.4227 loss: 1.2196 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2196 2023/04/14 12:53:29 - mmengine - INFO - Epoch(train) [81][ 960/1879] lr: 2.0000e-04 eta: 3:45:50 time: 0.3700 data_time: 0.2287 memory: 6717 grad_norm: 3.3173 loss: 1.1328 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1328 2023/04/14 12:53:38 - mmengine - INFO - Epoch(train) [81][ 980/1879] lr: 2.0000e-04 eta: 3:45:43 time: 0.4318 data_time: 0.2915 memory: 6717 grad_norm: 3.3540 loss: 1.0192 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0192 2023/04/14 12:53:44 - mmengine - INFO - Epoch(train) [81][1000/1879] lr: 2.0000e-04 eta: 3:45:35 time: 0.3234 data_time: 0.1809 memory: 6717 grad_norm: 3.5050 loss: 1.2148 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2148 2023/04/14 12:53:52 - mmengine - INFO - Epoch(train) [81][1020/1879] lr: 2.0000e-04 eta: 3:45:28 time: 0.3891 data_time: 0.2476 memory: 6717 grad_norm: 3.4199 loss: 1.1176 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.1176 2023/04/14 12:54:00 - mmengine - INFO - Epoch(train) [81][1040/1879] lr: 2.0000e-04 eta: 3:45:21 time: 0.3750 data_time: 0.2311 memory: 6717 grad_norm: 3.3054 loss: 0.9961 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9961 2023/04/14 12:54:08 - mmengine - INFO - Epoch(train) [81][1060/1879] lr: 2.0000e-04 eta: 3:45:13 time: 0.4285 data_time: 0.2867 memory: 6717 grad_norm: 3.3546 loss: 1.1463 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1463 2023/04/14 12:54:14 - mmengine - INFO - Epoch(train) [81][1080/1879] lr: 2.0000e-04 eta: 3:45:06 time: 0.3201 data_time: 0.1782 memory: 6717 grad_norm: 3.3829 loss: 1.2087 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2087 2023/04/14 12:54:22 - mmengine - INFO - Epoch(train) [81][1100/1879] lr: 2.0000e-04 eta: 3:44:58 time: 0.3802 data_time: 0.2387 memory: 6717 grad_norm: 3.4536 loss: 1.1738 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1738 2023/04/14 12:54:29 - mmengine - INFO - Epoch(train) [81][1120/1879] lr: 2.0000e-04 eta: 3:44:51 time: 0.3489 data_time: 0.2105 memory: 6717 grad_norm: 3.3558 loss: 1.0480 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0480 2023/04/14 12:54:38 - mmengine - INFO - Epoch(train) [81][1140/1879] lr: 2.0000e-04 eta: 3:44:44 time: 0.4247 data_time: 0.2832 memory: 6717 grad_norm: 3.3030 loss: 1.1032 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1032 2023/04/14 12:54:44 - mmengine - INFO - Epoch(train) [81][1160/1879] lr: 2.0000e-04 eta: 3:44:36 time: 0.3141 data_time: 0.1749 memory: 6717 grad_norm: 3.3667 loss: 1.1005 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1005 2023/04/14 12:54:52 - mmengine - INFO - Epoch(train) [81][1180/1879] lr: 2.0000e-04 eta: 3:44:29 time: 0.4039 data_time: 0.2587 memory: 6717 grad_norm: 3.5066 loss: 1.0925 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0925 2023/04/14 12:54:58 - mmengine - INFO - Epoch(train) [81][1200/1879] lr: 2.0000e-04 eta: 3:44:21 time: 0.3197 data_time: 0.1774 memory: 6717 grad_norm: 3.4796 loss: 1.1573 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1573 2023/04/14 12:55:06 - mmengine - INFO - Epoch(train) [81][1220/1879] lr: 2.0000e-04 eta: 3:44:14 time: 0.4045 data_time: 0.2406 memory: 6717 grad_norm: 3.4324 loss: 1.1150 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1150 2023/04/14 12:55:13 - mmengine - INFO - Epoch(train) [81][1240/1879] lr: 2.0000e-04 eta: 3:44:06 time: 0.3258 data_time: 0.1810 memory: 6717 grad_norm: 3.4277 loss: 1.1438 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1438 2023/04/14 12:55:22 - mmengine - INFO - Epoch(train) [81][1260/1879] lr: 2.0000e-04 eta: 3:43:59 time: 0.4369 data_time: 0.2939 memory: 6717 grad_norm: 3.4323 loss: 1.2810 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2810 2023/04/14 12:55:29 - mmengine - INFO - Epoch(train) [81][1280/1879] lr: 2.0000e-04 eta: 3:43:52 time: 0.3395 data_time: 0.1947 memory: 6717 grad_norm: 3.3721 loss: 1.1196 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1196 2023/04/14 12:55:36 - mmengine - INFO - Epoch(train) [81][1300/1879] lr: 2.0000e-04 eta: 3:43:44 time: 0.3752 data_time: 0.2334 memory: 6717 grad_norm: 3.3252 loss: 1.0815 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0815 2023/04/14 12:55:43 - mmengine - INFO - Epoch(train) [81][1320/1879] lr: 2.0000e-04 eta: 3:43:37 time: 0.3444 data_time: 0.2023 memory: 6717 grad_norm: 3.3371 loss: 0.9314 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9314 2023/04/14 12:55:51 - mmengine - INFO - Epoch(train) [81][1340/1879] lr: 2.0000e-04 eta: 3:43:30 time: 0.4245 data_time: 0.2837 memory: 6717 grad_norm: 3.3426 loss: 1.0773 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0773 2023/04/14 12:55:57 - mmengine - INFO - Epoch(train) [81][1360/1879] lr: 2.0000e-04 eta: 3:43:22 time: 0.2838 data_time: 0.1434 memory: 6717 grad_norm: 3.3997 loss: 1.1300 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1300 2023/04/14 12:56:05 - mmengine - INFO - Epoch(train) [81][1380/1879] lr: 2.0000e-04 eta: 3:43:15 time: 0.4133 data_time: 0.2706 memory: 6717 grad_norm: 3.4080 loss: 1.0872 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0872 2023/04/14 12:56:12 - mmengine - INFO - Epoch(train) [81][1400/1879] lr: 2.0000e-04 eta: 3:43:07 time: 0.3519 data_time: 0.2102 memory: 6717 grad_norm: 3.3355 loss: 0.9546 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9546 2023/04/14 12:56:20 - mmengine - INFO - Epoch(train) [81][1420/1879] lr: 2.0000e-04 eta: 3:43:00 time: 0.3829 data_time: 0.2436 memory: 6717 grad_norm: 3.3262 loss: 1.1129 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1129 2023/04/14 12:56:27 - mmengine - INFO - Epoch(train) [81][1440/1879] lr: 2.0000e-04 eta: 3:42:52 time: 0.3255 data_time: 0.1673 memory: 6717 grad_norm: 3.4851 loss: 1.2062 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2062 2023/04/14 12:56:34 - mmengine - INFO - Epoch(train) [81][1460/1879] lr: 2.0000e-04 eta: 3:42:45 time: 0.3836 data_time: 0.2154 memory: 6717 grad_norm: 3.4498 loss: 1.1422 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1422 2023/04/14 12:56:41 - mmengine - INFO - Epoch(train) [81][1480/1879] lr: 2.0000e-04 eta: 3:42:37 time: 0.3524 data_time: 0.1879 memory: 6717 grad_norm: 3.3649 loss: 0.9618 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.9618 2023/04/14 12:56:50 - mmengine - INFO - Epoch(train) [81][1500/1879] lr: 2.0000e-04 eta: 3:42:30 time: 0.4372 data_time: 0.3039 memory: 6717 grad_norm: 3.3532 loss: 1.1183 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1183 2023/04/14 12:56:56 - mmengine - INFO - Epoch(train) [81][1520/1879] lr: 2.0000e-04 eta: 3:42:23 time: 0.2910 data_time: 0.1314 memory: 6717 grad_norm: 3.4069 loss: 1.1997 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1997 2023/04/14 12:57:04 - mmengine - INFO - Epoch(train) [81][1540/1879] lr: 2.0000e-04 eta: 3:42:15 time: 0.3929 data_time: 0.1557 memory: 6717 grad_norm: 3.3542 loss: 1.0314 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0314 2023/04/14 12:57:11 - mmengine - INFO - Epoch(train) [81][1560/1879] lr: 2.0000e-04 eta: 3:42:08 time: 0.3528 data_time: 0.1253 memory: 6717 grad_norm: 3.5010 loss: 1.1398 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1398 2023/04/14 12:57:19 - mmengine - INFO - Epoch(train) [81][1580/1879] lr: 2.0000e-04 eta: 3:42:01 time: 0.3936 data_time: 0.1284 memory: 6717 grad_norm: 3.3265 loss: 1.0705 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0705 2023/04/14 12:57:25 - mmengine - INFO - Epoch(train) [81][1600/1879] lr: 2.0000e-04 eta: 3:41:53 time: 0.3276 data_time: 0.1367 memory: 6717 grad_norm: 3.3543 loss: 1.1874 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1874 2023/04/14 12:57:34 - mmengine - INFO - Epoch(train) [81][1620/1879] lr: 2.0000e-04 eta: 3:41:46 time: 0.4160 data_time: 0.1284 memory: 6717 grad_norm: 3.3462 loss: 1.0368 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0368 2023/04/14 12:57:40 - mmengine - INFO - Epoch(train) [81][1640/1879] lr: 2.0000e-04 eta: 3:41:38 time: 0.3144 data_time: 0.0872 memory: 6717 grad_norm: 3.4278 loss: 1.2662 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2662 2023/04/14 12:57:48 - mmengine - INFO - Epoch(train) [81][1660/1879] lr: 2.0000e-04 eta: 3:41:31 time: 0.4129 data_time: 0.1993 memory: 6717 grad_norm: 3.5157 loss: 1.2548 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2548 2023/04/14 12:57:55 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 12:57:55 - mmengine - INFO - Epoch(train) [81][1680/1879] lr: 2.0000e-04 eta: 3:41:23 time: 0.3437 data_time: 0.1129 memory: 6717 grad_norm: 3.3650 loss: 1.1912 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1912 2023/04/14 12:58:03 - mmengine - INFO - Epoch(train) [81][1700/1879] lr: 2.0000e-04 eta: 3:41:16 time: 0.3784 data_time: 0.0795 memory: 6717 grad_norm: 3.4429 loss: 1.1167 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1167 2023/04/14 12:58:10 - mmengine - INFO - Epoch(train) [81][1720/1879] lr: 2.0000e-04 eta: 3:41:09 time: 0.3606 data_time: 0.1561 memory: 6717 grad_norm: 3.3873 loss: 1.0336 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0336 2023/04/14 12:58:17 - mmengine - INFO - Epoch(train) [81][1740/1879] lr: 2.0000e-04 eta: 3:41:01 time: 0.3783 data_time: 0.2102 memory: 6717 grad_norm: 3.3475 loss: 1.1693 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1693 2023/04/14 12:58:24 - mmengine - INFO - Epoch(train) [81][1760/1879] lr: 2.0000e-04 eta: 3:40:54 time: 0.3361 data_time: 0.1248 memory: 6717 grad_norm: 3.3576 loss: 1.2139 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2139 2023/04/14 12:58:32 - mmengine - INFO - Epoch(train) [81][1780/1879] lr: 2.0000e-04 eta: 3:40:46 time: 0.4137 data_time: 0.0854 memory: 6717 grad_norm: 3.3415 loss: 1.0236 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0236 2023/04/14 12:58:39 - mmengine - INFO - Epoch(train) [81][1800/1879] lr: 2.0000e-04 eta: 3:40:39 time: 0.3415 data_time: 0.0685 memory: 6717 grad_norm: 3.3528 loss: 1.0781 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0781 2023/04/14 12:58:47 - mmengine - INFO - Epoch(train) [81][1820/1879] lr: 2.0000e-04 eta: 3:40:32 time: 0.3882 data_time: 0.0182 memory: 6717 grad_norm: 3.4365 loss: 1.0085 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0085 2023/04/14 12:58:54 - mmengine - INFO - Epoch(train) [81][1840/1879] lr: 2.0000e-04 eta: 3:40:24 time: 0.3568 data_time: 0.0137 memory: 6717 grad_norm: 3.3851 loss: 1.1888 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1888 2023/04/14 12:59:02 - mmengine - INFO - Epoch(train) [81][1860/1879] lr: 2.0000e-04 eta: 3:40:17 time: 0.3989 data_time: 0.0291 memory: 6717 grad_norm: 3.3667 loss: 1.1698 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1698 2023/04/14 12:59:08 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 12:59:08 - mmengine - INFO - Epoch(train) [81][1879/1879] lr: 2.0000e-04 eta: 3:40:10 time: 0.3854 data_time: 0.0278 memory: 6717 grad_norm: 3.4618 loss: 1.1496 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.1496 2023/04/14 12:59:08 - mmengine - INFO - Saving checkpoint at 81 epochs 2023/04/14 12:59:18 - mmengine - INFO - Epoch(val) [81][ 20/155] eta: 0:01:01 time: 0.4586 data_time: 0.4260 memory: 1391 2023/04/14 12:59:24 - mmengine - INFO - Epoch(val) [81][ 40/155] eta: 0:00:43 time: 0.2957 data_time: 0.2630 memory: 1391 2023/04/14 12:59:33 - mmengine - INFO - Epoch(val) [81][ 60/155] eta: 0:00:38 time: 0.4568 data_time: 0.4232 memory: 1391 2023/04/14 12:59:39 - mmengine - INFO - Epoch(val) [81][ 80/155] eta: 0:00:28 time: 0.3164 data_time: 0.2831 memory: 1391 2023/04/14 12:59:48 - mmengine - INFO - Epoch(val) [81][100/155] eta: 0:00:21 time: 0.4515 data_time: 0.4182 memory: 1391 2023/04/14 12:59:54 - mmengine - INFO - Epoch(val) [81][120/155] eta: 0:00:13 time: 0.2995 data_time: 0.2663 memory: 1391 2023/04/14 13:00:04 - mmengine - INFO - Epoch(val) [81][140/155] eta: 0:00:05 time: 0.4844 data_time: 0.4517 memory: 1391 2023/04/14 13:00:11 - mmengine - INFO - Epoch(val) [81][155/155] acc/top1: 0.6665 acc/top5: 0.8746 acc/mean1: 0.6664 data_time: 0.4176 time: 0.4501 2023/04/14 13:00:11 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_80.pth is removed 2023/04/14 13:00:12 - mmengine - INFO - The best checkpoint with 0.6665 acc/top1 at 81 epoch is saved to best_acc_top1_epoch_81.pth. 2023/04/14 13:00:21 - mmengine - INFO - Epoch(train) [82][ 20/1879] lr: 2.0000e-04 eta: 3:40:03 time: 0.4893 data_time: 0.3518 memory: 6717 grad_norm: 3.3462 loss: 1.0631 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0631 2023/04/14 13:00:28 - mmengine - INFO - Epoch(train) [82][ 40/1879] lr: 2.0000e-04 eta: 3:39:55 time: 0.3487 data_time: 0.2155 memory: 6717 grad_norm: 3.4335 loss: 1.3434 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3434 2023/04/14 13:00:37 - mmengine - INFO - Epoch(train) [82][ 60/1879] lr: 2.0000e-04 eta: 3:39:48 time: 0.4130 data_time: 0.2766 memory: 6717 grad_norm: 3.3507 loss: 1.0675 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0675 2023/04/14 13:00:43 - mmengine - INFO - Epoch(train) [82][ 80/1879] lr: 2.0000e-04 eta: 3:39:40 time: 0.2934 data_time: 0.1604 memory: 6717 grad_norm: 3.2450 loss: 1.1481 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1481 2023/04/14 13:00:51 - mmengine - INFO - Epoch(train) [82][ 100/1879] lr: 2.0000e-04 eta: 3:39:33 time: 0.4054 data_time: 0.1876 memory: 6717 grad_norm: 3.4803 loss: 1.1167 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1167 2023/04/14 13:00:57 - mmengine - INFO - Epoch(train) [82][ 120/1879] lr: 2.0000e-04 eta: 3:39:25 time: 0.3200 data_time: 0.1392 memory: 6717 grad_norm: 3.3588 loss: 1.2651 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2651 2023/04/14 13:01:05 - mmengine - INFO - Epoch(train) [82][ 140/1879] lr: 2.0000e-04 eta: 3:39:18 time: 0.4075 data_time: 0.2412 memory: 6717 grad_norm: 3.3769 loss: 1.0828 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0828 2023/04/14 13:01:12 - mmengine - INFO - Epoch(train) [82][ 160/1879] lr: 2.0000e-04 eta: 3:39:11 time: 0.3275 data_time: 0.1335 memory: 6717 grad_norm: 3.4199 loss: 1.0851 top1_acc: 0.3125 top5_acc: 0.8125 loss_cls: 1.0851 2023/04/14 13:01:20 - mmengine - INFO - Epoch(train) [82][ 180/1879] lr: 2.0000e-04 eta: 3:39:03 time: 0.4144 data_time: 0.2330 memory: 6717 grad_norm: 3.3935 loss: 1.0306 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0306 2023/04/14 13:01:27 - mmengine - INFO - Epoch(train) [82][ 200/1879] lr: 2.0000e-04 eta: 3:38:56 time: 0.3326 data_time: 0.1247 memory: 6717 grad_norm: 3.4811 loss: 1.0577 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0577 2023/04/14 13:01:35 - mmengine - INFO - Epoch(train) [82][ 220/1879] lr: 2.0000e-04 eta: 3:38:49 time: 0.4015 data_time: 0.1389 memory: 6717 grad_norm: 3.4215 loss: 1.1505 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1505 2023/04/14 13:01:42 - mmengine - INFO - Epoch(train) [82][ 240/1879] lr: 2.0000e-04 eta: 3:38:41 time: 0.3371 data_time: 0.0878 memory: 6717 grad_norm: 3.2697 loss: 1.1407 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1407 2023/04/14 13:01:50 - mmengine - INFO - Epoch(train) [82][ 260/1879] lr: 2.0000e-04 eta: 3:38:34 time: 0.4091 data_time: 0.1039 memory: 6717 grad_norm: 3.3648 loss: 1.0006 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0006 2023/04/14 13:01:56 - mmengine - INFO - Epoch(train) [82][ 280/1879] lr: 2.0000e-04 eta: 3:38:26 time: 0.3318 data_time: 0.0413 memory: 6717 grad_norm: 3.3114 loss: 1.0401 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0401 2023/04/14 13:02:05 - mmengine - INFO - Epoch(train) [82][ 300/1879] lr: 2.0000e-04 eta: 3:38:19 time: 0.4259 data_time: 0.0362 memory: 6717 grad_norm: 3.3258 loss: 1.0534 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0534 2023/04/14 13:02:11 - mmengine - INFO - Epoch(train) [82][ 320/1879] lr: 2.0000e-04 eta: 3:38:11 time: 0.3135 data_time: 0.0197 memory: 6717 grad_norm: 3.3435 loss: 1.0692 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0692 2023/04/14 13:02:20 - mmengine - INFO - Epoch(train) [82][ 340/1879] lr: 2.0000e-04 eta: 3:38:04 time: 0.4168 data_time: 0.0160 memory: 6717 grad_norm: 3.3913 loss: 1.0892 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0892 2023/04/14 13:02:26 - mmengine - INFO - Epoch(train) [82][ 360/1879] lr: 2.0000e-04 eta: 3:37:57 time: 0.3314 data_time: 0.0521 memory: 6717 grad_norm: 3.3769 loss: 1.1233 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1233 2023/04/14 13:02:34 - mmengine - INFO - Epoch(train) [82][ 380/1879] lr: 2.0000e-04 eta: 3:37:49 time: 0.3998 data_time: 0.1365 memory: 6717 grad_norm: 3.4327 loss: 1.1188 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1188 2023/04/14 13:02:41 - mmengine - INFO - Epoch(train) [82][ 400/1879] lr: 2.0000e-04 eta: 3:37:42 time: 0.3525 data_time: 0.0132 memory: 6717 grad_norm: 3.3909 loss: 1.2706 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2706 2023/04/14 13:02:49 - mmengine - INFO - Epoch(train) [82][ 420/1879] lr: 2.0000e-04 eta: 3:37:35 time: 0.3891 data_time: 0.0342 memory: 6717 grad_norm: 3.3640 loss: 1.1060 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1060 2023/04/14 13:02:56 - mmengine - INFO - Epoch(train) [82][ 440/1879] lr: 2.0000e-04 eta: 3:37:27 time: 0.3733 data_time: 0.0129 memory: 6717 grad_norm: 3.3766 loss: 0.9576 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9576 2023/04/14 13:03:05 - mmengine - INFO - Epoch(train) [82][ 460/1879] lr: 2.0000e-04 eta: 3:37:20 time: 0.4019 data_time: 0.0143 memory: 6717 grad_norm: 3.2698 loss: 1.1493 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1493 2023/04/14 13:03:11 - mmengine - INFO - Epoch(train) [82][ 480/1879] lr: 2.0000e-04 eta: 3:37:12 time: 0.3454 data_time: 0.0149 memory: 6717 grad_norm: 3.4094 loss: 1.0735 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0735 2023/04/14 13:03:20 - mmengine - INFO - Epoch(train) [82][ 500/1879] lr: 2.0000e-04 eta: 3:37:05 time: 0.4166 data_time: 0.0151 memory: 6717 grad_norm: 3.4236 loss: 1.2737 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2737 2023/04/14 13:03:27 - mmengine - INFO - Epoch(train) [82][ 520/1879] lr: 2.0000e-04 eta: 3:36:58 time: 0.3465 data_time: 0.0139 memory: 6717 grad_norm: 3.3559 loss: 1.1709 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1709 2023/04/14 13:03:35 - mmengine - INFO - Epoch(train) [82][ 540/1879] lr: 2.0000e-04 eta: 3:36:50 time: 0.3953 data_time: 0.0153 memory: 6717 grad_norm: 3.3713 loss: 1.0842 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0842 2023/04/14 13:03:41 - mmengine - INFO - Epoch(train) [82][ 560/1879] lr: 2.0000e-04 eta: 3:36:43 time: 0.3384 data_time: 0.0134 memory: 6717 grad_norm: 3.2659 loss: 1.0132 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0132 2023/04/14 13:03:49 - mmengine - INFO - Epoch(train) [82][ 580/1879] lr: 2.0000e-04 eta: 3:36:35 time: 0.3668 data_time: 0.0203 memory: 6717 grad_norm: 3.4371 loss: 1.1665 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1665 2023/04/14 13:03:55 - mmengine - INFO - Epoch(train) [82][ 600/1879] lr: 2.0000e-04 eta: 3:36:28 time: 0.3364 data_time: 0.0318 memory: 6717 grad_norm: 3.3611 loss: 1.1616 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1616 2023/04/14 13:04:03 - mmengine - INFO - Epoch(train) [82][ 620/1879] lr: 2.0000e-04 eta: 3:36:21 time: 0.3846 data_time: 0.0161 memory: 6717 grad_norm: 3.3886 loss: 1.0492 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0492 2023/04/14 13:04:10 - mmengine - INFO - Epoch(train) [82][ 640/1879] lr: 2.0000e-04 eta: 3:36:13 time: 0.3242 data_time: 0.0326 memory: 6717 grad_norm: 3.4422 loss: 1.0573 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0573 2023/04/14 13:04:18 - mmengine - INFO - Epoch(train) [82][ 660/1879] lr: 2.0000e-04 eta: 3:36:06 time: 0.4085 data_time: 0.0204 memory: 6717 grad_norm: 3.4408 loss: 1.1339 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1339 2023/04/14 13:04:24 - mmengine - INFO - Epoch(train) [82][ 680/1879] lr: 2.0000e-04 eta: 3:35:58 time: 0.3269 data_time: 0.0133 memory: 6717 grad_norm: 3.5034 loss: 1.1955 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1955 2023/04/14 13:04:33 - mmengine - INFO - Epoch(train) [82][ 700/1879] lr: 2.0000e-04 eta: 3:35:51 time: 0.4231 data_time: 0.0629 memory: 6717 grad_norm: 3.3708 loss: 1.1069 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1069 2023/04/14 13:04:40 - mmengine - INFO - Epoch(train) [82][ 720/1879] lr: 2.0000e-04 eta: 3:35:44 time: 0.3694 data_time: 0.0726 memory: 6717 grad_norm: 3.3993 loss: 1.1768 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1768 2023/04/14 13:04:48 - mmengine - INFO - Epoch(train) [82][ 740/1879] lr: 2.0000e-04 eta: 3:35:36 time: 0.4003 data_time: 0.0661 memory: 6717 grad_norm: 3.4325 loss: 1.2427 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2427 2023/04/14 13:04:55 - mmengine - INFO - Epoch(train) [82][ 760/1879] lr: 2.0000e-04 eta: 3:35:29 time: 0.3371 data_time: 0.0310 memory: 6717 grad_norm: 3.3453 loss: 1.1577 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1577 2023/04/14 13:05:03 - mmengine - INFO - Epoch(train) [82][ 780/1879] lr: 2.0000e-04 eta: 3:35:22 time: 0.4072 data_time: 0.0266 memory: 6717 grad_norm: 3.4590 loss: 1.1360 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1360 2023/04/14 13:05:10 - mmengine - INFO - Epoch(train) [82][ 800/1879] lr: 2.0000e-04 eta: 3:35:14 time: 0.3451 data_time: 0.0136 memory: 6717 grad_norm: 3.2886 loss: 0.9517 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9517 2023/04/14 13:05:10 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 13:05:18 - mmengine - INFO - Epoch(train) [82][ 820/1879] lr: 2.0000e-04 eta: 3:35:07 time: 0.4069 data_time: 0.0155 memory: 6717 grad_norm: 3.3579 loss: 1.2033 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2033 2023/04/14 13:05:25 - mmengine - INFO - Epoch(train) [82][ 840/1879] lr: 2.0000e-04 eta: 3:34:59 time: 0.3205 data_time: 0.0139 memory: 6717 grad_norm: 3.3821 loss: 1.1185 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1185 2023/04/14 13:05:33 - mmengine - INFO - Epoch(train) [82][ 860/1879] lr: 2.0000e-04 eta: 3:34:52 time: 0.4078 data_time: 0.0152 memory: 6717 grad_norm: 3.4487 loss: 1.0922 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0922 2023/04/14 13:05:39 - mmengine - INFO - Epoch(train) [82][ 880/1879] lr: 2.0000e-04 eta: 3:34:44 time: 0.3244 data_time: 0.0146 memory: 6717 grad_norm: 3.4049 loss: 1.0701 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0701 2023/04/14 13:05:47 - mmengine - INFO - Epoch(train) [82][ 900/1879] lr: 2.0000e-04 eta: 3:34:37 time: 0.3978 data_time: 0.0133 memory: 6717 grad_norm: 3.4320 loss: 1.3253 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3253 2023/04/14 13:05:54 - mmengine - INFO - Epoch(train) [82][ 920/1879] lr: 2.0000e-04 eta: 3:34:29 time: 0.3135 data_time: 0.0322 memory: 6717 grad_norm: 3.3941 loss: 1.2120 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2120 2023/04/14 13:06:02 - mmengine - INFO - Epoch(train) [82][ 940/1879] lr: 2.0000e-04 eta: 3:34:22 time: 0.4303 data_time: 0.0149 memory: 6717 grad_norm: 3.2478 loss: 1.1172 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1172 2023/04/14 13:06:08 - mmengine - INFO - Epoch(train) [82][ 960/1879] lr: 2.0000e-04 eta: 3:34:15 time: 0.2965 data_time: 0.0155 memory: 6717 grad_norm: 3.3664 loss: 1.0776 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0776 2023/04/14 13:06:17 - mmengine - INFO - Epoch(train) [82][ 980/1879] lr: 2.0000e-04 eta: 3:34:07 time: 0.4324 data_time: 0.0133 memory: 6717 grad_norm: 3.4440 loss: 1.3664 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3664 2023/04/14 13:06:24 - mmengine - INFO - Epoch(train) [82][1000/1879] lr: 2.0000e-04 eta: 3:34:00 time: 0.3407 data_time: 0.0162 memory: 6717 grad_norm: 3.3477 loss: 1.1648 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1648 2023/04/14 13:06:31 - mmengine - INFO - Epoch(train) [82][1020/1879] lr: 2.0000e-04 eta: 3:33:53 time: 0.3875 data_time: 0.0130 memory: 6717 grad_norm: 3.3462 loss: 0.9559 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9559 2023/04/14 13:06:38 - mmengine - INFO - Epoch(train) [82][1040/1879] lr: 2.0000e-04 eta: 3:33:45 time: 0.3303 data_time: 0.0147 memory: 6717 grad_norm: 3.3916 loss: 1.1740 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1740 2023/04/14 13:06:46 - mmengine - INFO - Epoch(train) [82][1060/1879] lr: 2.0000e-04 eta: 3:33:38 time: 0.3911 data_time: 0.0145 memory: 6717 grad_norm: 3.4145 loss: 1.1593 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1593 2023/04/14 13:06:53 - mmengine - INFO - Epoch(train) [82][1080/1879] lr: 2.0000e-04 eta: 3:33:30 time: 0.3402 data_time: 0.0155 memory: 6717 grad_norm: 3.4241 loss: 1.0145 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0145 2023/04/14 13:07:01 - mmengine - INFO - Epoch(train) [82][1100/1879] lr: 2.0000e-04 eta: 3:33:23 time: 0.4091 data_time: 0.0133 memory: 6717 grad_norm: 3.3299 loss: 1.2808 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2808 2023/04/14 13:07:07 - mmengine - INFO - Epoch(train) [82][1120/1879] lr: 2.0000e-04 eta: 3:33:15 time: 0.3298 data_time: 0.0144 memory: 6717 grad_norm: 3.4931 loss: 1.2643 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2643 2023/04/14 13:07:16 - mmengine - INFO - Epoch(train) [82][1140/1879] lr: 2.0000e-04 eta: 3:33:08 time: 0.4349 data_time: 0.0132 memory: 6717 grad_norm: 3.3922 loss: 1.2061 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2061 2023/04/14 13:07:23 - mmengine - INFO - Epoch(train) [82][1160/1879] lr: 2.0000e-04 eta: 3:33:01 time: 0.3491 data_time: 0.0143 memory: 6717 grad_norm: 3.4668 loss: 1.2582 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2582 2023/04/14 13:07:30 - mmengine - INFO - Epoch(train) [82][1180/1879] lr: 2.0000e-04 eta: 3:32:53 time: 0.3702 data_time: 0.0150 memory: 6717 grad_norm: 3.3021 loss: 1.1446 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1446 2023/04/14 13:07:37 - mmengine - INFO - Epoch(train) [82][1200/1879] lr: 2.0000e-04 eta: 3:32:46 time: 0.3421 data_time: 0.0147 memory: 6717 grad_norm: 3.3424 loss: 1.0913 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0913 2023/04/14 13:07:46 - mmengine - INFO - Epoch(train) [82][1220/1879] lr: 2.0000e-04 eta: 3:32:39 time: 0.4194 data_time: 0.0147 memory: 6717 grad_norm: 3.4384 loss: 1.2224 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2224 2023/04/14 13:07:52 - mmengine - INFO - Epoch(train) [82][1240/1879] lr: 2.0000e-04 eta: 3:32:31 time: 0.3285 data_time: 0.0153 memory: 6717 grad_norm: 3.4044 loss: 0.9398 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9398 2023/04/14 13:08:01 - mmengine - INFO - Epoch(train) [82][1260/1879] lr: 2.0000e-04 eta: 3:32:24 time: 0.4135 data_time: 0.0141 memory: 6717 grad_norm: 3.3620 loss: 1.1578 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1578 2023/04/14 13:08:07 - mmengine - INFO - Epoch(train) [82][1280/1879] lr: 2.0000e-04 eta: 3:32:16 time: 0.3172 data_time: 0.0143 memory: 6717 grad_norm: 3.3669 loss: 1.0983 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0983 2023/04/14 13:08:15 - mmengine - INFO - Epoch(train) [82][1300/1879] lr: 2.0000e-04 eta: 3:32:09 time: 0.4189 data_time: 0.0143 memory: 6717 grad_norm: 3.3615 loss: 0.9135 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9135 2023/04/14 13:08:21 - mmengine - INFO - Epoch(train) [82][1320/1879] lr: 2.0000e-04 eta: 3:32:01 time: 0.3012 data_time: 0.0151 memory: 6717 grad_norm: 3.4629 loss: 1.2251 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.2251 2023/04/14 13:08:30 - mmengine - INFO - Epoch(train) [82][1340/1879] lr: 2.0000e-04 eta: 3:31:54 time: 0.4199 data_time: 0.0128 memory: 6717 grad_norm: 3.3721 loss: 1.1328 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1328 2023/04/14 13:08:37 - mmengine - INFO - Epoch(train) [82][1360/1879] lr: 2.0000e-04 eta: 3:31:47 time: 0.3614 data_time: 0.0156 memory: 6717 grad_norm: 3.4141 loss: 0.9946 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9946 2023/04/14 13:08:45 - mmengine - INFO - Epoch(train) [82][1380/1879] lr: 2.0000e-04 eta: 3:31:39 time: 0.3973 data_time: 0.0134 memory: 6717 grad_norm: 3.4292 loss: 1.0177 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0177 2023/04/14 13:08:54 - mmengine - INFO - Epoch(train) [82][1400/1879] lr: 2.0000e-04 eta: 3:31:32 time: 0.4380 data_time: 0.0147 memory: 6717 grad_norm: 3.5327 loss: 1.2227 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2227 2023/04/14 13:09:00 - mmengine - INFO - Epoch(train) [82][1420/1879] lr: 2.0000e-04 eta: 3:31:25 time: 0.3063 data_time: 0.0149 memory: 6717 grad_norm: 3.3990 loss: 1.1464 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1464 2023/04/14 13:09:08 - mmengine - INFO - Epoch(train) [82][1440/1879] lr: 2.0000e-04 eta: 3:31:17 time: 0.3971 data_time: 0.0158 memory: 6717 grad_norm: 3.3617 loss: 1.1613 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1613 2023/04/14 13:09:15 - mmengine - INFO - Epoch(train) [82][1460/1879] lr: 2.0000e-04 eta: 3:31:10 time: 0.3516 data_time: 0.0130 memory: 6717 grad_norm: 3.2759 loss: 1.1104 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1104 2023/04/14 13:09:23 - mmengine - INFO - Epoch(train) [82][1480/1879] lr: 2.0000e-04 eta: 3:31:03 time: 0.4169 data_time: 0.0153 memory: 6717 grad_norm: 3.3685 loss: 1.0812 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0812 2023/04/14 13:09:29 - mmengine - INFO - Epoch(train) [82][1500/1879] lr: 2.0000e-04 eta: 3:30:55 time: 0.3088 data_time: 0.0136 memory: 6717 grad_norm: 3.3136 loss: 1.0923 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.0923 2023/04/14 13:09:37 - mmengine - INFO - Epoch(train) [82][1520/1879] lr: 2.0000e-04 eta: 3:30:48 time: 0.3978 data_time: 0.0163 memory: 6717 grad_norm: 3.3808 loss: 1.1351 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1351 2023/04/14 13:09:44 - mmengine - INFO - Epoch(train) [82][1540/1879] lr: 2.0000e-04 eta: 3:30:40 time: 0.3178 data_time: 0.0124 memory: 6717 grad_norm: 3.4078 loss: 1.0408 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0408 2023/04/14 13:09:52 - mmengine - INFO - Epoch(train) [82][1560/1879] lr: 2.0000e-04 eta: 3:30:33 time: 0.4209 data_time: 0.0162 memory: 6717 grad_norm: 3.4067 loss: 1.1225 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1225 2023/04/14 13:09:59 - mmengine - INFO - Epoch(train) [82][1580/1879] lr: 2.0000e-04 eta: 3:30:25 time: 0.3431 data_time: 0.0143 memory: 6717 grad_norm: 3.4007 loss: 1.1814 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1814 2023/04/14 13:10:06 - mmengine - INFO - Epoch(train) [82][1600/1879] lr: 2.0000e-04 eta: 3:30:18 time: 0.3751 data_time: 0.0159 memory: 6717 grad_norm: 3.3279 loss: 1.1520 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1520 2023/04/14 13:10:15 - mmengine - INFO - Epoch(train) [82][1620/1879] lr: 2.0000e-04 eta: 3:30:11 time: 0.4175 data_time: 0.0140 memory: 6717 grad_norm: 3.4388 loss: 0.9951 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9951 2023/04/14 13:10:22 - mmengine - INFO - Epoch(train) [82][1640/1879] lr: 2.0000e-04 eta: 3:30:03 time: 0.3475 data_time: 0.0157 memory: 6717 grad_norm: 3.3228 loss: 1.0429 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0429 2023/04/14 13:10:30 - mmengine - INFO - Epoch(train) [82][1660/1879] lr: 2.0000e-04 eta: 3:29:56 time: 0.3948 data_time: 0.0128 memory: 6717 grad_norm: 3.3277 loss: 1.0415 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0415 2023/04/14 13:10:36 - mmengine - INFO - Epoch(train) [82][1680/1879] lr: 2.0000e-04 eta: 3:29:48 time: 0.3275 data_time: 0.0156 memory: 6717 grad_norm: 3.3515 loss: 0.9507 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9507 2023/04/14 13:10:45 - mmengine - INFO - Epoch(train) [82][1700/1879] lr: 2.0000e-04 eta: 3:29:41 time: 0.4204 data_time: 0.0132 memory: 6717 grad_norm: 3.4397 loss: 1.2235 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2235 2023/04/14 13:10:51 - mmengine - INFO - Epoch(train) [82][1720/1879] lr: 2.0000e-04 eta: 3:29:34 time: 0.3240 data_time: 0.0152 memory: 6717 grad_norm: 3.4099 loss: 1.1625 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1625 2023/04/14 13:10:59 - mmengine - INFO - Epoch(train) [82][1740/1879] lr: 2.0000e-04 eta: 3:29:26 time: 0.3845 data_time: 0.0133 memory: 6717 grad_norm: 3.3801 loss: 1.1465 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1465 2023/04/14 13:11:05 - mmengine - INFO - Epoch(train) [82][1760/1879] lr: 2.0000e-04 eta: 3:29:19 time: 0.3322 data_time: 0.0154 memory: 6717 grad_norm: 3.3945 loss: 0.9723 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9723 2023/04/14 13:11:13 - mmengine - INFO - Epoch(train) [82][1780/1879] lr: 2.0000e-04 eta: 3:29:11 time: 0.3821 data_time: 0.0135 memory: 6717 grad_norm: 3.4593 loss: 1.0914 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0914 2023/04/14 13:11:21 - mmengine - INFO - Epoch(train) [82][1800/1879] lr: 2.0000e-04 eta: 3:29:04 time: 0.3873 data_time: 0.0157 memory: 6717 grad_norm: 3.4388 loss: 1.1857 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1857 2023/04/14 13:11:21 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 13:11:27 - mmengine - INFO - Epoch(train) [82][1820/1879] lr: 2.0000e-04 eta: 3:28:57 time: 0.3258 data_time: 0.0146 memory: 6717 grad_norm: 3.4097 loss: 1.1823 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1823 2023/04/14 13:11:36 - mmengine - INFO - Epoch(train) [82][1840/1879] lr: 2.0000e-04 eta: 3:28:49 time: 0.4312 data_time: 0.0136 memory: 6717 grad_norm: 3.5084 loss: 1.1932 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1932 2023/04/14 13:11:43 - mmengine - INFO - Epoch(train) [82][1860/1879] lr: 2.0000e-04 eta: 3:28:42 time: 0.3390 data_time: 0.0155 memory: 6717 grad_norm: 3.4122 loss: 1.1179 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1179 2023/04/14 13:11:49 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 13:11:49 - mmengine - INFO - Epoch(train) [82][1879/1879] lr: 2.0000e-04 eta: 3:28:35 time: 0.3008 data_time: 0.0112 memory: 6717 grad_norm: 3.4657 loss: 0.9810 top1_acc: 0.5714 top5_acc: 1.0000 loss_cls: 0.9810 2023/04/14 13:11:58 - mmengine - INFO - Epoch(val) [82][ 20/155] eta: 0:01:02 time: 0.4645 data_time: 0.4312 memory: 1391 2023/04/14 13:12:04 - mmengine - INFO - Epoch(val) [82][ 40/155] eta: 0:00:44 time: 0.3177 data_time: 0.2844 memory: 1391 2023/04/14 13:12:13 - mmengine - INFO - Epoch(val) [82][ 60/155] eta: 0:00:38 time: 0.4311 data_time: 0.3977 memory: 1391 2023/04/14 13:12:19 - mmengine - INFO - Epoch(val) [82][ 80/155] eta: 0:00:28 time: 0.3177 data_time: 0.2841 memory: 1391 2023/04/14 13:12:28 - mmengine - INFO - Epoch(val) [82][100/155] eta: 0:00:21 time: 0.4560 data_time: 0.4230 memory: 1391 2023/04/14 13:12:34 - mmengine - INFO - Epoch(val) [82][120/155] eta: 0:00:13 time: 0.3008 data_time: 0.2675 memory: 1391 2023/04/14 13:12:43 - mmengine - INFO - Epoch(val) [82][140/155] eta: 0:00:05 time: 0.4431 data_time: 0.4095 memory: 1391 2023/04/14 13:12:50 - mmengine - INFO - Epoch(val) [82][155/155] acc/top1: 0.6666 acc/top5: 0.8740 acc/mean1: 0.6666 data_time: 0.3577 time: 0.3899 2023/04/14 13:12:50 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_81.pth is removed 2023/04/14 13:12:51 - mmengine - INFO - The best checkpoint with 0.6666 acc/top1 at 82 epoch is saved to best_acc_top1_epoch_82.pth. 2023/04/14 13:13:00 - mmengine - INFO - Epoch(train) [83][ 20/1879] lr: 2.0000e-04 eta: 3:28:28 time: 0.4635 data_time: 0.3212 memory: 6717 grad_norm: 3.2460 loss: 1.0532 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0532 2023/04/14 13:13:07 - mmengine - INFO - Epoch(train) [83][ 40/1879] lr: 2.0000e-04 eta: 3:28:20 time: 0.3201 data_time: 0.1494 memory: 6717 grad_norm: 3.3023 loss: 1.1587 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.1587 2023/04/14 13:13:15 - mmengine - INFO - Epoch(train) [83][ 60/1879] lr: 2.0000e-04 eta: 3:28:13 time: 0.4173 data_time: 0.1459 memory: 6717 grad_norm: 3.3559 loss: 1.0927 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0927 2023/04/14 13:13:22 - mmengine - INFO - Epoch(train) [83][ 80/1879] lr: 2.0000e-04 eta: 3:28:05 time: 0.3441 data_time: 0.1142 memory: 6717 grad_norm: 3.4702 loss: 1.2273 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2273 2023/04/14 13:13:30 - mmengine - INFO - Epoch(train) [83][ 100/1879] lr: 2.0000e-04 eta: 3:27:58 time: 0.4019 data_time: 0.1225 memory: 6717 grad_norm: 3.3767 loss: 1.0477 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0477 2023/04/14 13:13:36 - mmengine - INFO - Epoch(train) [83][ 120/1879] lr: 2.0000e-04 eta: 3:27:50 time: 0.3209 data_time: 0.1516 memory: 6717 grad_norm: 3.4085 loss: 1.0390 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0390 2023/04/14 13:13:45 - mmengine - INFO - Epoch(train) [83][ 140/1879] lr: 2.0000e-04 eta: 3:27:43 time: 0.4524 data_time: 0.2725 memory: 6717 grad_norm: 3.3800 loss: 1.0405 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0405 2023/04/14 13:13:51 - mmengine - INFO - Epoch(train) [83][ 160/1879] lr: 2.0000e-04 eta: 3:27:36 time: 0.2967 data_time: 0.1249 memory: 6717 grad_norm: 3.3066 loss: 1.0620 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0620 2023/04/14 13:14:00 - mmengine - INFO - Epoch(train) [83][ 180/1879] lr: 2.0000e-04 eta: 3:27:28 time: 0.4260 data_time: 0.2749 memory: 6717 grad_norm: 3.4548 loss: 1.0875 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0875 2023/04/14 13:14:07 - mmengine - INFO - Epoch(train) [83][ 200/1879] lr: 2.0000e-04 eta: 3:27:21 time: 0.3310 data_time: 0.1909 memory: 6717 grad_norm: 3.4810 loss: 1.2578 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2578 2023/04/14 13:14:15 - mmengine - INFO - Epoch(train) [83][ 220/1879] lr: 2.0000e-04 eta: 3:27:14 time: 0.4061 data_time: 0.2631 memory: 6717 grad_norm: 3.3689 loss: 1.2132 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2132 2023/04/14 13:14:21 - mmengine - INFO - Epoch(train) [83][ 240/1879] lr: 2.0000e-04 eta: 3:27:06 time: 0.3163 data_time: 0.1711 memory: 6717 grad_norm: 3.3626 loss: 1.1119 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1119 2023/04/14 13:14:29 - mmengine - INFO - Epoch(train) [83][ 260/1879] lr: 2.0000e-04 eta: 3:26:59 time: 0.3907 data_time: 0.1726 memory: 6717 grad_norm: 3.4405 loss: 1.1897 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1897 2023/04/14 13:14:36 - mmengine - INFO - Epoch(train) [83][ 280/1879] lr: 2.0000e-04 eta: 3:26:51 time: 0.3349 data_time: 0.1438 memory: 6717 grad_norm: 3.3942 loss: 1.3748 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3748 2023/04/14 13:14:44 - mmengine - INFO - Epoch(train) [83][ 300/1879] lr: 2.0000e-04 eta: 3:26:44 time: 0.4359 data_time: 0.2821 memory: 6717 grad_norm: 3.3596 loss: 0.9611 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9611 2023/04/14 13:14:51 - mmengine - INFO - Epoch(train) [83][ 320/1879] lr: 2.0000e-04 eta: 3:26:36 time: 0.3351 data_time: 0.1944 memory: 6717 grad_norm: 3.4051 loss: 1.0572 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0572 2023/04/14 13:14:59 - mmengine - INFO - Epoch(train) [83][ 340/1879] lr: 2.0000e-04 eta: 3:26:29 time: 0.3986 data_time: 0.2547 memory: 6717 grad_norm: 3.4539 loss: 0.9608 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9608 2023/04/14 13:15:06 - mmengine - INFO - Epoch(train) [83][ 360/1879] lr: 2.0000e-04 eta: 3:26:22 time: 0.3426 data_time: 0.1985 memory: 6717 grad_norm: 3.3674 loss: 0.9308 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9308 2023/04/14 13:15:14 - mmengine - INFO - Epoch(train) [83][ 380/1879] lr: 2.0000e-04 eta: 3:26:14 time: 0.4173 data_time: 0.2725 memory: 6717 grad_norm: 3.3851 loss: 1.0654 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0654 2023/04/14 13:15:20 - mmengine - INFO - Epoch(train) [83][ 400/1879] lr: 2.0000e-04 eta: 3:26:07 time: 0.3087 data_time: 0.1462 memory: 6717 grad_norm: 3.3594 loss: 1.1440 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1440 2023/04/14 13:15:28 - mmengine - INFO - Epoch(train) [83][ 420/1879] lr: 2.0000e-04 eta: 3:26:00 time: 0.4073 data_time: 0.2321 memory: 6717 grad_norm: 3.3974 loss: 1.0927 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0927 2023/04/14 13:15:35 - mmengine - INFO - Epoch(train) [83][ 440/1879] lr: 2.0000e-04 eta: 3:25:52 time: 0.3382 data_time: 0.1299 memory: 6717 grad_norm: 3.3920 loss: 1.0722 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.0722 2023/04/14 13:15:43 - mmengine - INFO - Epoch(train) [83][ 460/1879] lr: 2.0000e-04 eta: 3:25:45 time: 0.4049 data_time: 0.0835 memory: 6717 grad_norm: 3.3813 loss: 1.1034 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1034 2023/04/14 13:15:50 - mmengine - INFO - Epoch(train) [83][ 480/1879] lr: 2.0000e-04 eta: 3:25:37 time: 0.3085 data_time: 0.1447 memory: 6717 grad_norm: 3.3373 loss: 1.0604 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0604 2023/04/14 13:15:58 - mmengine - INFO - Epoch(train) [83][ 500/1879] lr: 2.0000e-04 eta: 3:25:30 time: 0.4184 data_time: 0.2667 memory: 6717 grad_norm: 3.4589 loss: 1.1918 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1918 2023/04/14 13:16:05 - mmengine - INFO - Epoch(train) [83][ 520/1879] lr: 2.0000e-04 eta: 3:25:22 time: 0.3310 data_time: 0.1643 memory: 6717 grad_norm: 3.4068 loss: 0.9496 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9496 2023/04/14 13:16:13 - mmengine - INFO - Epoch(train) [83][ 540/1879] lr: 2.0000e-04 eta: 3:25:15 time: 0.4049 data_time: 0.2144 memory: 6717 grad_norm: 3.4659 loss: 1.1864 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1864 2023/04/14 13:16:20 - mmengine - INFO - Epoch(train) [83][ 560/1879] lr: 2.0000e-04 eta: 3:25:08 time: 0.3561 data_time: 0.1394 memory: 6717 grad_norm: 3.3905 loss: 1.3011 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3011 2023/04/14 13:16:28 - mmengine - INFO - Epoch(train) [83][ 580/1879] lr: 2.0000e-04 eta: 3:25:00 time: 0.3945 data_time: 0.2151 memory: 6717 grad_norm: 3.4846 loss: 1.1876 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1876 2023/04/14 13:16:34 - mmengine - INFO - Epoch(train) [83][ 600/1879] lr: 2.0000e-04 eta: 3:24:53 time: 0.3362 data_time: 0.1693 memory: 6717 grad_norm: 3.3461 loss: 1.2998 top1_acc: 0.3125 top5_acc: 0.7500 loss_cls: 1.2998 2023/04/14 13:16:42 - mmengine - INFO - Epoch(train) [83][ 620/1879] lr: 2.0000e-04 eta: 3:24:45 time: 0.3652 data_time: 0.1670 memory: 6717 grad_norm: 3.4719 loss: 1.0933 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0933 2023/04/14 13:16:49 - mmengine - INFO - Epoch(train) [83][ 640/1879] lr: 2.0000e-04 eta: 3:24:38 time: 0.3423 data_time: 0.0829 memory: 6717 grad_norm: 3.3947 loss: 1.1027 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1027 2023/04/14 13:16:56 - mmengine - INFO - Epoch(train) [83][ 660/1879] lr: 2.0000e-04 eta: 3:24:30 time: 0.3620 data_time: 0.1078 memory: 6717 grad_norm: 3.4700 loss: 1.1799 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1799 2023/04/14 13:17:03 - mmengine - INFO - Epoch(train) [83][ 680/1879] lr: 2.0000e-04 eta: 3:24:23 time: 0.3669 data_time: 0.0774 memory: 6717 grad_norm: 3.4793 loss: 1.2252 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2252 2023/04/14 13:17:11 - mmengine - INFO - Epoch(train) [83][ 700/1879] lr: 2.0000e-04 eta: 3:24:16 time: 0.3927 data_time: 0.1741 memory: 6717 grad_norm: 3.4045 loss: 1.1113 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1113 2023/04/14 13:17:18 - mmengine - INFO - Epoch(train) [83][ 720/1879] lr: 2.0000e-04 eta: 3:24:08 time: 0.3403 data_time: 0.1331 memory: 6717 grad_norm: 3.3957 loss: 0.9854 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9854 2023/04/14 13:17:26 - mmengine - INFO - Epoch(train) [83][ 740/1879] lr: 2.0000e-04 eta: 3:24:01 time: 0.4216 data_time: 0.1497 memory: 6717 grad_norm: 3.3334 loss: 1.0579 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0579 2023/04/14 13:17:33 - mmengine - INFO - Epoch(train) [83][ 760/1879] lr: 2.0000e-04 eta: 3:23:53 time: 0.3188 data_time: 0.1257 memory: 6717 grad_norm: 3.4014 loss: 1.1881 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.1881 2023/04/14 13:17:41 - mmengine - INFO - Epoch(train) [83][ 780/1879] lr: 2.0000e-04 eta: 3:23:46 time: 0.4220 data_time: 0.1915 memory: 6717 grad_norm: 3.3430 loss: 0.9853 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9853 2023/04/14 13:17:47 - mmengine - INFO - Epoch(train) [83][ 800/1879] lr: 2.0000e-04 eta: 3:23:39 time: 0.3008 data_time: 0.0832 memory: 6717 grad_norm: 3.4462 loss: 1.1112 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1112 2023/04/14 13:17:55 - mmengine - INFO - Epoch(train) [83][ 820/1879] lr: 2.0000e-04 eta: 3:23:31 time: 0.4151 data_time: 0.2634 memory: 6717 grad_norm: 3.3527 loss: 1.1894 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1894 2023/04/14 13:18:02 - mmengine - INFO - Epoch(train) [83][ 840/1879] lr: 2.0000e-04 eta: 3:23:24 time: 0.3399 data_time: 0.1709 memory: 6717 grad_norm: 3.4726 loss: 1.1849 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1849 2023/04/14 13:18:10 - mmengine - INFO - Epoch(train) [83][ 860/1879] lr: 2.0000e-04 eta: 3:23:16 time: 0.4001 data_time: 0.2106 memory: 6717 grad_norm: 3.3475 loss: 1.1324 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1324 2023/04/14 13:18:17 - mmengine - INFO - Epoch(train) [83][ 880/1879] lr: 2.0000e-04 eta: 3:23:09 time: 0.3476 data_time: 0.1874 memory: 6717 grad_norm: 3.3632 loss: 1.1222 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1222 2023/04/14 13:18:25 - mmengine - INFO - Epoch(train) [83][ 900/1879] lr: 2.0000e-04 eta: 3:23:02 time: 0.3999 data_time: 0.2493 memory: 6717 grad_norm: 3.3687 loss: 1.1221 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1221 2023/04/14 13:18:32 - mmengine - INFO - Epoch(train) [83][ 920/1879] lr: 2.0000e-04 eta: 3:22:54 time: 0.3518 data_time: 0.2045 memory: 6717 grad_norm: 3.3544 loss: 1.0348 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0348 2023/04/14 13:18:33 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 13:18:40 - mmengine - INFO - Epoch(train) [83][ 940/1879] lr: 2.0000e-04 eta: 3:22:47 time: 0.3872 data_time: 0.2185 memory: 6717 grad_norm: 3.3665 loss: 1.0757 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0757 2023/04/14 13:18:46 - mmengine - INFO - Epoch(train) [83][ 960/1879] lr: 2.0000e-04 eta: 3:22:39 time: 0.3264 data_time: 0.1424 memory: 6717 grad_norm: 3.3701 loss: 1.1799 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1799 2023/04/14 13:18:55 - mmengine - INFO - Epoch(train) [83][ 980/1879] lr: 2.0000e-04 eta: 3:22:32 time: 0.4040 data_time: 0.2611 memory: 6717 grad_norm: 3.3882 loss: 1.1816 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1816 2023/04/14 13:19:02 - mmengine - INFO - Epoch(train) [83][1000/1879] lr: 2.0000e-04 eta: 3:22:25 time: 0.3469 data_time: 0.2057 memory: 6717 grad_norm: 3.3723 loss: 1.0735 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0735 2023/04/14 13:19:10 - mmengine - INFO - Epoch(train) [83][1020/1879] lr: 2.0000e-04 eta: 3:22:17 time: 0.4122 data_time: 0.2720 memory: 6717 grad_norm: 3.4626 loss: 1.0926 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0926 2023/04/14 13:19:16 - mmengine - INFO - Epoch(train) [83][1040/1879] lr: 2.0000e-04 eta: 3:22:10 time: 0.3095 data_time: 0.1711 memory: 6717 grad_norm: 3.4791 loss: 1.0555 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0555 2023/04/14 13:19:25 - mmengine - INFO - Epoch(train) [83][1060/1879] lr: 2.0000e-04 eta: 3:22:03 time: 0.4362 data_time: 0.2948 memory: 6717 grad_norm: 3.3214 loss: 1.0747 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0747 2023/04/14 13:19:31 - mmengine - INFO - Epoch(train) [83][1080/1879] lr: 2.0000e-04 eta: 3:21:55 time: 0.3177 data_time: 0.1777 memory: 6717 grad_norm: 3.2797 loss: 1.0458 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0458 2023/04/14 13:19:39 - mmengine - INFO - Epoch(train) [83][1100/1879] lr: 2.0000e-04 eta: 3:21:48 time: 0.3920 data_time: 0.2522 memory: 6717 grad_norm: 3.4064 loss: 0.9803 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9803 2023/04/14 13:19:45 - mmengine - INFO - Epoch(train) [83][1120/1879] lr: 2.0000e-04 eta: 3:21:40 time: 0.3207 data_time: 0.1817 memory: 6717 grad_norm: 3.4005 loss: 0.9428 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9428 2023/04/14 13:19:54 - mmengine - INFO - Epoch(train) [83][1140/1879] lr: 2.0000e-04 eta: 3:21:33 time: 0.4115 data_time: 0.2740 memory: 6717 grad_norm: 3.3786 loss: 1.0316 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0316 2023/04/14 13:20:00 - mmengine - INFO - Epoch(train) [83][1160/1879] lr: 2.0000e-04 eta: 3:21:25 time: 0.3362 data_time: 0.1953 memory: 6717 grad_norm: 3.4420 loss: 1.0817 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0817 2023/04/14 13:20:09 - mmengine - INFO - Epoch(train) [83][1180/1879] lr: 2.0000e-04 eta: 3:21:18 time: 0.4466 data_time: 0.3067 memory: 6717 grad_norm: 3.4920 loss: 1.2226 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2226 2023/04/14 13:20:16 - mmengine - INFO - Epoch(train) [83][1200/1879] lr: 2.0000e-04 eta: 3:21:11 time: 0.3152 data_time: 0.1726 memory: 6717 grad_norm: 3.3967 loss: 1.1062 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1062 2023/04/14 13:20:23 - mmengine - INFO - Epoch(train) [83][1220/1879] lr: 2.0000e-04 eta: 3:21:03 time: 0.3938 data_time: 0.2532 memory: 6717 grad_norm: 3.3450 loss: 1.0447 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0447 2023/04/14 13:20:30 - mmengine - INFO - Epoch(train) [83][1240/1879] lr: 2.0000e-04 eta: 3:20:56 time: 0.3262 data_time: 0.1864 memory: 6717 grad_norm: 3.3662 loss: 1.0326 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0326 2023/04/14 13:20:38 - mmengine - INFO - Epoch(train) [83][1260/1879] lr: 2.0000e-04 eta: 3:20:48 time: 0.4215 data_time: 0.2761 memory: 6717 grad_norm: 3.3627 loss: 1.0374 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0374 2023/04/14 13:20:45 - mmengine - INFO - Epoch(train) [83][1280/1879] lr: 2.0000e-04 eta: 3:20:41 time: 0.3213 data_time: 0.1739 memory: 6717 grad_norm: 3.4332 loss: 1.3455 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3455 2023/04/14 13:20:53 - mmengine - INFO - Epoch(train) [83][1300/1879] lr: 2.0000e-04 eta: 3:20:34 time: 0.3851 data_time: 0.2135 memory: 6717 grad_norm: 3.3283 loss: 1.0631 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0631 2023/04/14 13:20:59 - mmengine - INFO - Epoch(train) [83][1320/1879] lr: 2.0000e-04 eta: 3:20:26 time: 0.3253 data_time: 0.1444 memory: 6717 grad_norm: 3.3731 loss: 1.1688 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1688 2023/04/14 13:21:07 - mmengine - INFO - Epoch(train) [83][1340/1879] lr: 2.0000e-04 eta: 3:20:19 time: 0.4096 data_time: 0.2677 memory: 6717 grad_norm: 3.3379 loss: 1.1719 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1719 2023/04/14 13:21:14 - mmengine - INFO - Epoch(train) [83][1360/1879] lr: 2.0000e-04 eta: 3:20:11 time: 0.3557 data_time: 0.2114 memory: 6717 grad_norm: 3.4301 loss: 1.0887 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0887 2023/04/14 13:21:22 - mmengine - INFO - Epoch(train) [83][1380/1879] lr: 2.0000e-04 eta: 3:20:04 time: 0.3828 data_time: 0.2402 memory: 6717 grad_norm: 3.3947 loss: 1.0321 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0321 2023/04/14 13:21:29 - mmengine - INFO - Epoch(train) [83][1400/1879] lr: 2.0000e-04 eta: 3:19:56 time: 0.3288 data_time: 0.1880 memory: 6717 grad_norm: 3.4079 loss: 1.0557 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0557 2023/04/14 13:21:37 - mmengine - INFO - Epoch(train) [83][1420/1879] lr: 2.0000e-04 eta: 3:19:49 time: 0.4211 data_time: 0.2198 memory: 6717 grad_norm: 3.3607 loss: 1.0926 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0926 2023/04/14 13:21:43 - mmengine - INFO - Epoch(train) [83][1440/1879] lr: 2.0000e-04 eta: 3:19:41 time: 0.2959 data_time: 0.1259 memory: 6717 grad_norm: 3.4436 loss: 1.1778 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1778 2023/04/14 13:21:50 - mmengine - INFO - Epoch(train) [83][1460/1879] lr: 2.0000e-04 eta: 3:19:34 time: 0.3746 data_time: 0.2346 memory: 6717 grad_norm: 3.5328 loss: 1.1938 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1938 2023/04/14 13:21:57 - mmengine - INFO - Epoch(train) [83][1480/1879] lr: 2.0000e-04 eta: 3:19:26 time: 0.3240 data_time: 0.1731 memory: 6717 grad_norm: 3.3360 loss: 1.0273 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0273 2023/04/14 13:22:06 - mmengine - INFO - Epoch(train) [83][1500/1879] lr: 2.0000e-04 eta: 3:19:19 time: 0.4422 data_time: 0.2245 memory: 6717 grad_norm: 3.3606 loss: 1.0567 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0567 2023/04/14 13:22:12 - mmengine - INFO - Epoch(train) [83][1520/1879] lr: 2.0000e-04 eta: 3:19:12 time: 0.3113 data_time: 0.0675 memory: 6717 grad_norm: 3.3257 loss: 0.9522 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9522 2023/04/14 13:22:21 - mmengine - INFO - Epoch(train) [83][1540/1879] lr: 2.0000e-04 eta: 3:19:05 time: 0.4315 data_time: 0.0496 memory: 6717 grad_norm: 3.3857 loss: 0.9787 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.9787 2023/04/14 13:22:27 - mmengine - INFO - Epoch(train) [83][1560/1879] lr: 2.0000e-04 eta: 3:18:57 time: 0.3282 data_time: 0.0134 memory: 6717 grad_norm: 3.3997 loss: 1.0446 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0446 2023/04/14 13:22:36 - mmengine - INFO - Epoch(train) [83][1580/1879] lr: 2.0000e-04 eta: 3:18:50 time: 0.4333 data_time: 0.0521 memory: 6717 grad_norm: 3.4554 loss: 1.1826 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1826 2023/04/14 13:22:42 - mmengine - INFO - Epoch(train) [83][1600/1879] lr: 2.0000e-04 eta: 3:18:42 time: 0.3208 data_time: 0.0122 memory: 6717 grad_norm: 3.3394 loss: 1.0715 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0715 2023/04/14 13:22:50 - mmengine - INFO - Epoch(train) [83][1620/1879] lr: 2.0000e-04 eta: 3:18:35 time: 0.3948 data_time: 0.0157 memory: 6717 grad_norm: 3.4073 loss: 1.1268 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1268 2023/04/14 13:22:57 - mmengine - INFO - Epoch(train) [83][1640/1879] lr: 2.0000e-04 eta: 3:18:27 time: 0.3300 data_time: 0.0139 memory: 6717 grad_norm: 3.4165 loss: 1.0335 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0335 2023/04/14 13:23:05 - mmengine - INFO - Epoch(train) [83][1660/1879] lr: 2.0000e-04 eta: 3:18:20 time: 0.3986 data_time: 0.0164 memory: 6717 grad_norm: 3.3308 loss: 1.1566 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1566 2023/04/14 13:23:11 - mmengine - INFO - Epoch(train) [83][1680/1879] lr: 2.0000e-04 eta: 3:18:13 time: 0.3217 data_time: 0.0129 memory: 6717 grad_norm: 3.4771 loss: 1.1471 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1471 2023/04/14 13:23:19 - mmengine - INFO - Epoch(train) [83][1700/1879] lr: 2.0000e-04 eta: 3:18:05 time: 0.3695 data_time: 0.0160 memory: 6717 grad_norm: 3.4394 loss: 1.1562 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.1562 2023/04/14 13:23:25 - mmengine - INFO - Epoch(train) [83][1720/1879] lr: 2.0000e-04 eta: 3:17:58 time: 0.3362 data_time: 0.0144 memory: 6717 grad_norm: 3.4058 loss: 1.0522 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0522 2023/04/14 13:23:33 - mmengine - INFO - Epoch(train) [83][1740/1879] lr: 2.0000e-04 eta: 3:17:50 time: 0.3902 data_time: 0.0156 memory: 6717 grad_norm: 3.3911 loss: 1.0980 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0980 2023/04/14 13:23:40 - mmengine - INFO - Epoch(train) [83][1760/1879] lr: 2.0000e-04 eta: 3:17:43 time: 0.3595 data_time: 0.0146 memory: 6717 grad_norm: 3.3891 loss: 1.1631 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1631 2023/04/14 13:23:49 - mmengine - INFO - Epoch(train) [83][1780/1879] lr: 2.0000e-04 eta: 3:17:36 time: 0.4271 data_time: 0.0151 memory: 6717 grad_norm: 3.4727 loss: 1.2341 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2341 2023/04/14 13:23:56 - mmengine - INFO - Epoch(train) [83][1800/1879] lr: 2.0000e-04 eta: 3:17:28 time: 0.3460 data_time: 0.0152 memory: 6717 grad_norm: 3.3876 loss: 1.0744 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0744 2023/04/14 13:24:04 - mmengine - INFO - Epoch(train) [83][1820/1879] lr: 2.0000e-04 eta: 3:17:21 time: 0.4001 data_time: 0.0255 memory: 6717 grad_norm: 3.3437 loss: 1.1468 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1468 2023/04/14 13:24:11 - mmengine - INFO - Epoch(train) [83][1840/1879] lr: 2.0000e-04 eta: 3:17:13 time: 0.3405 data_time: 0.0158 memory: 6717 grad_norm: 3.4814 loss: 1.2715 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2715 2023/04/14 13:24:18 - mmengine - INFO - Epoch(train) [83][1860/1879] lr: 2.0000e-04 eta: 3:17:06 time: 0.3846 data_time: 0.0285 memory: 6717 grad_norm: 3.4540 loss: 1.1894 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1894 2023/04/14 13:24:25 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 13:24:25 - mmengine - INFO - Epoch(train) [83][1879/1879] lr: 2.0000e-04 eta: 3:16:59 time: 0.3780 data_time: 0.0545 memory: 6717 grad_norm: 3.4768 loss: 1.0007 top1_acc: 0.5714 top5_acc: 1.0000 loss_cls: 1.0007 2023/04/14 13:24:34 - mmengine - INFO - Epoch(val) [83][ 20/155] eta: 0:01:02 time: 0.4617 data_time: 0.4287 memory: 1391 2023/04/14 13:24:40 - mmengine - INFO - Epoch(val) [83][ 40/155] eta: 0:00:44 time: 0.3197 data_time: 0.2867 memory: 1391 2023/04/14 13:24:49 - mmengine - INFO - Epoch(val) [83][ 60/155] eta: 0:00:38 time: 0.4353 data_time: 0.4020 memory: 1391 2023/04/14 13:24:55 - mmengine - INFO - Epoch(val) [83][ 80/155] eta: 0:00:28 time: 0.3155 data_time: 0.2821 memory: 1391 2023/04/14 13:25:04 - mmengine - INFO - Epoch(val) [83][100/155] eta: 0:00:21 time: 0.4591 data_time: 0.4256 memory: 1391 2023/04/14 13:25:10 - mmengine - INFO - Epoch(val) [83][120/155] eta: 0:00:13 time: 0.2969 data_time: 0.2644 memory: 1391 2023/04/14 13:25:19 - mmengine - INFO - Epoch(val) [83][140/155] eta: 0:00:05 time: 0.4430 data_time: 0.4097 memory: 1391 2023/04/14 13:25:26 - mmengine - INFO - Epoch(val) [83][155/155] acc/top1: 0.6693 acc/top5: 0.8742 acc/mean1: 0.6692 data_time: 0.3616 time: 0.3940 2023/04/14 13:25:26 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_82.pth is removed 2023/04/14 13:25:27 - mmengine - INFO - The best checkpoint with 0.6693 acc/top1 at 83 epoch is saved to best_acc_top1_epoch_83.pth. 2023/04/14 13:25:36 - mmengine - INFO - Epoch(train) [84][ 20/1879] lr: 2.0000e-04 eta: 3:16:52 time: 0.4523 data_time: 0.3104 memory: 6717 grad_norm: 3.4591 loss: 1.1450 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1450 2023/04/14 13:25:43 - mmengine - INFO - Epoch(train) [84][ 40/1879] lr: 2.0000e-04 eta: 3:16:44 time: 0.3368 data_time: 0.2035 memory: 6717 grad_norm: 3.4108 loss: 1.0831 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0831 2023/04/14 13:25:45 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 13:25:50 - mmengine - INFO - Epoch(train) [84][ 60/1879] lr: 2.0000e-04 eta: 3:16:37 time: 0.3876 data_time: 0.2284 memory: 6717 grad_norm: 3.4893 loss: 1.2266 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2266 2023/04/14 13:25:58 - mmengine - INFO - Epoch(train) [84][ 80/1879] lr: 2.0000e-04 eta: 3:16:29 time: 0.3707 data_time: 0.1638 memory: 6717 grad_norm: 3.4076 loss: 1.0980 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0980 2023/04/14 13:26:05 - mmengine - INFO - Epoch(train) [84][ 100/1879] lr: 2.0000e-04 eta: 3:16:22 time: 0.3718 data_time: 0.0947 memory: 6717 grad_norm: 3.3498 loss: 1.2157 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2157 2023/04/14 13:26:13 - mmengine - INFO - Epoch(train) [84][ 120/1879] lr: 2.0000e-04 eta: 3:16:15 time: 0.3644 data_time: 0.1036 memory: 6717 grad_norm: 3.4692 loss: 1.0026 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0026 2023/04/14 13:26:21 - mmengine - INFO - Epoch(train) [84][ 140/1879] lr: 2.0000e-04 eta: 3:16:08 time: 0.4261 data_time: 0.0153 memory: 6717 grad_norm: 3.2649 loss: 0.9652 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9652 2023/04/14 13:26:27 - mmengine - INFO - Epoch(train) [84][ 160/1879] lr: 2.0000e-04 eta: 3:16:00 time: 0.3089 data_time: 0.0144 memory: 6717 grad_norm: 3.4329 loss: 1.0390 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0390 2023/04/14 13:26:35 - mmengine - INFO - Epoch(train) [84][ 180/1879] lr: 2.0000e-04 eta: 3:15:53 time: 0.3829 data_time: 0.0162 memory: 6717 grad_norm: 3.3388 loss: 1.0248 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0248 2023/04/14 13:26:41 - mmengine - INFO - Epoch(train) [84][ 200/1879] lr: 2.0000e-04 eta: 3:15:45 time: 0.3229 data_time: 0.0148 memory: 6717 grad_norm: 3.4576 loss: 1.0634 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0634 2023/04/14 13:26:50 - mmengine - INFO - Epoch(train) [84][ 220/1879] lr: 2.0000e-04 eta: 3:15:38 time: 0.4442 data_time: 0.0154 memory: 6717 grad_norm: 3.4185 loss: 1.2496 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2496 2023/04/14 13:26:57 - mmengine - INFO - Epoch(train) [84][ 240/1879] lr: 2.0000e-04 eta: 3:15:30 time: 0.3342 data_time: 0.0136 memory: 6717 grad_norm: 3.3536 loss: 1.0409 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0409 2023/04/14 13:27:06 - mmengine - INFO - Epoch(train) [84][ 260/1879] lr: 2.0000e-04 eta: 3:15:23 time: 0.4292 data_time: 0.0154 memory: 6717 grad_norm: 3.4107 loss: 1.2341 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2341 2023/04/14 13:27:12 - mmengine - INFO - Epoch(train) [84][ 280/1879] lr: 2.0000e-04 eta: 3:15:15 time: 0.3165 data_time: 0.0142 memory: 6717 grad_norm: 3.4307 loss: 1.1974 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1974 2023/04/14 13:27:20 - mmengine - INFO - Epoch(train) [84][ 300/1879] lr: 2.0000e-04 eta: 3:15:08 time: 0.4232 data_time: 0.0263 memory: 6717 grad_norm: 3.3416 loss: 1.0462 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0462 2023/04/14 13:27:27 - mmengine - INFO - Epoch(train) [84][ 320/1879] lr: 2.0000e-04 eta: 3:15:01 time: 0.3057 data_time: 0.0194 memory: 6717 grad_norm: 3.3619 loss: 1.1857 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1857 2023/04/14 13:27:35 - mmengine - INFO - Epoch(train) [84][ 340/1879] lr: 2.0000e-04 eta: 3:14:53 time: 0.3975 data_time: 0.0908 memory: 6717 grad_norm: 3.3948 loss: 1.1978 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1978 2023/04/14 13:27:41 - mmengine - INFO - Epoch(train) [84][ 360/1879] lr: 2.0000e-04 eta: 3:14:46 time: 0.3194 data_time: 0.0823 memory: 6717 grad_norm: 3.4245 loss: 1.0337 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.0337 2023/04/14 13:27:49 - mmengine - INFO - Epoch(train) [84][ 380/1879] lr: 2.0000e-04 eta: 3:14:38 time: 0.3988 data_time: 0.2046 memory: 6717 grad_norm: 3.4300 loss: 1.0554 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0554 2023/04/14 13:27:56 - mmengine - INFO - Epoch(train) [84][ 400/1879] lr: 2.0000e-04 eta: 3:14:31 time: 0.3461 data_time: 0.1930 memory: 6717 grad_norm: 3.4630 loss: 1.3038 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3038 2023/04/14 13:28:04 - mmengine - INFO - Epoch(train) [84][ 420/1879] lr: 2.0000e-04 eta: 3:14:24 time: 0.4063 data_time: 0.2663 memory: 6717 grad_norm: 3.3693 loss: 1.0487 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0487 2023/04/14 13:28:10 - mmengine - INFO - Epoch(train) [84][ 440/1879] lr: 2.0000e-04 eta: 3:14:16 time: 0.3237 data_time: 0.1842 memory: 6717 grad_norm: 3.3954 loss: 1.1250 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1250 2023/04/14 13:28:19 - mmengine - INFO - Epoch(train) [84][ 460/1879] lr: 2.0000e-04 eta: 3:14:09 time: 0.4062 data_time: 0.2588 memory: 6717 grad_norm: 3.4513 loss: 1.2574 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2574 2023/04/14 13:28:25 - mmengine - INFO - Epoch(train) [84][ 480/1879] lr: 2.0000e-04 eta: 3:14:01 time: 0.3156 data_time: 0.1708 memory: 6717 grad_norm: 3.4737 loss: 1.2894 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2894 2023/04/14 13:28:33 - mmengine - INFO - Epoch(train) [84][ 500/1879] lr: 2.0000e-04 eta: 3:13:54 time: 0.4289 data_time: 0.2860 memory: 6717 grad_norm: 3.5099 loss: 1.2934 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2934 2023/04/14 13:28:40 - mmengine - INFO - Epoch(train) [84][ 520/1879] lr: 2.0000e-04 eta: 3:13:47 time: 0.3236 data_time: 0.1543 memory: 6717 grad_norm: 3.4254 loss: 1.1656 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1656 2023/04/14 13:28:48 - mmengine - INFO - Epoch(train) [84][ 540/1879] lr: 2.0000e-04 eta: 3:13:39 time: 0.4033 data_time: 0.2547 memory: 6717 grad_norm: 3.4367 loss: 1.1932 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1932 2023/04/14 13:28:55 - mmengine - INFO - Epoch(train) [84][ 560/1879] lr: 2.0000e-04 eta: 3:13:32 time: 0.3588 data_time: 0.1488 memory: 6717 grad_norm: 3.3820 loss: 1.1448 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1448 2023/04/14 13:29:03 - mmengine - INFO - Epoch(train) [84][ 580/1879] lr: 2.0000e-04 eta: 3:13:24 time: 0.3664 data_time: 0.0353 memory: 6717 grad_norm: 3.3994 loss: 1.0682 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0682 2023/04/14 13:29:10 - mmengine - INFO - Epoch(train) [84][ 600/1879] lr: 2.0000e-04 eta: 3:13:17 time: 0.3497 data_time: 0.0726 memory: 6717 grad_norm: 3.5077 loss: 1.2521 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2521 2023/04/14 13:29:18 - mmengine - INFO - Epoch(train) [84][ 620/1879] lr: 2.0000e-04 eta: 3:13:10 time: 0.4303 data_time: 0.0149 memory: 6717 grad_norm: 3.3924 loss: 1.0373 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.0373 2023/04/14 13:29:25 - mmengine - INFO - Epoch(train) [84][ 640/1879] lr: 2.0000e-04 eta: 3:13:02 time: 0.3410 data_time: 0.0133 memory: 6717 grad_norm: 3.4475 loss: 1.0011 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0011 2023/04/14 13:29:33 - mmengine - INFO - Epoch(train) [84][ 660/1879] lr: 2.0000e-04 eta: 3:12:55 time: 0.4060 data_time: 0.0140 memory: 6717 grad_norm: 3.4228 loss: 1.1006 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1006 2023/04/14 13:29:40 - mmengine - INFO - Epoch(train) [84][ 680/1879] lr: 2.0000e-04 eta: 3:12:47 time: 0.3515 data_time: 0.0161 memory: 6717 grad_norm: 3.4122 loss: 1.1939 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1939 2023/04/14 13:29:48 - mmengine - INFO - Epoch(train) [84][ 700/1879] lr: 2.0000e-04 eta: 3:12:40 time: 0.3998 data_time: 0.0139 memory: 6717 grad_norm: 3.2935 loss: 0.9360 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9360 2023/04/14 13:29:54 - mmengine - INFO - Epoch(train) [84][ 720/1879] lr: 2.0000e-04 eta: 3:12:33 time: 0.3033 data_time: 0.0151 memory: 6717 grad_norm: 3.3580 loss: 1.1051 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1051 2023/04/14 13:30:02 - mmengine - INFO - Epoch(train) [84][ 740/1879] lr: 2.0000e-04 eta: 3:12:25 time: 0.4105 data_time: 0.0134 memory: 6717 grad_norm: 3.4263 loss: 1.2527 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2527 2023/04/14 13:30:09 - mmengine - INFO - Epoch(train) [84][ 760/1879] lr: 2.0000e-04 eta: 3:12:18 time: 0.3280 data_time: 0.0155 memory: 6717 grad_norm: 3.4710 loss: 1.1263 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1263 2023/04/14 13:30:18 - mmengine - INFO - Epoch(train) [84][ 780/1879] lr: 2.0000e-04 eta: 3:12:11 time: 0.4462 data_time: 0.0151 memory: 6717 grad_norm: 3.3174 loss: 1.0099 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0099 2023/04/14 13:30:24 - mmengine - INFO - Epoch(train) [84][ 800/1879] lr: 2.0000e-04 eta: 3:12:03 time: 0.2902 data_time: 0.0140 memory: 6717 grad_norm: 3.4608 loss: 1.3113 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3113 2023/04/14 13:30:32 - mmengine - INFO - Epoch(train) [84][ 820/1879] lr: 2.0000e-04 eta: 3:11:56 time: 0.4150 data_time: 0.0153 memory: 6717 grad_norm: 3.3979 loss: 1.1184 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1184 2023/04/14 13:30:39 - mmengine - INFO - Epoch(train) [84][ 840/1879] lr: 2.0000e-04 eta: 3:11:48 time: 0.3507 data_time: 0.0138 memory: 6717 grad_norm: 3.3250 loss: 1.2177 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2177 2023/04/14 13:30:46 - mmengine - INFO - Epoch(train) [84][ 860/1879] lr: 2.0000e-04 eta: 3:11:41 time: 0.3630 data_time: 0.0143 memory: 6717 grad_norm: 3.3818 loss: 1.0157 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0157 2023/04/14 13:30:54 - mmengine - INFO - Epoch(train) [84][ 880/1879] lr: 2.0000e-04 eta: 3:11:33 time: 0.3896 data_time: 0.0139 memory: 6717 grad_norm: 3.4446 loss: 1.0015 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0015 2023/04/14 13:31:00 - mmengine - INFO - Epoch(train) [84][ 900/1879] lr: 2.0000e-04 eta: 3:11:26 time: 0.3174 data_time: 0.0146 memory: 6717 grad_norm: 3.4199 loss: 1.1290 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1290 2023/04/14 13:31:08 - mmengine - INFO - Epoch(train) [84][ 920/1879] lr: 2.0000e-04 eta: 3:11:19 time: 0.3844 data_time: 0.0136 memory: 6717 grad_norm: 3.4707 loss: 1.1504 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1504 2023/04/14 13:31:15 - mmengine - INFO - Epoch(train) [84][ 940/1879] lr: 2.0000e-04 eta: 3:11:11 time: 0.3382 data_time: 0.0159 memory: 6717 grad_norm: 3.4076 loss: 1.1900 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1900 2023/04/14 13:31:23 - mmengine - INFO - Epoch(train) [84][ 960/1879] lr: 2.0000e-04 eta: 3:11:04 time: 0.3879 data_time: 0.0124 memory: 6717 grad_norm: 3.4230 loss: 1.2251 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2251 2023/04/14 13:31:30 - mmengine - INFO - Epoch(train) [84][ 980/1879] lr: 2.0000e-04 eta: 3:10:56 time: 0.3542 data_time: 0.1024 memory: 6717 grad_norm: 3.3373 loss: 1.0212 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0212 2023/04/14 13:31:37 - mmengine - INFO - Epoch(train) [84][1000/1879] lr: 2.0000e-04 eta: 3:10:49 time: 0.3702 data_time: 0.0310 memory: 6717 grad_norm: 3.4152 loss: 1.0586 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0586 2023/04/14 13:31:45 - mmengine - INFO - Epoch(train) [84][1020/1879] lr: 2.0000e-04 eta: 3:10:41 time: 0.3832 data_time: 0.0182 memory: 6717 grad_norm: 3.4144 loss: 1.1065 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1065 2023/04/14 13:31:52 - mmengine - INFO - Epoch(train) [84][1040/1879] lr: 2.0000e-04 eta: 3:10:34 time: 0.3607 data_time: 0.0137 memory: 6717 grad_norm: 3.4031 loss: 1.1348 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1348 2023/04/14 13:31:53 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 13:32:00 - mmengine - INFO - Epoch(train) [84][1060/1879] lr: 2.0000e-04 eta: 3:10:27 time: 0.3919 data_time: 0.0145 memory: 6717 grad_norm: 3.3438 loss: 0.9530 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9530 2023/04/14 13:32:06 - mmengine - INFO - Epoch(train) [84][1080/1879] lr: 2.0000e-04 eta: 3:10:19 time: 0.3204 data_time: 0.0134 memory: 6717 grad_norm: 3.3666 loss: 1.0960 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0960 2023/04/14 13:32:14 - mmengine - INFO - Epoch(train) [84][1100/1879] lr: 2.0000e-04 eta: 3:10:12 time: 0.3955 data_time: 0.0148 memory: 6717 grad_norm: 3.3500 loss: 1.1671 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1671 2023/04/14 13:32:21 - mmengine - INFO - Epoch(train) [84][1120/1879] lr: 2.0000e-04 eta: 3:10:04 time: 0.3292 data_time: 0.0147 memory: 6717 grad_norm: 3.3758 loss: 1.0513 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0513 2023/04/14 13:32:29 - mmengine - INFO - Epoch(train) [84][1140/1879] lr: 2.0000e-04 eta: 3:09:57 time: 0.4000 data_time: 0.0138 memory: 6717 grad_norm: 3.4584 loss: 1.2114 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2114 2023/04/14 13:32:36 - mmengine - INFO - Epoch(train) [84][1160/1879] lr: 2.0000e-04 eta: 3:09:49 time: 0.3479 data_time: 0.0148 memory: 6717 grad_norm: 3.3722 loss: 1.0974 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0974 2023/04/14 13:32:43 - mmengine - INFO - Epoch(train) [84][1180/1879] lr: 2.0000e-04 eta: 3:09:42 time: 0.3695 data_time: 0.0137 memory: 6717 grad_norm: 3.3236 loss: 1.1254 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1254 2023/04/14 13:32:51 - mmengine - INFO - Epoch(train) [84][1200/1879] lr: 2.0000e-04 eta: 3:09:35 time: 0.3955 data_time: 0.0149 memory: 6717 grad_norm: 3.3492 loss: 0.9561 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9561 2023/04/14 13:32:58 - mmengine - INFO - Epoch(train) [84][1220/1879] lr: 2.0000e-04 eta: 3:09:27 time: 0.3336 data_time: 0.0140 memory: 6717 grad_norm: 3.4057 loss: 1.1040 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1040 2023/04/14 13:33:06 - mmengine - INFO - Epoch(train) [84][1240/1879] lr: 2.0000e-04 eta: 3:09:20 time: 0.4244 data_time: 0.0146 memory: 6717 grad_norm: 3.3685 loss: 1.0981 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0981 2023/04/14 13:33:13 - mmengine - INFO - Epoch(train) [84][1260/1879] lr: 2.0000e-04 eta: 3:09:12 time: 0.3318 data_time: 0.0156 memory: 6717 grad_norm: 3.2932 loss: 1.1266 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1266 2023/04/14 13:33:21 - mmengine - INFO - Epoch(train) [84][1280/1879] lr: 2.0000e-04 eta: 3:09:05 time: 0.3838 data_time: 0.0143 memory: 6717 grad_norm: 3.4176 loss: 1.2231 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2231 2023/04/14 13:33:27 - mmengine - INFO - Epoch(train) [84][1300/1879] lr: 2.0000e-04 eta: 3:08:58 time: 0.3163 data_time: 0.0150 memory: 6717 grad_norm: 3.3823 loss: 0.9615 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9615 2023/04/14 13:33:35 - mmengine - INFO - Epoch(train) [84][1320/1879] lr: 2.0000e-04 eta: 3:08:50 time: 0.4141 data_time: 0.0160 memory: 6717 grad_norm: 3.3935 loss: 1.0061 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0061 2023/04/14 13:33:42 - mmengine - INFO - Epoch(train) [84][1340/1879] lr: 2.0000e-04 eta: 3:08:43 time: 0.3336 data_time: 0.0145 memory: 6717 grad_norm: 3.2730 loss: 1.0680 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.0680 2023/04/14 13:33:50 - mmengine - INFO - Epoch(train) [84][1360/1879] lr: 2.0000e-04 eta: 3:08:36 time: 0.4043 data_time: 0.0235 memory: 6717 grad_norm: 3.4058 loss: 1.0108 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0108 2023/04/14 13:33:56 - mmengine - INFO - Epoch(train) [84][1380/1879] lr: 2.0000e-04 eta: 3:08:28 time: 0.3117 data_time: 0.0415 memory: 6717 grad_norm: 3.4006 loss: 1.3576 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3576 2023/04/14 13:34:04 - mmengine - INFO - Epoch(train) [84][1400/1879] lr: 2.0000e-04 eta: 3:08:21 time: 0.3773 data_time: 0.0561 memory: 6717 grad_norm: 3.4068 loss: 1.2258 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2258 2023/04/14 13:34:10 - mmengine - INFO - Epoch(train) [84][1420/1879] lr: 2.0000e-04 eta: 3:08:13 time: 0.3327 data_time: 0.0642 memory: 6717 grad_norm: 3.3898 loss: 1.1560 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1560 2023/04/14 13:34:19 - mmengine - INFO - Epoch(train) [84][1440/1879] lr: 2.0000e-04 eta: 3:08:06 time: 0.4052 data_time: 0.0438 memory: 6717 grad_norm: 3.4121 loss: 1.0124 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0124 2023/04/14 13:34:26 - mmengine - INFO - Epoch(train) [84][1460/1879] lr: 2.0000e-04 eta: 3:07:58 time: 0.3582 data_time: 0.0139 memory: 6717 grad_norm: 3.3917 loss: 1.0546 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.0546 2023/04/14 13:34:34 - mmengine - INFO - Epoch(train) [84][1480/1879] lr: 2.0000e-04 eta: 3:07:51 time: 0.3959 data_time: 0.0210 memory: 6717 grad_norm: 3.5547 loss: 1.1724 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1724 2023/04/14 13:34:41 - mmengine - INFO - Epoch(train) [84][1500/1879] lr: 2.0000e-04 eta: 3:07:44 time: 0.3858 data_time: 0.0131 memory: 6717 grad_norm: 3.4426 loss: 1.0975 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0975 2023/04/14 13:34:49 - mmengine - INFO - Epoch(train) [84][1520/1879] lr: 2.0000e-04 eta: 3:07:36 time: 0.3743 data_time: 0.0153 memory: 6717 grad_norm: 3.5158 loss: 1.0716 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0716 2023/04/14 13:34:56 - mmengine - INFO - Epoch(train) [84][1540/1879] lr: 2.0000e-04 eta: 3:07:29 time: 0.3336 data_time: 0.0142 memory: 6717 grad_norm: 3.3588 loss: 0.9662 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9662 2023/04/14 13:35:04 - mmengine - INFO - Epoch(train) [84][1560/1879] lr: 2.0000e-04 eta: 3:07:21 time: 0.4006 data_time: 0.0144 memory: 6717 grad_norm: 3.3229 loss: 1.1116 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1116 2023/04/14 13:35:10 - mmengine - INFO - Epoch(train) [84][1580/1879] lr: 2.0000e-04 eta: 3:07:14 time: 0.3354 data_time: 0.0151 memory: 6717 grad_norm: 3.4686 loss: 1.1102 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1102 2023/04/14 13:35:18 - mmengine - INFO - Epoch(train) [84][1600/1879] lr: 2.0000e-04 eta: 3:07:07 time: 0.3905 data_time: 0.0145 memory: 6717 grad_norm: 3.3813 loss: 1.1622 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1622 2023/04/14 13:35:25 - mmengine - INFO - Epoch(train) [84][1620/1879] lr: 2.0000e-04 eta: 3:06:59 time: 0.3218 data_time: 0.0136 memory: 6717 grad_norm: 3.4496 loss: 1.2565 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2565 2023/04/14 13:35:33 - mmengine - INFO - Epoch(train) [84][1640/1879] lr: 2.0000e-04 eta: 3:06:52 time: 0.4166 data_time: 0.1160 memory: 6717 grad_norm: 3.4259 loss: 1.1370 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1370 2023/04/14 13:35:40 - mmengine - INFO - Epoch(train) [84][1660/1879] lr: 2.0000e-04 eta: 3:06:44 time: 0.3301 data_time: 0.0973 memory: 6717 grad_norm: 3.3212 loss: 1.0223 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0223 2023/04/14 13:35:47 - mmengine - INFO - Epoch(train) [84][1680/1879] lr: 2.0000e-04 eta: 3:06:37 time: 0.3744 data_time: 0.1357 memory: 6717 grad_norm: 3.3049 loss: 1.0624 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0624 2023/04/14 13:35:55 - mmengine - INFO - Epoch(train) [84][1700/1879] lr: 2.0000e-04 eta: 3:06:29 time: 0.3777 data_time: 0.0765 memory: 6717 grad_norm: 3.4365 loss: 1.1468 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1468 2023/04/14 13:36:01 - mmengine - INFO - Epoch(train) [84][1720/1879] lr: 2.0000e-04 eta: 3:06:22 time: 0.3431 data_time: 0.0603 memory: 6717 grad_norm: 3.4828 loss: 1.0607 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.0607 2023/04/14 13:36:09 - mmengine - INFO - Epoch(train) [84][1740/1879] lr: 2.0000e-04 eta: 3:06:15 time: 0.3864 data_time: 0.0117 memory: 6717 grad_norm: 3.4495 loss: 1.0825 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0825 2023/04/14 13:36:16 - mmengine - INFO - Epoch(train) [84][1760/1879] lr: 2.0000e-04 eta: 3:06:07 time: 0.3522 data_time: 0.0149 memory: 6717 grad_norm: 3.3594 loss: 1.1957 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1957 2023/04/14 13:36:24 - mmengine - INFO - Epoch(train) [84][1780/1879] lr: 2.0000e-04 eta: 3:06:00 time: 0.3845 data_time: 0.0141 memory: 6717 grad_norm: 3.4032 loss: 1.0950 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0950 2023/04/14 13:36:31 - mmengine - INFO - Epoch(train) [84][1800/1879] lr: 2.0000e-04 eta: 3:05:52 time: 0.3324 data_time: 0.0144 memory: 6717 grad_norm: 3.4351 loss: 1.0971 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0971 2023/04/14 13:36:39 - mmengine - INFO - Epoch(train) [84][1820/1879] lr: 2.0000e-04 eta: 3:05:45 time: 0.4347 data_time: 0.0146 memory: 6717 grad_norm: 3.5068 loss: 1.2628 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2628 2023/04/14 13:36:46 - mmengine - INFO - Epoch(train) [84][1840/1879] lr: 2.0000e-04 eta: 3:05:37 time: 0.3137 data_time: 0.0146 memory: 6717 grad_norm: 3.3848 loss: 1.0425 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0425 2023/04/14 13:36:54 - mmengine - INFO - Epoch(train) [84][1860/1879] lr: 2.0000e-04 eta: 3:05:30 time: 0.4251 data_time: 0.0141 memory: 6717 grad_norm: 3.4046 loss: 1.2951 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2951 2023/04/14 13:37:00 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 13:37:00 - mmengine - INFO - Epoch(train) [84][1879/1879] lr: 2.0000e-04 eta: 3:05:23 time: 0.2881 data_time: 0.0123 memory: 6717 grad_norm: 3.4089 loss: 1.0578 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 1.0578 2023/04/14 13:37:00 - mmengine - INFO - Saving checkpoint at 84 epochs 2023/04/14 13:37:09 - mmengine - INFO - Epoch(val) [84][ 20/155] eta: 0:01:02 time: 0.4618 data_time: 0.4289 memory: 1391 2023/04/14 13:37:16 - mmengine - INFO - Epoch(val) [84][ 40/155] eta: 0:00:44 time: 0.3149 data_time: 0.2819 memory: 1391 2023/04/14 13:37:24 - mmengine - INFO - Epoch(val) [84][ 60/155] eta: 0:00:38 time: 0.4250 data_time: 0.3916 memory: 1391 2023/04/14 13:37:31 - mmengine - INFO - Epoch(val) [84][ 80/155] eta: 0:00:28 time: 0.3223 data_time: 0.2893 memory: 1391 2023/04/14 13:37:39 - mmengine - INFO - Epoch(val) [84][100/155] eta: 0:00:21 time: 0.4225 data_time: 0.3889 memory: 1391 2023/04/14 13:37:46 - mmengine - INFO - Epoch(val) [84][120/155] eta: 0:00:13 time: 0.3326 data_time: 0.2992 memory: 1391 2023/04/14 13:37:56 - mmengine - INFO - Epoch(val) [84][140/155] eta: 0:00:05 time: 0.4864 data_time: 0.4534 memory: 1391 2023/04/14 13:38:03 - mmengine - INFO - Epoch(val) [84][155/155] acc/top1: 0.6684 acc/top5: 0.8739 acc/mean1: 0.6684 data_time: 0.4162 time: 0.4478 2023/04/14 13:38:13 - mmengine - INFO - Epoch(train) [85][ 20/1879] lr: 2.0000e-04 eta: 3:05:16 time: 0.4901 data_time: 0.2146 memory: 6717 grad_norm: 3.4440 loss: 1.2433 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2433 2023/04/14 13:38:19 - mmengine - INFO - Epoch(train) [85][ 40/1879] lr: 2.0000e-04 eta: 3:05:09 time: 0.3462 data_time: 0.0584 memory: 6717 grad_norm: 3.3678 loss: 1.2298 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2298 2023/04/14 13:38:27 - mmengine - INFO - Epoch(train) [85][ 60/1879] lr: 2.0000e-04 eta: 3:05:01 time: 0.3895 data_time: 0.0187 memory: 6717 grad_norm: 3.4691 loss: 1.1982 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1982 2023/04/14 13:38:34 - mmengine - INFO - Epoch(train) [85][ 80/1879] lr: 2.0000e-04 eta: 3:04:54 time: 0.3213 data_time: 0.0140 memory: 6717 grad_norm: 3.3964 loss: 1.0001 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0001 2023/04/14 13:38:42 - mmengine - INFO - Epoch(train) [85][ 100/1879] lr: 2.0000e-04 eta: 3:04:46 time: 0.3938 data_time: 0.0155 memory: 6717 grad_norm: 3.4104 loss: 1.0671 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0671 2023/04/14 13:38:49 - mmengine - INFO - Epoch(train) [85][ 120/1879] lr: 2.0000e-04 eta: 3:04:39 time: 0.3509 data_time: 0.1031 memory: 6717 grad_norm: 3.4517 loss: 1.1470 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1470 2023/04/14 13:38:57 - mmengine - INFO - Epoch(train) [85][ 140/1879] lr: 2.0000e-04 eta: 3:04:32 time: 0.4364 data_time: 0.0190 memory: 6717 grad_norm: 3.4810 loss: 1.2918 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2918 2023/04/14 13:39:04 - mmengine - INFO - Epoch(train) [85][ 160/1879] lr: 2.0000e-04 eta: 3:04:24 time: 0.3304 data_time: 0.0157 memory: 6717 grad_norm: 3.4411 loss: 1.1555 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1555 2023/04/14 13:39:06 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 13:39:12 - mmengine - INFO - Epoch(train) [85][ 180/1879] lr: 2.0000e-04 eta: 3:04:17 time: 0.3910 data_time: 0.0136 memory: 6717 grad_norm: 3.3668 loss: 1.0433 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0433 2023/04/14 13:39:18 - mmengine - INFO - Epoch(train) [85][ 200/1879] lr: 2.0000e-04 eta: 3:04:09 time: 0.3175 data_time: 0.0149 memory: 6717 grad_norm: 3.3000 loss: 1.2018 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2018 2023/04/14 13:39:26 - mmengine - INFO - Epoch(train) [85][ 220/1879] lr: 2.0000e-04 eta: 3:04:02 time: 0.4052 data_time: 0.0152 memory: 6717 grad_norm: 3.3973 loss: 1.3211 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3211 2023/04/14 13:39:33 - mmengine - INFO - Epoch(train) [85][ 240/1879] lr: 2.0000e-04 eta: 3:03:54 time: 0.3339 data_time: 0.0149 memory: 6717 grad_norm: 3.3760 loss: 1.1035 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1035 2023/04/14 13:39:41 - mmengine - INFO - Epoch(train) [85][ 260/1879] lr: 2.0000e-04 eta: 3:03:47 time: 0.3890 data_time: 0.0131 memory: 6717 grad_norm: 3.3343 loss: 1.0691 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0691 2023/04/14 13:39:47 - mmengine - INFO - Epoch(train) [85][ 280/1879] lr: 2.0000e-04 eta: 3:03:40 time: 0.3279 data_time: 0.0159 memory: 6717 grad_norm: 3.4055 loss: 1.0089 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0089 2023/04/14 13:39:56 - mmengine - INFO - Epoch(train) [85][ 300/1879] lr: 2.0000e-04 eta: 3:03:32 time: 0.4249 data_time: 0.0140 memory: 6717 grad_norm: 3.4607 loss: 1.1018 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1018 2023/04/14 13:40:02 - mmengine - INFO - Epoch(train) [85][ 320/1879] lr: 2.0000e-04 eta: 3:03:25 time: 0.3271 data_time: 0.0139 memory: 6717 grad_norm: 3.3518 loss: 0.9920 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9920 2023/04/14 13:40:11 - mmengine - INFO - Epoch(train) [85][ 340/1879] lr: 2.0000e-04 eta: 3:03:18 time: 0.4187 data_time: 0.0143 memory: 6717 grad_norm: 3.3732 loss: 1.0772 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0772 2023/04/14 13:40:17 - mmengine - INFO - Epoch(train) [85][ 360/1879] lr: 2.0000e-04 eta: 3:03:10 time: 0.3031 data_time: 0.0147 memory: 6717 grad_norm: 3.3584 loss: 1.0619 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0619 2023/04/14 13:40:25 - mmengine - INFO - Epoch(train) [85][ 380/1879] lr: 2.0000e-04 eta: 3:03:03 time: 0.4182 data_time: 0.0144 memory: 6717 grad_norm: 3.5105 loss: 1.1170 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.1170 2023/04/14 13:40:32 - mmengine - INFO - Epoch(train) [85][ 400/1879] lr: 2.0000e-04 eta: 3:02:55 time: 0.3251 data_time: 0.0151 memory: 6717 grad_norm: 3.4399 loss: 1.1095 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1095 2023/04/14 13:40:41 - mmengine - INFO - Epoch(train) [85][ 420/1879] lr: 2.0000e-04 eta: 3:02:48 time: 0.4630 data_time: 0.0156 memory: 6717 grad_norm: 3.4232 loss: 1.1149 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1149 2023/04/14 13:40:47 - mmengine - INFO - Epoch(train) [85][ 440/1879] lr: 2.0000e-04 eta: 3:02:41 time: 0.3291 data_time: 0.0137 memory: 6717 grad_norm: 3.4614 loss: 1.0071 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0071 2023/04/14 13:40:55 - mmengine - INFO - Epoch(train) [85][ 460/1879] lr: 2.0000e-04 eta: 3:02:33 time: 0.3924 data_time: 0.0146 memory: 6717 grad_norm: 3.4359 loss: 1.2639 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.2639 2023/04/14 13:41:02 - mmengine - INFO - Epoch(train) [85][ 480/1879] lr: 2.0000e-04 eta: 3:02:26 time: 0.3564 data_time: 0.0147 memory: 6717 grad_norm: 3.4070 loss: 1.0549 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0549 2023/04/14 13:41:11 - mmengine - INFO - Epoch(train) [85][ 500/1879] lr: 2.0000e-04 eta: 3:02:19 time: 0.4298 data_time: 0.0139 memory: 6717 grad_norm: 3.3885 loss: 1.1266 top1_acc: 0.3750 top5_acc: 0.6875 loss_cls: 1.1266 2023/04/14 13:41:17 - mmengine - INFO - Epoch(train) [85][ 520/1879] lr: 2.0000e-04 eta: 3:02:11 time: 0.3205 data_time: 0.0150 memory: 6717 grad_norm: 3.3991 loss: 1.1357 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1357 2023/04/14 13:41:26 - mmengine - INFO - Epoch(train) [85][ 540/1879] lr: 2.0000e-04 eta: 3:02:04 time: 0.4208 data_time: 0.0140 memory: 6717 grad_norm: 3.4372 loss: 1.2308 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2308 2023/04/14 13:41:32 - mmengine - INFO - Epoch(train) [85][ 560/1879] lr: 2.0000e-04 eta: 3:01:56 time: 0.3070 data_time: 0.0144 memory: 6717 grad_norm: 3.4004 loss: 1.1648 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1648 2023/04/14 13:41:40 - mmengine - INFO - Epoch(train) [85][ 580/1879] lr: 2.0000e-04 eta: 3:01:49 time: 0.4154 data_time: 0.0145 memory: 6717 grad_norm: 3.3934 loss: 1.0869 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0869 2023/04/14 13:41:47 - mmengine - INFO - Epoch(train) [85][ 600/1879] lr: 2.0000e-04 eta: 3:01:41 time: 0.3515 data_time: 0.0151 memory: 6717 grad_norm: 3.3634 loss: 1.0859 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0859 2023/04/14 13:41:55 - mmengine - INFO - Epoch(train) [85][ 620/1879] lr: 2.0000e-04 eta: 3:01:34 time: 0.4004 data_time: 0.0147 memory: 6717 grad_norm: 3.2971 loss: 1.0472 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0472 2023/04/14 13:42:01 - mmengine - INFO - Epoch(train) [85][ 640/1879] lr: 2.0000e-04 eta: 3:01:26 time: 0.2909 data_time: 0.0150 memory: 6717 grad_norm: 3.5106 loss: 1.1017 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1017 2023/04/14 13:42:09 - mmengine - INFO - Epoch(train) [85][ 660/1879] lr: 2.0000e-04 eta: 3:01:19 time: 0.3935 data_time: 0.0159 memory: 6717 grad_norm: 3.3919 loss: 1.1346 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1346 2023/04/14 13:42:16 - mmengine - INFO - Epoch(train) [85][ 680/1879] lr: 2.0000e-04 eta: 3:01:12 time: 0.3278 data_time: 0.0135 memory: 6717 grad_norm: 3.4913 loss: 1.1010 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1010 2023/04/14 13:42:23 - mmengine - INFO - Epoch(train) [85][ 700/1879] lr: 2.0000e-04 eta: 3:01:04 time: 0.3793 data_time: 0.0375 memory: 6717 grad_norm: 3.3597 loss: 1.1084 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1084 2023/04/14 13:42:31 - mmengine - INFO - Epoch(train) [85][ 720/1879] lr: 2.0000e-04 eta: 3:00:57 time: 0.3727 data_time: 0.0649 memory: 6717 grad_norm: 3.3274 loss: 1.0761 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0761 2023/04/14 13:42:39 - mmengine - INFO - Epoch(train) [85][ 740/1879] lr: 2.0000e-04 eta: 3:00:50 time: 0.4084 data_time: 0.1876 memory: 6717 grad_norm: 3.3997 loss: 1.0876 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0876 2023/04/14 13:42:46 - mmengine - INFO - Epoch(train) [85][ 760/1879] lr: 2.0000e-04 eta: 3:00:42 time: 0.3715 data_time: 0.1339 memory: 6717 grad_norm: 3.4709 loss: 1.2650 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2650 2023/04/14 13:42:54 - mmengine - INFO - Epoch(train) [85][ 780/1879] lr: 2.0000e-04 eta: 3:00:35 time: 0.3781 data_time: 0.1583 memory: 6717 grad_norm: 3.3863 loss: 1.0306 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0306 2023/04/14 13:43:01 - mmengine - INFO - Epoch(train) [85][ 800/1879] lr: 2.0000e-04 eta: 3:00:27 time: 0.3373 data_time: 0.0994 memory: 6717 grad_norm: 3.3905 loss: 1.1579 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1579 2023/04/14 13:43:08 - mmengine - INFO - Epoch(train) [85][ 820/1879] lr: 2.0000e-04 eta: 3:00:20 time: 0.3507 data_time: 0.1125 memory: 6717 grad_norm: 3.4201 loss: 1.1000 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1000 2023/04/14 13:43:15 - mmengine - INFO - Epoch(train) [85][ 840/1879] lr: 2.0000e-04 eta: 3:00:12 time: 0.3603 data_time: 0.0991 memory: 6717 grad_norm: 3.4113 loss: 1.1406 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1406 2023/04/14 13:43:23 - mmengine - INFO - Epoch(train) [85][ 860/1879] lr: 2.0000e-04 eta: 3:00:05 time: 0.4223 data_time: 0.2188 memory: 6717 grad_norm: 3.3725 loss: 1.2025 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2025 2023/04/14 13:43:30 - mmengine - INFO - Epoch(train) [85][ 880/1879] lr: 2.0000e-04 eta: 2:59:58 time: 0.3379 data_time: 0.1241 memory: 6717 grad_norm: 3.3635 loss: 1.1332 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1332 2023/04/14 13:43:38 - mmengine - INFO - Epoch(train) [85][ 900/1879] lr: 2.0000e-04 eta: 2:59:50 time: 0.3749 data_time: 0.1025 memory: 6717 grad_norm: 3.3366 loss: 1.0732 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0732 2023/04/14 13:43:45 - mmengine - INFO - Epoch(train) [85][ 920/1879] lr: 2.0000e-04 eta: 2:59:43 time: 0.3724 data_time: 0.1089 memory: 6717 grad_norm: 3.4297 loss: 1.0280 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0280 2023/04/14 13:43:52 - mmengine - INFO - Epoch(train) [85][ 940/1879] lr: 2.0000e-04 eta: 2:59:35 time: 0.3283 data_time: 0.0495 memory: 6717 grad_norm: 3.3838 loss: 1.0197 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0197 2023/04/14 13:44:00 - mmengine - INFO - Epoch(train) [85][ 960/1879] lr: 2.0000e-04 eta: 2:59:28 time: 0.4057 data_time: 0.0557 memory: 6717 grad_norm: 3.3218 loss: 0.9977 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9977 2023/04/14 13:44:07 - mmengine - INFO - Epoch(train) [85][ 980/1879] lr: 2.0000e-04 eta: 2:59:21 time: 0.3463 data_time: 0.0569 memory: 6717 grad_norm: 3.3726 loss: 1.1362 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1362 2023/04/14 13:44:14 - mmengine - INFO - Epoch(train) [85][1000/1879] lr: 2.0000e-04 eta: 2:59:13 time: 0.3841 data_time: 0.0459 memory: 6717 grad_norm: 3.5271 loss: 1.1426 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1426 2023/04/14 13:44:21 - mmengine - INFO - Epoch(train) [85][1020/1879] lr: 2.0000e-04 eta: 2:59:06 time: 0.3459 data_time: 0.0182 memory: 6717 grad_norm: 3.4077 loss: 0.9688 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 0.9688 2023/04/14 13:44:29 - mmengine - INFO - Epoch(train) [85][1040/1879] lr: 2.0000e-04 eta: 2:58:58 time: 0.3811 data_time: 0.0675 memory: 6717 grad_norm: 3.4451 loss: 1.2190 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.2190 2023/04/14 13:44:36 - mmengine - INFO - Epoch(train) [85][1060/1879] lr: 2.0000e-04 eta: 2:58:51 time: 0.3753 data_time: 0.1725 memory: 6717 grad_norm: 3.4141 loss: 1.0280 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0280 2023/04/14 13:44:44 - mmengine - INFO - Epoch(train) [85][1080/1879] lr: 2.0000e-04 eta: 2:58:44 time: 0.3673 data_time: 0.0882 memory: 6717 grad_norm: 3.3891 loss: 1.2167 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2167 2023/04/14 13:44:51 - mmengine - INFO - Epoch(train) [85][1100/1879] lr: 2.0000e-04 eta: 2:58:36 time: 0.3516 data_time: 0.0802 memory: 6717 grad_norm: 3.4218 loss: 1.1875 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1875 2023/04/14 13:44:59 - mmengine - INFO - Epoch(train) [85][1120/1879] lr: 2.0000e-04 eta: 2:58:29 time: 0.3985 data_time: 0.0645 memory: 6717 grad_norm: 3.4066 loss: 1.2231 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2231 2023/04/14 13:45:06 - mmengine - INFO - Epoch(train) [85][1140/1879] lr: 2.0000e-04 eta: 2:58:21 time: 0.3803 data_time: 0.2172 memory: 6717 grad_norm: 3.3319 loss: 0.8823 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 0.8823 2023/04/14 13:45:13 - mmengine - INFO - Epoch(train) [85][1160/1879] lr: 2.0000e-04 eta: 2:58:14 time: 0.3475 data_time: 0.1492 memory: 6717 grad_norm: 3.3979 loss: 1.1590 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1590 2023/04/14 13:45:15 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 13:45:21 - mmengine - INFO - Epoch(train) [85][1180/1879] lr: 2.0000e-04 eta: 2:58:07 time: 0.3635 data_time: 0.1544 memory: 6717 grad_norm: 3.3620 loss: 1.1843 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1843 2023/04/14 13:45:28 - mmengine - INFO - Epoch(train) [85][1200/1879] lr: 2.0000e-04 eta: 2:57:59 time: 0.3636 data_time: 0.0763 memory: 6717 grad_norm: 3.4316 loss: 1.3351 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.3351 2023/04/14 13:45:35 - mmengine - INFO - Epoch(train) [85][1220/1879] lr: 2.0000e-04 eta: 2:57:52 time: 0.3663 data_time: 0.1163 memory: 6717 grad_norm: 3.3831 loss: 1.2687 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2687 2023/04/14 13:45:43 - mmengine - INFO - Epoch(train) [85][1240/1879] lr: 2.0000e-04 eta: 2:57:44 time: 0.3810 data_time: 0.0802 memory: 6717 grad_norm: 3.5005 loss: 1.2504 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2504 2023/04/14 13:45:50 - mmengine - INFO - Epoch(train) [85][1260/1879] lr: 2.0000e-04 eta: 2:57:37 time: 0.3357 data_time: 0.0945 memory: 6717 grad_norm: 3.4627 loss: 1.0563 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0563 2023/04/14 13:45:58 - mmengine - INFO - Epoch(train) [85][1280/1879] lr: 2.0000e-04 eta: 2:57:30 time: 0.4024 data_time: 0.0295 memory: 6717 grad_norm: 3.4256 loss: 1.0511 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0511 2023/04/14 13:46:05 - mmengine - INFO - Epoch(train) [85][1300/1879] lr: 2.0000e-04 eta: 2:57:22 time: 0.3463 data_time: 0.0942 memory: 6717 grad_norm: 3.4316 loss: 1.2991 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2991 2023/04/14 13:46:12 - mmengine - INFO - Epoch(train) [85][1320/1879] lr: 2.0000e-04 eta: 2:57:15 time: 0.3774 data_time: 0.1038 memory: 6717 grad_norm: 3.3467 loss: 0.9860 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9860 2023/04/14 13:46:19 - mmengine - INFO - Epoch(train) [85][1340/1879] lr: 2.0000e-04 eta: 2:57:07 time: 0.3378 data_time: 0.0389 memory: 6717 grad_norm: 3.3938 loss: 1.1497 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1497 2023/04/14 13:46:27 - mmengine - INFO - Epoch(train) [85][1360/1879] lr: 2.0000e-04 eta: 2:57:00 time: 0.4206 data_time: 0.0204 memory: 6717 grad_norm: 3.4605 loss: 1.1230 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1230 2023/04/14 13:46:34 - mmengine - INFO - Epoch(train) [85][1380/1879] lr: 2.0000e-04 eta: 2:56:52 time: 0.3317 data_time: 0.0478 memory: 6717 grad_norm: 3.4213 loss: 1.2078 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2078 2023/04/14 13:46:42 - mmengine - INFO - Epoch(train) [85][1400/1879] lr: 2.0000e-04 eta: 2:56:45 time: 0.3830 data_time: 0.0198 memory: 6717 grad_norm: 3.4287 loss: 1.1201 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1201 2023/04/14 13:46:49 - mmengine - INFO - Epoch(train) [85][1420/1879] lr: 2.0000e-04 eta: 2:56:38 time: 0.3447 data_time: 0.1100 memory: 6717 grad_norm: 3.4515 loss: 1.2159 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2159 2023/04/14 13:46:56 - mmengine - INFO - Epoch(train) [85][1440/1879] lr: 2.0000e-04 eta: 2:56:30 time: 0.3956 data_time: 0.1865 memory: 6717 grad_norm: 3.4014 loss: 1.0937 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0937 2023/04/14 13:47:03 - mmengine - INFO - Epoch(train) [85][1460/1879] lr: 2.0000e-04 eta: 2:56:23 time: 0.3406 data_time: 0.1778 memory: 6717 grad_norm: 3.3661 loss: 0.9885 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9885 2023/04/14 13:47:12 - mmengine - INFO - Epoch(train) [85][1480/1879] lr: 2.0000e-04 eta: 2:56:16 time: 0.4221 data_time: 0.2203 memory: 6717 grad_norm: 3.3292 loss: 1.1879 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1879 2023/04/14 13:47:18 - mmengine - INFO - Epoch(train) [85][1500/1879] lr: 2.0000e-04 eta: 2:56:08 time: 0.3356 data_time: 0.1991 memory: 6717 grad_norm: 3.4719 loss: 1.2549 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2549 2023/04/14 13:47:26 - mmengine - INFO - Epoch(train) [85][1520/1879] lr: 2.0000e-04 eta: 2:56:01 time: 0.3629 data_time: 0.2149 memory: 6717 grad_norm: 3.4952 loss: 1.1555 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1555 2023/04/14 13:47:32 - mmengine - INFO - Epoch(train) [85][1540/1879] lr: 2.0000e-04 eta: 2:55:53 time: 0.3399 data_time: 0.1766 memory: 6717 grad_norm: 3.4181 loss: 1.1732 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1732 2023/04/14 13:47:40 - mmengine - INFO - Epoch(train) [85][1560/1879] lr: 2.0000e-04 eta: 2:55:46 time: 0.3951 data_time: 0.0580 memory: 6717 grad_norm: 3.3568 loss: 0.9983 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9983 2023/04/14 13:47:47 - mmengine - INFO - Epoch(train) [85][1580/1879] lr: 2.0000e-04 eta: 2:55:38 time: 0.3260 data_time: 0.0140 memory: 6717 grad_norm: 3.3921 loss: 0.9066 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9066 2023/04/14 13:47:55 - mmengine - INFO - Epoch(train) [85][1600/1879] lr: 2.0000e-04 eta: 2:55:31 time: 0.4042 data_time: 0.0581 memory: 6717 grad_norm: 3.3432 loss: 1.2273 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2273 2023/04/14 13:48:02 - mmengine - INFO - Epoch(train) [85][1620/1879] lr: 2.0000e-04 eta: 2:55:23 time: 0.3398 data_time: 0.1461 memory: 6717 grad_norm: 3.4169 loss: 1.0880 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0880 2023/04/14 13:48:10 - mmengine - INFO - Epoch(train) [85][1640/1879] lr: 2.0000e-04 eta: 2:55:16 time: 0.4017 data_time: 0.2221 memory: 6717 grad_norm: 3.4937 loss: 1.2985 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2985 2023/04/14 13:48:16 - mmengine - INFO - Epoch(train) [85][1660/1879] lr: 2.0000e-04 eta: 2:55:09 time: 0.3315 data_time: 0.1321 memory: 6717 grad_norm: 3.4759 loss: 1.1483 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1483 2023/04/14 13:48:25 - mmengine - INFO - Epoch(train) [85][1680/1879] lr: 2.0000e-04 eta: 2:55:01 time: 0.4269 data_time: 0.0974 memory: 6717 grad_norm: 3.4156 loss: 1.0567 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0567 2023/04/14 13:48:32 - mmengine - INFO - Epoch(train) [85][1700/1879] lr: 2.0000e-04 eta: 2:54:54 time: 0.3333 data_time: 0.1098 memory: 6717 grad_norm: 3.4148 loss: 0.9892 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9892 2023/04/14 13:48:39 - mmengine - INFO - Epoch(train) [85][1720/1879] lr: 2.0000e-04 eta: 2:54:47 time: 0.3848 data_time: 0.2153 memory: 6717 grad_norm: 3.5101 loss: 1.2276 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.2276 2023/04/14 13:48:47 - mmengine - INFO - Epoch(train) [85][1740/1879] lr: 2.0000e-04 eta: 2:54:39 time: 0.3615 data_time: 0.2255 memory: 6717 grad_norm: 3.4920 loss: 1.2016 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2016 2023/04/14 13:48:55 - mmengine - INFO - Epoch(train) [85][1760/1879] lr: 2.0000e-04 eta: 2:54:32 time: 0.4071 data_time: 0.2502 memory: 6717 grad_norm: 3.4187 loss: 1.1813 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1813 2023/04/14 13:49:02 - mmengine - INFO - Epoch(train) [85][1780/1879] lr: 2.0000e-04 eta: 2:54:24 time: 0.3508 data_time: 0.1854 memory: 6717 grad_norm: 3.3469 loss: 1.1078 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1078 2023/04/14 13:49:09 - mmengine - INFO - Epoch(train) [85][1800/1879] lr: 2.0000e-04 eta: 2:54:17 time: 0.3442 data_time: 0.1848 memory: 6717 grad_norm: 3.3345 loss: 1.0655 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0655 2023/04/14 13:49:17 - mmengine - INFO - Epoch(train) [85][1820/1879] lr: 2.0000e-04 eta: 2:54:10 time: 0.4007 data_time: 0.2093 memory: 6717 grad_norm: 3.4656 loss: 1.2973 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2973 2023/04/14 13:49:24 - mmengine - INFO - Epoch(train) [85][1840/1879] lr: 2.0000e-04 eta: 2:54:02 time: 0.3552 data_time: 0.1450 memory: 6717 grad_norm: 3.3171 loss: 1.0512 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0512 2023/04/14 13:49:31 - mmengine - INFO - Epoch(train) [85][1860/1879] lr: 2.0000e-04 eta: 2:53:55 time: 0.3690 data_time: 0.1744 memory: 6717 grad_norm: 3.5135 loss: 0.9796 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9796 2023/04/14 13:49:37 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 13:49:37 - mmengine - INFO - Epoch(train) [85][1879/1879] lr: 2.0000e-04 eta: 2:53:47 time: 0.3227 data_time: 0.1780 memory: 6717 grad_norm: 3.4350 loss: 1.0832 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.0832 2023/04/14 13:49:45 - mmengine - INFO - Epoch(val) [85][ 20/155] eta: 0:00:59 time: 0.4378 data_time: 0.4037 memory: 1391 2023/04/14 13:49:52 - mmengine - INFO - Epoch(val) [85][ 40/155] eta: 0:00:44 time: 0.3413 data_time: 0.3078 memory: 1391 2023/04/14 13:50:00 - mmengine - INFO - Epoch(val) [85][ 60/155] eta: 0:00:36 time: 0.3797 data_time: 0.3455 memory: 1391 2023/04/14 13:50:07 - mmengine - INFO - Epoch(val) [85][ 80/155] eta: 0:00:28 time: 0.3581 data_time: 0.3248 memory: 1391 2023/04/14 13:50:16 - mmengine - INFO - Epoch(val) [85][100/155] eta: 0:00:21 time: 0.4415 data_time: 0.4084 memory: 1391 2023/04/14 13:50:22 - mmengine - INFO - Epoch(val) [85][120/155] eta: 0:00:13 time: 0.2999 data_time: 0.2664 memory: 1391 2023/04/14 13:50:30 - mmengine - INFO - Epoch(val) [85][140/155] eta: 0:00:05 time: 0.3880 data_time: 0.3541 memory: 1391 2023/04/14 13:50:38 - mmengine - INFO - Epoch(val) [85][155/155] acc/top1: 0.6684 acc/top5: 0.8748 acc/mean1: 0.6684 data_time: 0.3042 time: 0.3373 2023/04/14 13:50:48 - mmengine - INFO - Epoch(train) [86][ 20/1879] lr: 2.0000e-04 eta: 2:53:40 time: 0.4791 data_time: 0.3244 memory: 6717 grad_norm: 3.4399 loss: 1.0007 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0007 2023/04/14 13:50:55 - mmengine - INFO - Epoch(train) [86][ 40/1879] lr: 2.0000e-04 eta: 2:53:33 time: 0.3375 data_time: 0.2025 memory: 6717 grad_norm: 3.4404 loss: 1.0963 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0963 2023/04/14 13:51:03 - mmengine - INFO - Epoch(train) [86][ 60/1879] lr: 2.0000e-04 eta: 2:53:26 time: 0.4336 data_time: 0.2975 memory: 6717 grad_norm: 3.5380 loss: 1.1925 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1925 2023/04/14 13:51:10 - mmengine - INFO - Epoch(train) [86][ 80/1879] lr: 2.0000e-04 eta: 2:53:18 time: 0.3251 data_time: 0.1881 memory: 6717 grad_norm: 3.3941 loss: 1.0962 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0962 2023/04/14 13:51:18 - mmengine - INFO - Epoch(train) [86][ 100/1879] lr: 2.0000e-04 eta: 2:53:11 time: 0.4180 data_time: 0.2809 memory: 6717 grad_norm: 3.5004 loss: 1.1386 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1386 2023/04/14 13:51:25 - mmengine - INFO - Epoch(train) [86][ 120/1879] lr: 2.0000e-04 eta: 2:53:03 time: 0.3271 data_time: 0.1903 memory: 6717 grad_norm: 3.4237 loss: 0.9978 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9978 2023/04/14 13:51:34 - mmengine - INFO - Epoch(train) [86][ 140/1879] lr: 2.0000e-04 eta: 2:52:56 time: 0.4465 data_time: 0.3059 memory: 6717 grad_norm: 3.4303 loss: 1.2223 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2223 2023/04/14 13:51:41 - mmengine - INFO - Epoch(train) [86][ 160/1879] lr: 2.0000e-04 eta: 2:52:49 time: 0.3512 data_time: 0.2138 memory: 6717 grad_norm: 3.4769 loss: 1.2098 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2098 2023/04/14 13:51:49 - mmengine - INFO - Epoch(train) [86][ 180/1879] lr: 2.0000e-04 eta: 2:52:42 time: 0.4265 data_time: 0.2855 memory: 6717 grad_norm: 3.3878 loss: 1.1111 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1111 2023/04/14 13:51:56 - mmengine - INFO - Epoch(train) [86][ 200/1879] lr: 2.0000e-04 eta: 2:52:34 time: 0.3329 data_time: 0.1960 memory: 6717 grad_norm: 3.3687 loss: 1.1140 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1140 2023/04/14 13:52:04 - mmengine - INFO - Epoch(train) [86][ 220/1879] lr: 2.0000e-04 eta: 2:52:27 time: 0.4200 data_time: 0.2816 memory: 6717 grad_norm: 3.4465 loss: 1.1314 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1314 2023/04/14 13:52:11 - mmengine - INFO - Epoch(train) [86][ 240/1879] lr: 2.0000e-04 eta: 2:52:19 time: 0.3280 data_time: 0.1870 memory: 6717 grad_norm: 3.3592 loss: 1.0639 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0639 2023/04/14 13:52:19 - mmengine - INFO - Epoch(train) [86][ 260/1879] lr: 2.0000e-04 eta: 2:52:12 time: 0.4073 data_time: 0.2698 memory: 6717 grad_norm: 3.4463 loss: 0.9998 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9998 2023/04/14 13:52:25 - mmengine - INFO - Epoch(train) [86][ 280/1879] lr: 2.0000e-04 eta: 2:52:04 time: 0.2957 data_time: 0.1548 memory: 6717 grad_norm: 3.3949 loss: 1.1730 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1730 2023/04/14 13:52:28 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 13:52:33 - mmengine - INFO - Epoch(train) [86][ 300/1879] lr: 2.0000e-04 eta: 2:51:57 time: 0.4111 data_time: 0.2642 memory: 6717 grad_norm: 3.4251 loss: 1.1936 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1936 2023/04/14 13:52:39 - mmengine - INFO - Epoch(train) [86][ 320/1879] lr: 2.0000e-04 eta: 2:51:49 time: 0.3033 data_time: 0.1429 memory: 6717 grad_norm: 3.4308 loss: 1.1539 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1539 2023/04/14 13:52:47 - mmengine - INFO - Epoch(train) [86][ 340/1879] lr: 2.0000e-04 eta: 2:51:42 time: 0.4025 data_time: 0.1977 memory: 6717 grad_norm: 3.4233 loss: 1.0513 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0513 2023/04/14 13:52:54 - mmengine - INFO - Epoch(train) [86][ 360/1879] lr: 2.0000e-04 eta: 2:51:35 time: 0.3425 data_time: 0.0770 memory: 6717 grad_norm: 3.4194 loss: 1.1643 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1643 2023/04/14 13:53:02 - mmengine - INFO - Epoch(train) [86][ 380/1879] lr: 2.0000e-04 eta: 2:51:27 time: 0.3887 data_time: 0.1336 memory: 6717 grad_norm: 3.3837 loss: 0.9956 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9956 2023/04/14 13:53:09 - mmengine - INFO - Epoch(train) [86][ 400/1879] lr: 2.0000e-04 eta: 2:51:20 time: 0.3588 data_time: 0.1278 memory: 6717 grad_norm: 3.4624 loss: 1.1052 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1052 2023/04/14 13:53:17 - mmengine - INFO - Epoch(train) [86][ 420/1879] lr: 2.0000e-04 eta: 2:51:13 time: 0.3864 data_time: 0.1902 memory: 6717 grad_norm: 3.4653 loss: 1.0126 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0126 2023/04/14 13:53:24 - mmengine - INFO - Epoch(train) [86][ 440/1879] lr: 2.0000e-04 eta: 2:51:05 time: 0.3311 data_time: 0.0653 memory: 6717 grad_norm: 3.4490 loss: 1.0582 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0582 2023/04/14 13:53:31 - mmengine - INFO - Epoch(train) [86][ 460/1879] lr: 2.0000e-04 eta: 2:50:58 time: 0.3970 data_time: 0.0542 memory: 6717 grad_norm: 3.4402 loss: 1.1615 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1615 2023/04/14 13:53:38 - mmengine - INFO - Epoch(train) [86][ 480/1879] lr: 2.0000e-04 eta: 2:50:50 time: 0.3326 data_time: 0.0135 memory: 6717 grad_norm: 3.4011 loss: 1.1436 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1436 2023/04/14 13:53:46 - mmengine - INFO - Epoch(train) [86][ 500/1879] lr: 2.0000e-04 eta: 2:50:43 time: 0.4029 data_time: 0.0150 memory: 6717 grad_norm: 3.4491 loss: 1.1302 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1302 2023/04/14 13:53:53 - mmengine - INFO - Epoch(train) [86][ 520/1879] lr: 2.0000e-04 eta: 2:50:35 time: 0.3317 data_time: 0.0400 memory: 6717 grad_norm: 3.3685 loss: 0.9767 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9767 2023/04/14 13:54:02 - mmengine - INFO - Epoch(train) [86][ 540/1879] lr: 2.0000e-04 eta: 2:50:28 time: 0.4507 data_time: 0.0359 memory: 6717 grad_norm: 3.3599 loss: 1.1273 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1273 2023/04/14 13:54:08 - mmengine - INFO - Epoch(train) [86][ 560/1879] lr: 2.0000e-04 eta: 2:50:21 time: 0.3059 data_time: 0.0127 memory: 6717 grad_norm: 3.4589 loss: 1.0193 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0193 2023/04/14 13:54:16 - mmengine - INFO - Epoch(train) [86][ 580/1879] lr: 2.0000e-04 eta: 2:50:13 time: 0.3913 data_time: 0.0148 memory: 6717 grad_norm: 3.3773 loss: 0.9989 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9989 2023/04/14 13:54:23 - mmengine - INFO - Epoch(train) [86][ 600/1879] lr: 2.0000e-04 eta: 2:50:06 time: 0.3352 data_time: 0.0155 memory: 6717 grad_norm: 3.4040 loss: 1.0690 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0690 2023/04/14 13:54:30 - mmengine - INFO - Epoch(train) [86][ 620/1879] lr: 2.0000e-04 eta: 2:49:58 time: 0.3676 data_time: 0.0146 memory: 6717 grad_norm: 3.3681 loss: 1.1411 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1411 2023/04/14 13:54:37 - mmengine - INFO - Epoch(train) [86][ 640/1879] lr: 2.0000e-04 eta: 2:49:51 time: 0.3550 data_time: 0.0155 memory: 6717 grad_norm: 3.4905 loss: 1.3509 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3509 2023/04/14 13:54:45 - mmengine - INFO - Epoch(train) [86][ 660/1879] lr: 2.0000e-04 eta: 2:49:44 time: 0.3877 data_time: 0.0137 memory: 6717 grad_norm: 3.3816 loss: 1.2508 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2508 2023/04/14 13:54:52 - mmengine - INFO - Epoch(train) [86][ 680/1879] lr: 2.0000e-04 eta: 2:49:36 time: 0.3538 data_time: 0.0155 memory: 6717 grad_norm: 3.4757 loss: 1.0711 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0711 2023/04/14 13:55:00 - mmengine - INFO - Epoch(train) [86][ 700/1879] lr: 2.0000e-04 eta: 2:49:29 time: 0.4043 data_time: 0.0138 memory: 6717 grad_norm: 3.3470 loss: 1.0493 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0493 2023/04/14 13:55:07 - mmengine - INFO - Epoch(train) [86][ 720/1879] lr: 2.0000e-04 eta: 2:49:21 time: 0.3500 data_time: 0.0141 memory: 6717 grad_norm: 3.4407 loss: 1.1930 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1930 2023/04/14 13:55:15 - mmengine - INFO - Epoch(train) [86][ 740/1879] lr: 2.0000e-04 eta: 2:49:14 time: 0.3989 data_time: 0.0143 memory: 6717 grad_norm: 3.4685 loss: 1.2762 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2762 2023/04/14 13:55:22 - mmengine - INFO - Epoch(train) [86][ 760/1879] lr: 2.0000e-04 eta: 2:49:07 time: 0.3322 data_time: 0.0144 memory: 6717 grad_norm: 3.5027 loss: 1.1261 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1261 2023/04/14 13:55:29 - mmengine - INFO - Epoch(train) [86][ 780/1879] lr: 2.0000e-04 eta: 2:48:59 time: 0.3823 data_time: 0.0149 memory: 6717 grad_norm: 3.4359 loss: 1.0974 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0974 2023/04/14 13:55:36 - mmengine - INFO - Epoch(train) [86][ 800/1879] lr: 2.0000e-04 eta: 2:48:52 time: 0.3521 data_time: 0.0143 memory: 6717 grad_norm: 3.4141 loss: 1.0693 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0693 2023/04/14 13:55:44 - mmengine - INFO - Epoch(train) [86][ 820/1879] lr: 2.0000e-04 eta: 2:48:44 time: 0.3711 data_time: 0.0439 memory: 6717 grad_norm: 3.4653 loss: 1.1671 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1671 2023/04/14 13:55:51 - mmengine - INFO - Epoch(train) [86][ 840/1879] lr: 2.0000e-04 eta: 2:48:37 time: 0.3789 data_time: 0.0140 memory: 6717 grad_norm: 3.3559 loss: 1.1041 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.1041 2023/04/14 13:55:58 - mmengine - INFO - Epoch(train) [86][ 860/1879] lr: 2.0000e-04 eta: 2:48:30 time: 0.3528 data_time: 0.0150 memory: 6717 grad_norm: 3.4274 loss: 1.2548 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2548 2023/04/14 13:56:06 - mmengine - INFO - Epoch(train) [86][ 880/1879] lr: 2.0000e-04 eta: 2:48:22 time: 0.3736 data_time: 0.0336 memory: 6717 grad_norm: 3.3452 loss: 1.0784 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0784 2023/04/14 13:56:13 - mmengine - INFO - Epoch(train) [86][ 900/1879] lr: 2.0000e-04 eta: 2:48:15 time: 0.3597 data_time: 0.0392 memory: 6717 grad_norm: 3.5348 loss: 1.1562 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1562 2023/04/14 13:56:21 - mmengine - INFO - Epoch(train) [86][ 920/1879] lr: 2.0000e-04 eta: 2:48:07 time: 0.3965 data_time: 0.0360 memory: 6717 grad_norm: 3.4117 loss: 0.9872 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9872 2023/04/14 13:56:27 - mmengine - INFO - Epoch(train) [86][ 940/1879] lr: 2.0000e-04 eta: 2:48:00 time: 0.3133 data_time: 0.0375 memory: 6717 grad_norm: 3.4480 loss: 1.0347 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0347 2023/04/14 13:56:35 - mmengine - INFO - Epoch(train) [86][ 960/1879] lr: 2.0000e-04 eta: 2:47:52 time: 0.3937 data_time: 0.0342 memory: 6717 grad_norm: 3.4109 loss: 1.1093 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1093 2023/04/14 13:56:42 - mmengine - INFO - Epoch(train) [86][ 980/1879] lr: 2.0000e-04 eta: 2:47:45 time: 0.3358 data_time: 0.0367 memory: 6717 grad_norm: 3.4611 loss: 1.2567 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2567 2023/04/14 13:56:50 - mmengine - INFO - Epoch(train) [86][1000/1879] lr: 2.0000e-04 eta: 2:47:38 time: 0.3913 data_time: 0.0150 memory: 6717 grad_norm: 3.3618 loss: 1.0516 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0516 2023/04/14 13:56:57 - mmengine - INFO - Epoch(train) [86][1020/1879] lr: 2.0000e-04 eta: 2:47:30 time: 0.3652 data_time: 0.0317 memory: 6717 grad_norm: 3.4714 loss: 1.1643 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1643 2023/04/14 13:57:05 - mmengine - INFO - Epoch(train) [86][1040/1879] lr: 2.0000e-04 eta: 2:47:23 time: 0.4194 data_time: 0.0135 memory: 6717 grad_norm: 3.4878 loss: 0.9839 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9839 2023/04/14 13:57:11 - mmengine - INFO - Epoch(train) [86][1060/1879] lr: 2.0000e-04 eta: 2:47:15 time: 0.2961 data_time: 0.0154 memory: 6717 grad_norm: 3.3831 loss: 1.1109 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1109 2023/04/14 13:57:20 - mmengine - INFO - Epoch(train) [86][1080/1879] lr: 2.0000e-04 eta: 2:47:08 time: 0.4335 data_time: 0.0131 memory: 6717 grad_norm: 3.3743 loss: 1.2621 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2621 2023/04/14 13:57:27 - mmengine - INFO - Epoch(train) [86][1100/1879] lr: 2.0000e-04 eta: 2:47:01 time: 0.3309 data_time: 0.0150 memory: 6717 grad_norm: 3.4064 loss: 1.0408 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0408 2023/04/14 13:57:35 - mmengine - INFO - Epoch(train) [86][1120/1879] lr: 2.0000e-04 eta: 2:46:53 time: 0.3973 data_time: 0.0146 memory: 6717 grad_norm: 3.3732 loss: 1.0268 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0268 2023/04/14 13:57:41 - mmengine - INFO - Epoch(train) [86][1140/1879] lr: 2.0000e-04 eta: 2:46:46 time: 0.3330 data_time: 0.0136 memory: 6717 grad_norm: 3.3793 loss: 0.9515 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9515 2023/04/14 13:57:49 - mmengine - INFO - Epoch(train) [86][1160/1879] lr: 2.0000e-04 eta: 2:46:38 time: 0.3954 data_time: 0.0146 memory: 6717 grad_norm: 3.3944 loss: 1.0734 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0734 2023/04/14 13:57:57 - mmengine - INFO - Epoch(train) [86][1180/1879] lr: 2.0000e-04 eta: 2:46:31 time: 0.3670 data_time: 0.0127 memory: 6717 grad_norm: 3.3547 loss: 1.0118 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0118 2023/04/14 13:58:03 - mmengine - INFO - Epoch(train) [86][1200/1879] lr: 2.0000e-04 eta: 2:46:24 time: 0.3323 data_time: 0.0139 memory: 6717 grad_norm: 3.4207 loss: 1.0592 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0592 2023/04/14 13:58:12 - mmengine - INFO - Epoch(train) [86][1220/1879] lr: 2.0000e-04 eta: 2:46:16 time: 0.4161 data_time: 0.0155 memory: 6717 grad_norm: 3.4884 loss: 1.2642 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2642 2023/04/14 13:58:18 - mmengine - INFO - Epoch(train) [86][1240/1879] lr: 2.0000e-04 eta: 2:46:09 time: 0.3413 data_time: 0.0281 memory: 6717 grad_norm: 3.3129 loss: 0.9159 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9159 2023/04/14 13:58:26 - mmengine - INFO - Epoch(train) [86][1260/1879] lr: 2.0000e-04 eta: 2:46:01 time: 0.3798 data_time: 0.0737 memory: 6717 grad_norm: 3.4891 loss: 1.2286 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2286 2023/04/14 13:58:33 - mmengine - INFO - Epoch(train) [86][1280/1879] lr: 2.0000e-04 eta: 2:45:54 time: 0.3573 data_time: 0.0155 memory: 6717 grad_norm: 3.5028 loss: 1.2929 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2929 2023/04/14 13:58:35 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 13:58:41 - mmengine - INFO - Epoch(train) [86][1300/1879] lr: 2.0000e-04 eta: 2:45:47 time: 0.3887 data_time: 0.0914 memory: 6717 grad_norm: 3.3959 loss: 1.1778 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1778 2023/04/14 13:58:48 - mmengine - INFO - Epoch(train) [86][1320/1879] lr: 2.0000e-04 eta: 2:45:39 time: 0.3588 data_time: 0.0134 memory: 6717 grad_norm: 3.4230 loss: 1.1683 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1683 2023/04/14 13:58:55 - mmengine - INFO - Epoch(train) [86][1340/1879] lr: 2.0000e-04 eta: 2:45:32 time: 0.3484 data_time: 0.0208 memory: 6717 grad_norm: 3.3652 loss: 0.9216 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9216 2023/04/14 13:59:02 - mmengine - INFO - Epoch(train) [86][1360/1879] lr: 2.0000e-04 eta: 2:45:24 time: 0.3255 data_time: 0.0620 memory: 6717 grad_norm: 3.4325 loss: 0.8884 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.8884 2023/04/14 13:59:09 - mmengine - INFO - Epoch(train) [86][1380/1879] lr: 2.0000e-04 eta: 2:45:17 time: 0.3933 data_time: 0.0835 memory: 6717 grad_norm: 3.3858 loss: 1.1092 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1092 2023/04/14 13:59:18 - mmengine - INFO - Epoch(train) [86][1400/1879] lr: 2.0000e-04 eta: 2:45:10 time: 0.4057 data_time: 0.0149 memory: 6717 grad_norm: 3.4115 loss: 1.0564 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0564 2023/04/14 13:59:25 - mmengine - INFO - Epoch(train) [86][1420/1879] lr: 2.0000e-04 eta: 2:45:02 time: 0.3554 data_time: 0.0144 memory: 6717 grad_norm: 3.3946 loss: 1.1816 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1816 2023/04/14 13:59:32 - mmengine - INFO - Epoch(train) [86][1440/1879] lr: 2.0000e-04 eta: 2:44:55 time: 0.3888 data_time: 0.0155 memory: 6717 grad_norm: 3.4625 loss: 1.1075 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1075 2023/04/14 13:59:38 - mmengine - INFO - Epoch(train) [86][1460/1879] lr: 2.0000e-04 eta: 2:44:47 time: 0.2848 data_time: 0.0136 memory: 6717 grad_norm: 3.3750 loss: 1.1131 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1131 2023/04/14 13:59:46 - mmengine - INFO - Epoch(train) [86][1480/1879] lr: 2.0000e-04 eta: 2:44:40 time: 0.4090 data_time: 0.0162 memory: 6717 grad_norm: 3.4578 loss: 1.2794 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2794 2023/04/14 13:59:53 - mmengine - INFO - Epoch(train) [86][1500/1879] lr: 2.0000e-04 eta: 2:44:32 time: 0.3579 data_time: 0.0142 memory: 6717 grad_norm: 3.4833 loss: 1.2273 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2273 2023/04/14 14:00:01 - mmengine - INFO - Epoch(train) [86][1520/1879] lr: 2.0000e-04 eta: 2:44:25 time: 0.3806 data_time: 0.0162 memory: 6717 grad_norm: 3.5399 loss: 1.2974 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2974 2023/04/14 14:00:09 - mmengine - INFO - Epoch(train) [86][1540/1879] lr: 2.0000e-04 eta: 2:44:18 time: 0.3737 data_time: 0.0138 memory: 6717 grad_norm: 3.3490 loss: 1.0609 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0609 2023/04/14 14:00:16 - mmengine - INFO - Epoch(train) [86][1560/1879] lr: 2.0000e-04 eta: 2:44:10 time: 0.3684 data_time: 0.0149 memory: 6717 grad_norm: 3.3333 loss: 1.0768 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0768 2023/04/14 14:00:24 - mmengine - INFO - Epoch(train) [86][1580/1879] lr: 2.0000e-04 eta: 2:44:03 time: 0.3879 data_time: 0.0143 memory: 6717 grad_norm: 3.4544 loss: 1.1258 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1258 2023/04/14 14:00:31 - mmengine - INFO - Epoch(train) [86][1600/1879] lr: 2.0000e-04 eta: 2:43:55 time: 0.3513 data_time: 0.0208 memory: 6717 grad_norm: 3.4298 loss: 1.1725 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1725 2023/04/14 14:00:38 - mmengine - INFO - Epoch(train) [86][1620/1879] lr: 2.0000e-04 eta: 2:43:48 time: 0.3768 data_time: 0.0180 memory: 6717 grad_norm: 3.3309 loss: 1.1144 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1144 2023/04/14 14:00:46 - mmengine - INFO - Epoch(train) [86][1640/1879] lr: 2.0000e-04 eta: 2:43:41 time: 0.3870 data_time: 0.0146 memory: 6717 grad_norm: 3.4251 loss: 1.2713 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2713 2023/04/14 14:00:54 - mmengine - INFO - Epoch(train) [86][1660/1879] lr: 2.0000e-04 eta: 2:43:33 time: 0.3923 data_time: 0.0132 memory: 6717 grad_norm: 3.3928 loss: 1.0777 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0777 2023/04/14 14:01:01 - mmengine - INFO - Epoch(train) [86][1680/1879] lr: 2.0000e-04 eta: 2:43:26 time: 0.3516 data_time: 0.0152 memory: 6717 grad_norm: 3.3520 loss: 0.9890 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9890 2023/04/14 14:01:09 - mmengine - INFO - Epoch(train) [86][1700/1879] lr: 2.0000e-04 eta: 2:43:19 time: 0.4082 data_time: 0.0129 memory: 6717 grad_norm: 3.3569 loss: 1.0434 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.0434 2023/04/14 14:01:15 - mmengine - INFO - Epoch(train) [86][1720/1879] lr: 2.0000e-04 eta: 2:43:11 time: 0.3158 data_time: 0.0154 memory: 6717 grad_norm: 3.3908 loss: 1.0931 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0931 2023/04/14 14:01:24 - mmengine - INFO - Epoch(train) [86][1740/1879] lr: 2.0000e-04 eta: 2:43:04 time: 0.4194 data_time: 0.0148 memory: 6717 grad_norm: 3.4930 loss: 1.1541 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.1541 2023/04/14 14:01:31 - mmengine - INFO - Epoch(train) [86][1760/1879] lr: 2.0000e-04 eta: 2:42:56 time: 0.3430 data_time: 0.0136 memory: 6717 grad_norm: 3.5012 loss: 1.0906 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0906 2023/04/14 14:01:39 - mmengine - INFO - Epoch(train) [86][1780/1879] lr: 2.0000e-04 eta: 2:42:49 time: 0.3963 data_time: 0.0137 memory: 6717 grad_norm: 3.4169 loss: 1.1533 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1533 2023/04/14 14:01:45 - mmengine - INFO - Epoch(train) [86][1800/1879] lr: 2.0000e-04 eta: 2:42:42 time: 0.3335 data_time: 0.0153 memory: 6717 grad_norm: 3.3902 loss: 1.1100 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1100 2023/04/14 14:01:53 - mmengine - INFO - Epoch(train) [86][1820/1879] lr: 2.0000e-04 eta: 2:42:34 time: 0.3647 data_time: 0.0137 memory: 6717 grad_norm: 3.5269 loss: 1.1742 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1742 2023/04/14 14:01:59 - mmengine - INFO - Epoch(train) [86][1840/1879] lr: 2.0000e-04 eta: 2:42:27 time: 0.3361 data_time: 0.0146 memory: 6717 grad_norm: 3.3953 loss: 1.0423 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0423 2023/04/14 14:02:07 - mmengine - INFO - Epoch(train) [86][1860/1879] lr: 2.0000e-04 eta: 2:42:19 time: 0.3966 data_time: 0.0159 memory: 6717 grad_norm: 3.4328 loss: 1.1816 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1816 2023/04/14 14:02:13 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 14:02:13 - mmengine - INFO - Epoch(train) [86][1879/1879] lr: 2.0000e-04 eta: 2:42:12 time: 0.2801 data_time: 0.0137 memory: 6717 grad_norm: 3.4657 loss: 1.2687 top1_acc: 0.2857 top5_acc: 1.0000 loss_cls: 1.2687 2023/04/14 14:02:22 - mmengine - INFO - Epoch(val) [86][ 20/155] eta: 0:01:01 time: 0.4551 data_time: 0.4217 memory: 1391 2023/04/14 14:02:28 - mmengine - INFO - Epoch(val) [86][ 40/155] eta: 0:00:44 time: 0.3202 data_time: 0.2871 memory: 1391 2023/04/14 14:02:37 - mmengine - INFO - Epoch(val) [86][ 60/155] eta: 0:00:38 time: 0.4473 data_time: 0.4136 memory: 1391 2023/04/14 14:02:43 - mmengine - INFO - Epoch(val) [86][ 80/155] eta: 0:00:28 time: 0.3153 data_time: 0.2821 memory: 1391 2023/04/14 14:02:53 - mmengine - INFO - Epoch(val) [86][100/155] eta: 0:00:21 time: 0.4537 data_time: 0.4201 memory: 1391 2023/04/14 14:02:58 - mmengine - INFO - Epoch(val) [86][120/155] eta: 0:00:13 time: 0.2956 data_time: 0.2626 memory: 1391 2023/04/14 14:03:08 - mmengine - INFO - Epoch(val) [86][140/155] eta: 0:00:05 time: 0.4840 data_time: 0.4512 memory: 1391 2023/04/14 14:03:15 - mmengine - INFO - Epoch(val) [86][155/155] acc/top1: 0.6690 acc/top5: 0.8736 acc/mean1: 0.6690 data_time: 0.4193 time: 0.4514 2023/04/14 14:03:26 - mmengine - INFO - Epoch(train) [87][ 20/1879] lr: 2.0000e-04 eta: 2:42:05 time: 0.5368 data_time: 0.1866 memory: 6717 grad_norm: 3.3831 loss: 1.0875 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0875 2023/04/14 14:03:32 - mmengine - INFO - Epoch(train) [87][ 40/1879] lr: 2.0000e-04 eta: 2:41:58 time: 0.3033 data_time: 0.0126 memory: 6717 grad_norm: 3.5005 loss: 1.2562 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2562 2023/04/14 14:03:41 - mmengine - INFO - Epoch(train) [87][ 60/1879] lr: 2.0000e-04 eta: 2:41:50 time: 0.4188 data_time: 0.0210 memory: 6717 grad_norm: 3.3873 loss: 1.1542 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1542 2023/04/14 14:03:48 - mmengine - INFO - Epoch(train) [87][ 80/1879] lr: 2.0000e-04 eta: 2:41:43 time: 0.3507 data_time: 0.0722 memory: 6717 grad_norm: 3.3764 loss: 1.0811 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0811 2023/04/14 14:03:56 - mmengine - INFO - Epoch(train) [87][ 100/1879] lr: 2.0000e-04 eta: 2:41:36 time: 0.4003 data_time: 0.1417 memory: 6717 grad_norm: 3.4985 loss: 1.0664 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0664 2023/04/14 14:04:02 - mmengine - INFO - Epoch(train) [87][ 120/1879] lr: 2.0000e-04 eta: 2:41:28 time: 0.3388 data_time: 0.0834 memory: 6717 grad_norm: 3.4423 loss: 1.1379 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1379 2023/04/14 14:04:10 - mmengine - INFO - Epoch(train) [87][ 140/1879] lr: 2.0000e-04 eta: 2:41:21 time: 0.3981 data_time: 0.1205 memory: 6717 grad_norm: 3.3707 loss: 0.9758 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9758 2023/04/14 14:04:17 - mmengine - INFO - Epoch(train) [87][ 160/1879] lr: 2.0000e-04 eta: 2:41:13 time: 0.3270 data_time: 0.0124 memory: 6717 grad_norm: 3.4397 loss: 0.9742 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9742 2023/04/14 14:04:25 - mmengine - INFO - Epoch(train) [87][ 180/1879] lr: 2.0000e-04 eta: 2:41:06 time: 0.3978 data_time: 0.0161 memory: 6717 grad_norm: 3.3731 loss: 1.1133 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1133 2023/04/14 14:04:31 - mmengine - INFO - Epoch(train) [87][ 200/1879] lr: 2.0000e-04 eta: 2:40:58 time: 0.3290 data_time: 0.0159 memory: 6717 grad_norm: 3.6684 loss: 1.1905 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1905 2023/04/14 14:04:39 - mmengine - INFO - Epoch(train) [87][ 220/1879] lr: 2.0000e-04 eta: 2:40:51 time: 0.3944 data_time: 0.0168 memory: 6717 grad_norm: 3.4785 loss: 1.2697 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2697 2023/04/14 14:04:46 - mmengine - INFO - Epoch(train) [87][ 240/1879] lr: 2.0000e-04 eta: 2:40:43 time: 0.3279 data_time: 0.0131 memory: 6717 grad_norm: 3.4618 loss: 1.2643 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2643 2023/04/14 14:04:54 - mmengine - INFO - Epoch(train) [87][ 260/1879] lr: 2.0000e-04 eta: 2:40:36 time: 0.4144 data_time: 0.0146 memory: 6717 grad_norm: 3.4707 loss: 1.0998 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0998 2023/04/14 14:05:01 - mmengine - INFO - Epoch(train) [87][ 280/1879] lr: 2.0000e-04 eta: 2:40:29 time: 0.3208 data_time: 0.0139 memory: 6717 grad_norm: 3.2949 loss: 1.2390 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2390 2023/04/14 14:05:09 - mmengine - INFO - Epoch(train) [87][ 300/1879] lr: 2.0000e-04 eta: 2:40:21 time: 0.4067 data_time: 0.0150 memory: 6717 grad_norm: 3.4113 loss: 0.9292 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 0.9292 2023/04/14 14:05:16 - mmengine - INFO - Epoch(train) [87][ 320/1879] lr: 2.0000e-04 eta: 2:40:14 time: 0.3420 data_time: 0.0134 memory: 6717 grad_norm: 3.3771 loss: 1.1777 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1777 2023/04/14 14:05:23 - mmengine - INFO - Epoch(train) [87][ 340/1879] lr: 2.0000e-04 eta: 2:40:07 time: 0.3906 data_time: 0.0160 memory: 6717 grad_norm: 3.4425 loss: 1.1637 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1637 2023/04/14 14:05:30 - mmengine - INFO - Epoch(train) [87][ 360/1879] lr: 2.0000e-04 eta: 2:39:59 time: 0.3142 data_time: 0.0130 memory: 6717 grad_norm: 3.5639 loss: 1.0297 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0297 2023/04/14 14:05:38 - mmengine - INFO - Epoch(train) [87][ 380/1879] lr: 2.0000e-04 eta: 2:39:52 time: 0.4187 data_time: 0.0137 memory: 6717 grad_norm: 3.3623 loss: 1.3250 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3250 2023/04/14 14:05:45 - mmengine - INFO - Epoch(train) [87][ 400/1879] lr: 2.0000e-04 eta: 2:39:44 time: 0.3329 data_time: 0.0147 memory: 6717 grad_norm: 3.4130 loss: 0.9704 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9704 2023/04/14 14:05:47 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 14:05:53 - mmengine - INFO - Epoch(train) [87][ 420/1879] lr: 2.0000e-04 eta: 2:39:37 time: 0.4236 data_time: 0.0135 memory: 6717 grad_norm: 3.4897 loss: 1.0892 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0892 2023/04/14 14:06:00 - mmengine - INFO - Epoch(train) [87][ 440/1879] lr: 2.0000e-04 eta: 2:39:30 time: 0.3461 data_time: 0.0137 memory: 6717 grad_norm: 3.4127 loss: 1.1308 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1308 2023/04/14 14:06:08 - mmengine - INFO - Epoch(train) [87][ 460/1879] lr: 2.0000e-04 eta: 2:39:22 time: 0.3891 data_time: 0.0150 memory: 6717 grad_norm: 3.4873 loss: 1.1803 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1803 2023/04/14 14:06:14 - mmengine - INFO - Epoch(train) [87][ 480/1879] lr: 2.0000e-04 eta: 2:39:15 time: 0.3103 data_time: 0.0153 memory: 6717 grad_norm: 3.4510 loss: 1.0632 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0632 2023/04/14 14:06:22 - mmengine - INFO - Epoch(train) [87][ 500/1879] lr: 2.0000e-04 eta: 2:39:07 time: 0.3978 data_time: 0.0151 memory: 6717 grad_norm: 3.4108 loss: 0.9527 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9527 2023/04/14 14:06:29 - mmengine - INFO - Epoch(train) [87][ 520/1879] lr: 2.0000e-04 eta: 2:39:00 time: 0.3371 data_time: 0.0144 memory: 6717 grad_norm: 3.4745 loss: 0.9501 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9501 2023/04/14 14:06:37 - mmengine - INFO - Epoch(train) [87][ 540/1879] lr: 2.0000e-04 eta: 2:38:52 time: 0.3874 data_time: 0.0141 memory: 6717 grad_norm: 3.4456 loss: 1.1171 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1171 2023/04/14 14:06:44 - mmengine - INFO - Epoch(train) [87][ 560/1879] lr: 2.0000e-04 eta: 2:38:45 time: 0.3649 data_time: 0.0152 memory: 6717 grad_norm: 3.4165 loss: 1.2207 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2207 2023/04/14 14:06:51 - mmengine - INFO - Epoch(train) [87][ 580/1879] lr: 2.0000e-04 eta: 2:38:38 time: 0.3769 data_time: 0.0141 memory: 6717 grad_norm: 3.4180 loss: 1.2273 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2273 2023/04/14 14:06:59 - mmengine - INFO - Epoch(train) [87][ 600/1879] lr: 2.0000e-04 eta: 2:38:30 time: 0.3671 data_time: 0.0164 memory: 6717 grad_norm: 3.3644 loss: 1.1521 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1521 2023/04/14 14:07:06 - mmengine - INFO - Epoch(train) [87][ 620/1879] lr: 2.0000e-04 eta: 2:38:23 time: 0.3446 data_time: 0.0131 memory: 6717 grad_norm: 3.4425 loss: 1.0534 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0534 2023/04/14 14:07:14 - mmengine - INFO - Epoch(train) [87][ 640/1879] lr: 2.0000e-04 eta: 2:38:16 time: 0.4344 data_time: 0.0161 memory: 6717 grad_norm: 3.4707 loss: 1.1768 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1768 2023/04/14 14:07:21 - mmengine - INFO - Epoch(train) [87][ 660/1879] lr: 2.0000e-04 eta: 2:38:08 time: 0.3340 data_time: 0.0124 memory: 6717 grad_norm: 3.4390 loss: 1.2924 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2924 2023/04/14 14:07:29 - mmengine - INFO - Epoch(train) [87][ 680/1879] lr: 2.0000e-04 eta: 2:38:01 time: 0.3821 data_time: 0.0950 memory: 6717 grad_norm: 3.4375 loss: 1.0625 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0625 2023/04/14 14:07:35 - mmengine - INFO - Epoch(train) [87][ 700/1879] lr: 2.0000e-04 eta: 2:37:53 time: 0.3335 data_time: 0.0943 memory: 6717 grad_norm: 3.5064 loss: 1.1854 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1854 2023/04/14 14:07:44 - mmengine - INFO - Epoch(train) [87][ 720/1879] lr: 2.0000e-04 eta: 2:37:46 time: 0.4078 data_time: 0.1328 memory: 6717 grad_norm: 3.4513 loss: 1.1066 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1066 2023/04/14 14:07:50 - mmengine - INFO - Epoch(train) [87][ 740/1879] lr: 2.0000e-04 eta: 2:37:38 time: 0.3123 data_time: 0.0243 memory: 6717 grad_norm: 3.4044 loss: 1.1603 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1603 2023/04/14 14:07:59 - mmengine - INFO - Epoch(train) [87][ 760/1879] lr: 2.0000e-04 eta: 2:37:31 time: 0.4360 data_time: 0.0357 memory: 6717 grad_norm: 3.3851 loss: 1.0893 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0893 2023/04/14 14:08:05 - mmengine - INFO - Epoch(train) [87][ 780/1879] lr: 2.0000e-04 eta: 2:37:24 time: 0.3177 data_time: 0.0130 memory: 6717 grad_norm: 3.3986 loss: 1.1885 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1885 2023/04/14 14:08:16 - mmengine - INFO - Epoch(train) [87][ 800/1879] lr: 2.0000e-04 eta: 2:37:17 time: 0.5611 data_time: 0.1141 memory: 6717 grad_norm: 3.3773 loss: 1.1243 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1243 2023/04/14 14:08:23 - mmengine - INFO - Epoch(train) [87][ 820/1879] lr: 2.0000e-04 eta: 2:37:09 time: 0.3588 data_time: 0.1217 memory: 6717 grad_norm: 3.4191 loss: 1.1101 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1101 2023/04/14 14:08:31 - mmengine - INFO - Epoch(train) [87][ 840/1879] lr: 2.0000e-04 eta: 2:37:02 time: 0.3807 data_time: 0.0565 memory: 6717 grad_norm: 3.4222 loss: 1.1378 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1378 2023/04/14 14:08:39 - mmengine - INFO - Epoch(train) [87][ 860/1879] lr: 2.0000e-04 eta: 2:36:55 time: 0.4183 data_time: 0.0132 memory: 6717 grad_norm: 3.3843 loss: 1.1987 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1987 2023/04/14 14:08:46 - mmengine - INFO - Epoch(train) [87][ 880/1879] lr: 2.0000e-04 eta: 2:36:47 time: 0.3461 data_time: 0.0167 memory: 6717 grad_norm: 3.4872 loss: 1.0325 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0325 2023/04/14 14:08:55 - mmengine - INFO - Epoch(train) [87][ 900/1879] lr: 2.0000e-04 eta: 2:36:40 time: 0.4169 data_time: 0.0140 memory: 6717 grad_norm: 3.4362 loss: 1.0602 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0602 2023/04/14 14:09:01 - mmengine - INFO - Epoch(train) [87][ 920/1879] lr: 2.0000e-04 eta: 2:36:32 time: 0.2992 data_time: 0.0228 memory: 6717 grad_norm: 3.4948 loss: 1.1768 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1768 2023/04/14 14:09:09 - mmengine - INFO - Epoch(train) [87][ 940/1879] lr: 2.0000e-04 eta: 2:36:25 time: 0.4083 data_time: 0.0137 memory: 6717 grad_norm: 3.3166 loss: 1.1251 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1251 2023/04/14 14:09:15 - mmengine - INFO - Epoch(train) [87][ 960/1879] lr: 2.0000e-04 eta: 2:36:18 time: 0.3269 data_time: 0.0337 memory: 6717 grad_norm: 3.4550 loss: 1.1166 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1166 2023/04/14 14:09:23 - mmengine - INFO - Epoch(train) [87][ 980/1879] lr: 2.0000e-04 eta: 2:36:10 time: 0.4068 data_time: 0.0139 memory: 6717 grad_norm: 3.4834 loss: 1.1905 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1905 2023/04/14 14:09:30 - mmengine - INFO - Epoch(train) [87][1000/1879] lr: 2.0000e-04 eta: 2:36:03 time: 0.3137 data_time: 0.0246 memory: 6717 grad_norm: 3.3876 loss: 1.1051 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1051 2023/04/14 14:09:38 - mmengine - INFO - Epoch(train) [87][1020/1879] lr: 2.0000e-04 eta: 2:35:55 time: 0.4110 data_time: 0.0681 memory: 6717 grad_norm: 3.3124 loss: 1.0896 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0896 2023/04/14 14:09:45 - mmengine - INFO - Epoch(train) [87][1040/1879] lr: 2.0000e-04 eta: 2:35:48 time: 0.3326 data_time: 0.0334 memory: 6717 grad_norm: 3.3238 loss: 1.0154 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0154 2023/04/14 14:09:53 - mmengine - INFO - Epoch(train) [87][1060/1879] lr: 2.0000e-04 eta: 2:35:41 time: 0.4176 data_time: 0.0129 memory: 6717 grad_norm: 3.4669 loss: 1.1228 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1228 2023/04/14 14:10:00 - mmengine - INFO - Epoch(train) [87][1080/1879] lr: 2.0000e-04 eta: 2:35:33 time: 0.3278 data_time: 0.0458 memory: 6717 grad_norm: 3.5102 loss: 1.0700 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0700 2023/04/14 14:10:07 - mmengine - INFO - Epoch(train) [87][1100/1879] lr: 2.0000e-04 eta: 2:35:26 time: 0.3744 data_time: 0.1563 memory: 6717 grad_norm: 3.4206 loss: 1.2660 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2660 2023/04/14 14:10:15 - mmengine - INFO - Epoch(train) [87][1120/1879] lr: 2.0000e-04 eta: 2:35:18 time: 0.3889 data_time: 0.2326 memory: 6717 grad_norm: 3.4659 loss: 1.1714 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1714 2023/04/14 14:10:21 - mmengine - INFO - Epoch(train) [87][1140/1879] lr: 2.0000e-04 eta: 2:35:11 time: 0.3299 data_time: 0.1478 memory: 6717 grad_norm: 3.4055 loss: 1.2845 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2845 2023/04/14 14:10:29 - mmengine - INFO - Epoch(train) [87][1160/1879] lr: 2.0000e-04 eta: 2:35:04 time: 0.3914 data_time: 0.2389 memory: 6717 grad_norm: 3.4194 loss: 1.2276 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2276 2023/04/14 14:10:36 - mmengine - INFO - Epoch(train) [87][1180/1879] lr: 2.0000e-04 eta: 2:34:56 time: 0.3318 data_time: 0.1692 memory: 6717 grad_norm: 3.4280 loss: 1.1371 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1371 2023/04/14 14:10:45 - mmengine - INFO - Epoch(train) [87][1200/1879] lr: 2.0000e-04 eta: 2:34:49 time: 0.4756 data_time: 0.3309 memory: 6717 grad_norm: 3.4704 loss: 1.1495 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1495 2023/04/14 14:10:52 - mmengine - INFO - Epoch(train) [87][1220/1879] lr: 2.0000e-04 eta: 2:34:41 time: 0.3129 data_time: 0.1681 memory: 6717 grad_norm: 3.4635 loss: 1.2459 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2459 2023/04/14 14:11:00 - mmengine - INFO - Epoch(train) [87][1240/1879] lr: 2.0000e-04 eta: 2:34:34 time: 0.4200 data_time: 0.2721 memory: 6717 grad_norm: 3.4372 loss: 1.1029 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1029 2023/04/14 14:11:06 - mmengine - INFO - Epoch(train) [87][1260/1879] lr: 2.0000e-04 eta: 2:34:26 time: 0.2965 data_time: 0.1522 memory: 6717 grad_norm: 3.4594 loss: 1.3322 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3322 2023/04/14 14:11:14 - mmengine - INFO - Epoch(train) [87][1280/1879] lr: 2.0000e-04 eta: 2:34:19 time: 0.4132 data_time: 0.2653 memory: 6717 grad_norm: 3.4075 loss: 1.0067 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0067 2023/04/14 14:11:20 - mmengine - INFO - Epoch(train) [87][1300/1879] lr: 2.0000e-04 eta: 2:34:12 time: 0.3017 data_time: 0.1565 memory: 6717 grad_norm: 3.3960 loss: 1.1149 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1149 2023/04/14 14:11:28 - mmengine - INFO - Epoch(train) [87][1320/1879] lr: 2.0000e-04 eta: 2:34:04 time: 0.3993 data_time: 0.2521 memory: 6717 grad_norm: 3.4232 loss: 1.0460 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0460 2023/04/14 14:11:36 - mmengine - INFO - Epoch(train) [87][1340/1879] lr: 2.0000e-04 eta: 2:33:57 time: 0.3624 data_time: 0.0996 memory: 6717 grad_norm: 3.3901 loss: 1.0661 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0661 2023/04/14 14:11:43 - mmengine - INFO - Epoch(train) [87][1360/1879] lr: 2.0000e-04 eta: 2:33:50 time: 0.3885 data_time: 0.1886 memory: 6717 grad_norm: 3.4206 loss: 1.1218 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1218 2023/04/14 14:11:50 - mmengine - INFO - Epoch(train) [87][1380/1879] lr: 2.0000e-04 eta: 2:33:42 time: 0.3422 data_time: 0.1162 memory: 6717 grad_norm: 3.4073 loss: 1.0683 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0683 2023/04/14 14:11:57 - mmengine - INFO - Epoch(train) [87][1400/1879] lr: 2.0000e-04 eta: 2:33:35 time: 0.3657 data_time: 0.0971 memory: 6717 grad_norm: 3.3032 loss: 1.0597 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0597 2023/04/14 14:12:00 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 14:12:05 - mmengine - INFO - Epoch(train) [87][1420/1879] lr: 2.0000e-04 eta: 2:33:27 time: 0.3569 data_time: 0.0770 memory: 6717 grad_norm: 3.3484 loss: 1.1659 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1659 2023/04/14 14:12:13 - mmengine - INFO - Epoch(train) [87][1440/1879] lr: 2.0000e-04 eta: 2:33:20 time: 0.4297 data_time: 0.0764 memory: 6717 grad_norm: 3.3935 loss: 1.1853 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1853 2023/04/14 14:12:20 - mmengine - INFO - Epoch(train) [87][1460/1879] lr: 2.0000e-04 eta: 2:33:12 time: 0.3244 data_time: 0.0136 memory: 6717 grad_norm: 3.5240 loss: 1.2474 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2474 2023/04/14 14:12:28 - mmengine - INFO - Epoch(train) [87][1480/1879] lr: 2.0000e-04 eta: 2:33:05 time: 0.4190 data_time: 0.0137 memory: 6717 grad_norm: 3.3515 loss: 1.0865 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0865 2023/04/14 14:12:35 - mmengine - INFO - Epoch(train) [87][1500/1879] lr: 2.0000e-04 eta: 2:32:58 time: 0.3194 data_time: 0.0138 memory: 6717 grad_norm: 3.3791 loss: 1.0319 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0319 2023/04/14 14:12:42 - mmengine - INFO - Epoch(train) [87][1520/1879] lr: 2.0000e-04 eta: 2:32:50 time: 0.3972 data_time: 0.0145 memory: 6717 grad_norm: 3.4166 loss: 1.1267 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1267 2023/04/14 14:12:49 - mmengine - INFO - Epoch(train) [87][1540/1879] lr: 2.0000e-04 eta: 2:32:43 time: 0.3461 data_time: 0.0143 memory: 6717 grad_norm: 3.3847 loss: 1.1309 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1309 2023/04/14 14:12:58 - mmengine - INFO - Epoch(train) [87][1560/1879] lr: 2.0000e-04 eta: 2:32:36 time: 0.4169 data_time: 0.0137 memory: 6717 grad_norm: 3.4657 loss: 1.2259 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2259 2023/04/14 14:13:04 - mmengine - INFO - Epoch(train) [87][1580/1879] lr: 2.0000e-04 eta: 2:32:28 time: 0.2965 data_time: 0.0154 memory: 6717 grad_norm: 3.4911 loss: 1.1623 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1623 2023/04/14 14:13:12 - mmengine - INFO - Epoch(train) [87][1600/1879] lr: 2.0000e-04 eta: 2:32:21 time: 0.4239 data_time: 0.0156 memory: 6717 grad_norm: 3.3926 loss: 1.0241 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.0241 2023/04/14 14:13:19 - mmengine - INFO - Epoch(train) [87][1620/1879] lr: 2.0000e-04 eta: 2:32:13 time: 0.3230 data_time: 0.0138 memory: 6717 grad_norm: 3.5012 loss: 1.2349 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2349 2023/04/14 14:13:27 - mmengine - INFO - Epoch(train) [87][1640/1879] lr: 2.0000e-04 eta: 2:32:06 time: 0.4222 data_time: 0.0156 memory: 6717 grad_norm: 3.4256 loss: 1.3454 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3454 2023/04/14 14:13:34 - mmengine - INFO - Epoch(train) [87][1660/1879] lr: 2.0000e-04 eta: 2:31:58 time: 0.3338 data_time: 0.0137 memory: 6717 grad_norm: 3.3481 loss: 1.1517 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1517 2023/04/14 14:13:42 - mmengine - INFO - Epoch(train) [87][1680/1879] lr: 2.0000e-04 eta: 2:31:51 time: 0.4001 data_time: 0.0145 memory: 6717 grad_norm: 3.4148 loss: 1.1157 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1157 2023/04/14 14:13:48 - mmengine - INFO - Epoch(train) [87][1700/1879] lr: 2.0000e-04 eta: 2:31:44 time: 0.3135 data_time: 0.0152 memory: 6717 grad_norm: 3.4298 loss: 1.1214 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1214 2023/04/14 14:13:56 - mmengine - INFO - Epoch(train) [87][1720/1879] lr: 2.0000e-04 eta: 2:31:36 time: 0.4015 data_time: 0.0137 memory: 6717 grad_norm: 3.2417 loss: 1.0708 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0708 2023/04/14 14:14:03 - mmengine - INFO - Epoch(train) [87][1740/1879] lr: 2.0000e-04 eta: 2:31:29 time: 0.3334 data_time: 0.0148 memory: 6717 grad_norm: 3.5213 loss: 0.9895 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9895 2023/04/14 14:14:11 - mmengine - INFO - Epoch(train) [87][1760/1879] lr: 2.0000e-04 eta: 2:31:21 time: 0.4022 data_time: 0.0145 memory: 6717 grad_norm: 3.3583 loss: 1.2279 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2279 2023/04/14 14:14:18 - mmengine - INFO - Epoch(train) [87][1780/1879] lr: 2.0000e-04 eta: 2:31:14 time: 0.3352 data_time: 0.0158 memory: 6717 grad_norm: 3.3916 loss: 1.0299 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0299 2023/04/14 14:14:26 - mmengine - INFO - Epoch(train) [87][1800/1879] lr: 2.0000e-04 eta: 2:31:07 time: 0.4094 data_time: 0.0154 memory: 6717 grad_norm: 3.4318 loss: 1.0800 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0800 2023/04/14 14:14:32 - mmengine - INFO - Epoch(train) [87][1820/1879] lr: 2.0000e-04 eta: 2:30:59 time: 0.3175 data_time: 0.0140 memory: 6717 grad_norm: 3.3932 loss: 1.2453 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2453 2023/04/14 14:14:41 - mmengine - INFO - Epoch(train) [87][1840/1879] lr: 2.0000e-04 eta: 2:30:52 time: 0.4276 data_time: 0.0156 memory: 6717 grad_norm: 3.3026 loss: 0.9295 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9295 2023/04/14 14:14:47 - mmengine - INFO - Epoch(train) [87][1860/1879] lr: 2.0000e-04 eta: 2:30:44 time: 0.3225 data_time: 0.0436 memory: 6717 grad_norm: 3.4530 loss: 1.2027 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.2027 2023/04/14 14:14:53 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 14:14:53 - mmengine - INFO - Epoch(train) [87][1879/1879] lr: 2.0000e-04 eta: 2:30:37 time: 0.2911 data_time: 0.0111 memory: 6717 grad_norm: 3.5136 loss: 1.1105 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 1.1105 2023/04/14 14:14:53 - mmengine - INFO - Saving checkpoint at 87 epochs 2023/04/14 14:15:03 - mmengine - INFO - Epoch(val) [87][ 20/155] eta: 0:01:02 time: 0.4607 data_time: 0.4273 memory: 1391 2023/04/14 14:15:09 - mmengine - INFO - Epoch(val) [87][ 40/155] eta: 0:00:44 time: 0.3102 data_time: 0.2773 memory: 1391 2023/04/14 14:15:18 - mmengine - INFO - Epoch(val) [87][ 60/155] eta: 0:00:38 time: 0.4316 data_time: 0.3983 memory: 1391 2023/04/14 14:15:24 - mmengine - INFO - Epoch(val) [87][ 80/155] eta: 0:00:28 time: 0.3223 data_time: 0.2894 memory: 1391 2023/04/14 14:15:32 - mmengine - INFO - Epoch(val) [87][100/155] eta: 0:00:21 time: 0.4174 data_time: 0.3843 memory: 1391 2023/04/14 14:15:39 - mmengine - INFO - Epoch(val) [87][120/155] eta: 0:00:13 time: 0.3374 data_time: 0.3039 memory: 1391 2023/04/14 14:15:49 - mmengine - INFO - Epoch(val) [87][140/155] eta: 0:00:05 time: 0.4832 data_time: 0.4499 memory: 1391 2023/04/14 14:15:56 - mmengine - INFO - Epoch(val) [87][155/155] acc/top1: 0.6687 acc/top5: 0.8739 acc/mean1: 0.6687 data_time: 0.4167 time: 0.4487 2023/04/14 14:16:06 - mmengine - INFO - Epoch(train) [88][ 20/1879] lr: 2.0000e-04 eta: 2:30:30 time: 0.4920 data_time: 0.3044 memory: 6717 grad_norm: 3.4602 loss: 1.2062 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2062 2023/04/14 14:16:12 - mmengine - INFO - Epoch(train) [88][ 40/1879] lr: 2.0000e-04 eta: 2:30:23 time: 0.3256 data_time: 0.1274 memory: 6717 grad_norm: 3.4525 loss: 1.0136 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0136 2023/04/14 14:16:21 - mmengine - INFO - Epoch(train) [88][ 60/1879] lr: 2.0000e-04 eta: 2:30:15 time: 0.4048 data_time: 0.2569 memory: 6717 grad_norm: 3.3802 loss: 1.1042 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1042 2023/04/14 14:16:27 - mmengine - INFO - Epoch(train) [88][ 80/1879] lr: 2.0000e-04 eta: 2:30:08 time: 0.3266 data_time: 0.1901 memory: 6717 grad_norm: 3.4976 loss: 1.3120 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.3120 2023/04/14 14:16:36 - mmengine - INFO - Epoch(train) [88][ 100/1879] lr: 2.0000e-04 eta: 2:30:01 time: 0.4475 data_time: 0.3088 memory: 6717 grad_norm: 3.3733 loss: 1.1363 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1363 2023/04/14 14:16:43 - mmengine - INFO - Epoch(train) [88][ 120/1879] lr: 2.0000e-04 eta: 2:29:53 time: 0.3324 data_time: 0.1969 memory: 6717 grad_norm: 3.3544 loss: 1.1743 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1743 2023/04/14 14:16:51 - mmengine - INFO - Epoch(train) [88][ 140/1879] lr: 2.0000e-04 eta: 2:29:46 time: 0.4020 data_time: 0.2619 memory: 6717 grad_norm: 3.5368 loss: 1.0109 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0109 2023/04/14 14:16:57 - mmengine - INFO - Epoch(train) [88][ 160/1879] lr: 2.0000e-04 eta: 2:29:38 time: 0.3181 data_time: 0.1809 memory: 6717 grad_norm: 3.3571 loss: 1.0430 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0430 2023/04/14 14:17:05 - mmengine - INFO - Epoch(train) [88][ 180/1879] lr: 2.0000e-04 eta: 2:29:31 time: 0.4083 data_time: 0.2692 memory: 6717 grad_norm: 3.4187 loss: 1.1431 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1431 2023/04/14 14:17:12 - mmengine - INFO - Epoch(train) [88][ 200/1879] lr: 2.0000e-04 eta: 2:29:23 time: 0.3473 data_time: 0.2074 memory: 6717 grad_norm: 3.4573 loss: 1.1785 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1785 2023/04/14 14:17:20 - mmengine - INFO - Epoch(train) [88][ 220/1879] lr: 2.0000e-04 eta: 2:29:16 time: 0.3997 data_time: 0.2508 memory: 6717 grad_norm: 3.4096 loss: 1.0620 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0620 2023/04/14 14:17:27 - mmengine - INFO - Epoch(train) [88][ 240/1879] lr: 2.0000e-04 eta: 2:29:09 time: 0.3142 data_time: 0.1781 memory: 6717 grad_norm: 3.3342 loss: 1.0262 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0262 2023/04/14 14:17:35 - mmengine - INFO - Epoch(train) [88][ 260/1879] lr: 2.0000e-04 eta: 2:29:01 time: 0.3973 data_time: 0.2251 memory: 6717 grad_norm: 3.4943 loss: 1.0308 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.0308 2023/04/14 14:17:41 - mmengine - INFO - Epoch(train) [88][ 280/1879] lr: 2.0000e-04 eta: 2:28:54 time: 0.3342 data_time: 0.1665 memory: 6717 grad_norm: 3.3474 loss: 1.0075 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0075 2023/04/14 14:17:50 - mmengine - INFO - Epoch(train) [88][ 300/1879] lr: 2.0000e-04 eta: 2:28:46 time: 0.4330 data_time: 0.2426 memory: 6717 grad_norm: 3.4435 loss: 1.0465 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0465 2023/04/14 14:17:57 - mmengine - INFO - Epoch(train) [88][ 320/1879] lr: 2.0000e-04 eta: 2:28:39 time: 0.3331 data_time: 0.0988 memory: 6717 grad_norm: 3.3994 loss: 1.1848 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1848 2023/04/14 14:18:05 - mmengine - INFO - Epoch(train) [88][ 340/1879] lr: 2.0000e-04 eta: 2:28:32 time: 0.4022 data_time: 0.1681 memory: 6717 grad_norm: 3.4622 loss: 1.1954 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1954 2023/04/14 14:18:18 - mmengine - INFO - Epoch(train) [88][ 360/1879] lr: 2.0000e-04 eta: 2:28:25 time: 0.6949 data_time: 0.1286 memory: 6717 grad_norm: 3.4067 loss: 1.0939 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0939 2023/04/14 14:18:26 - mmengine - INFO - Epoch(train) [88][ 380/1879] lr: 2.0000e-04 eta: 2:28:18 time: 0.3965 data_time: 0.0145 memory: 6717 grad_norm: 3.3870 loss: 0.9996 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9996 2023/04/14 14:18:33 - mmengine - INFO - Epoch(train) [88][ 400/1879] lr: 2.0000e-04 eta: 2:28:10 time: 0.3070 data_time: 0.0143 memory: 6717 grad_norm: 3.4050 loss: 1.1025 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1025 2023/04/14 14:18:42 - mmengine - INFO - Epoch(train) [88][ 420/1879] lr: 2.0000e-04 eta: 2:28:03 time: 0.4508 data_time: 0.0142 memory: 6717 grad_norm: 3.4776 loss: 1.1226 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1226 2023/04/14 14:18:48 - mmengine - INFO - Epoch(train) [88][ 440/1879] lr: 2.0000e-04 eta: 2:27:56 time: 0.3089 data_time: 0.0127 memory: 6717 grad_norm: 3.4145 loss: 1.0423 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0423 2023/04/14 14:18:55 - mmengine - INFO - Epoch(train) [88][ 460/1879] lr: 2.0000e-04 eta: 2:27:48 time: 0.3797 data_time: 0.0144 memory: 6717 grad_norm: 3.4753 loss: 0.9029 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9029 2023/04/14 14:19:03 - mmengine - INFO - Epoch(train) [88][ 480/1879] lr: 2.0000e-04 eta: 2:27:41 time: 0.3712 data_time: 0.0136 memory: 6717 grad_norm: 3.4311 loss: 1.0831 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0831 2023/04/14 14:19:11 - mmengine - INFO - Epoch(train) [88][ 500/1879] lr: 2.0000e-04 eta: 2:27:34 time: 0.4127 data_time: 0.0154 memory: 6717 grad_norm: 3.3948 loss: 1.1955 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1955 2023/04/14 14:19:17 - mmengine - INFO - Epoch(train) [88][ 520/1879] lr: 2.0000e-04 eta: 2:27:26 time: 0.2877 data_time: 0.0153 memory: 6717 grad_norm: 3.4040 loss: 1.2268 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2268 2023/04/14 14:19:20 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 14:19:25 - mmengine - INFO - Epoch(train) [88][ 540/1879] lr: 2.0000e-04 eta: 2:27:19 time: 0.3969 data_time: 0.0156 memory: 6717 grad_norm: 3.3749 loss: 1.2164 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2164 2023/04/14 14:19:31 - mmengine - INFO - Epoch(train) [88][ 560/1879] lr: 2.0000e-04 eta: 2:27:11 time: 0.3177 data_time: 0.0127 memory: 6717 grad_norm: 3.4964 loss: 1.2241 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2241 2023/04/14 14:19:40 - mmengine - INFO - Epoch(train) [88][ 580/1879] lr: 2.0000e-04 eta: 2:27:04 time: 0.4253 data_time: 0.0152 memory: 6717 grad_norm: 3.4257 loss: 1.2235 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2235 2023/04/14 14:19:46 - mmengine - INFO - Epoch(train) [88][ 600/1879] lr: 2.0000e-04 eta: 2:26:56 time: 0.3380 data_time: 0.0127 memory: 6717 grad_norm: 3.4581 loss: 1.1089 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.1089 2023/04/14 14:19:54 - mmengine - INFO - Epoch(train) [88][ 620/1879] lr: 2.0000e-04 eta: 2:26:49 time: 0.3841 data_time: 0.0153 memory: 6717 grad_norm: 3.4974 loss: 1.0731 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0731 2023/04/14 14:20:01 - mmengine - INFO - Epoch(train) [88][ 640/1879] lr: 2.0000e-04 eta: 2:26:41 time: 0.3608 data_time: 0.0136 memory: 6717 grad_norm: 3.4365 loss: 1.1910 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1910 2023/04/14 14:20:09 - mmengine - INFO - Epoch(train) [88][ 660/1879] lr: 2.0000e-04 eta: 2:26:34 time: 0.3717 data_time: 0.0146 memory: 6717 grad_norm: 3.4345 loss: 1.1177 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.1177 2023/04/14 14:20:16 - mmengine - INFO - Epoch(train) [88][ 680/1879] lr: 2.0000e-04 eta: 2:26:27 time: 0.3732 data_time: 0.0137 memory: 6717 grad_norm: 3.4784 loss: 1.2264 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2264 2023/04/14 14:20:24 - mmengine - INFO - Epoch(train) [88][ 700/1879] lr: 2.0000e-04 eta: 2:26:19 time: 0.3670 data_time: 0.0141 memory: 6717 grad_norm: 3.4292 loss: 1.1981 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1981 2023/04/14 14:20:31 - mmengine - INFO - Epoch(train) [88][ 720/1879] lr: 2.0000e-04 eta: 2:26:12 time: 0.3662 data_time: 0.0131 memory: 6717 grad_norm: 3.3857 loss: 1.3595 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.3595 2023/04/14 14:20:38 - mmengine - INFO - Epoch(train) [88][ 740/1879] lr: 2.0000e-04 eta: 2:26:04 time: 0.3618 data_time: 0.0150 memory: 6717 grad_norm: 3.3843 loss: 1.0776 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0776 2023/04/14 14:20:45 - mmengine - INFO - Epoch(train) [88][ 760/1879] lr: 2.0000e-04 eta: 2:25:57 time: 0.3497 data_time: 0.0119 memory: 6717 grad_norm: 3.3766 loss: 0.9980 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9980 2023/04/14 14:20:53 - mmengine - INFO - Epoch(train) [88][ 780/1879] lr: 2.0000e-04 eta: 2:25:50 time: 0.3998 data_time: 0.0149 memory: 6717 grad_norm: 3.3929 loss: 1.2050 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2050 2023/04/14 14:21:00 - mmengine - INFO - Epoch(train) [88][ 800/1879] lr: 2.0000e-04 eta: 2:25:42 time: 0.3614 data_time: 0.0137 memory: 6717 grad_norm: 3.4430 loss: 1.0985 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0985 2023/04/14 14:21:08 - mmengine - INFO - Epoch(train) [88][ 820/1879] lr: 2.0000e-04 eta: 2:25:35 time: 0.3848 data_time: 0.0158 memory: 6717 grad_norm: 3.4555 loss: 1.2450 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2450 2023/04/14 14:21:16 - mmengine - INFO - Epoch(train) [88][ 840/1879] lr: 2.0000e-04 eta: 2:25:28 time: 0.3854 data_time: 0.0130 memory: 6717 grad_norm: 3.4817 loss: 1.1830 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1830 2023/04/14 14:21:23 - mmengine - INFO - Epoch(train) [88][ 860/1879] lr: 2.0000e-04 eta: 2:25:20 time: 0.3541 data_time: 0.0153 memory: 6717 grad_norm: 3.4207 loss: 1.1382 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1382 2023/04/14 14:21:30 - mmengine - INFO - Epoch(train) [88][ 880/1879] lr: 2.0000e-04 eta: 2:25:13 time: 0.3557 data_time: 0.0139 memory: 6717 grad_norm: 3.4270 loss: 0.9769 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9769 2023/04/14 14:21:37 - mmengine - INFO - Epoch(train) [88][ 900/1879] lr: 2.0000e-04 eta: 2:25:05 time: 0.3628 data_time: 0.0633 memory: 6717 grad_norm: 3.4228 loss: 1.0012 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0012 2023/04/14 14:21:45 - mmengine - INFO - Epoch(train) [88][ 920/1879] lr: 2.0000e-04 eta: 2:24:58 time: 0.3759 data_time: 0.0315 memory: 6717 grad_norm: 3.4334 loss: 1.1495 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1495 2023/04/14 14:21:51 - mmengine - INFO - Epoch(train) [88][ 940/1879] lr: 2.0000e-04 eta: 2:24:50 time: 0.3185 data_time: 0.0145 memory: 6717 grad_norm: 3.4088 loss: 0.9334 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9334 2023/04/14 14:22:00 - mmengine - INFO - Epoch(train) [88][ 960/1879] lr: 2.0000e-04 eta: 2:24:43 time: 0.4311 data_time: 0.0124 memory: 6717 grad_norm: 3.3275 loss: 1.0319 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0319 2023/04/14 14:22:06 - mmengine - INFO - Epoch(train) [88][ 980/1879] lr: 2.0000e-04 eta: 2:24:35 time: 0.3071 data_time: 0.0154 memory: 6717 grad_norm: 3.3746 loss: 0.9623 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9623 2023/04/14 14:22:14 - mmengine - INFO - Epoch(train) [88][1000/1879] lr: 2.0000e-04 eta: 2:24:28 time: 0.3976 data_time: 0.0139 memory: 6717 grad_norm: 3.3614 loss: 1.0091 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0091 2023/04/14 14:22:21 - mmengine - INFO - Epoch(train) [88][1020/1879] lr: 2.0000e-04 eta: 2:24:21 time: 0.3664 data_time: 0.0151 memory: 6717 grad_norm: 3.4616 loss: 1.1798 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1798 2023/04/14 14:22:28 - mmengine - INFO - Epoch(train) [88][1040/1879] lr: 2.0000e-04 eta: 2:24:13 time: 0.3517 data_time: 0.0138 memory: 6717 grad_norm: 3.4414 loss: 0.9418 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9418 2023/04/14 14:22:37 - mmengine - INFO - Epoch(train) [88][1060/1879] lr: 2.0000e-04 eta: 2:24:06 time: 0.4165 data_time: 0.0137 memory: 6717 grad_norm: 3.3862 loss: 1.2067 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2067 2023/04/14 14:22:43 - mmengine - INFO - Epoch(train) [88][1080/1879] lr: 2.0000e-04 eta: 2:23:58 time: 0.3170 data_time: 0.0156 memory: 6717 grad_norm: 3.4062 loss: 1.1719 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1719 2023/04/14 14:22:51 - mmengine - INFO - Epoch(train) [88][1100/1879] lr: 2.0000e-04 eta: 2:23:51 time: 0.4068 data_time: 0.0147 memory: 6717 grad_norm: 3.3614 loss: 1.1270 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1270 2023/04/14 14:22:58 - mmengine - INFO - Epoch(train) [88][1120/1879] lr: 2.0000e-04 eta: 2:23:44 time: 0.3290 data_time: 0.0150 memory: 6717 grad_norm: 3.4744 loss: 1.0355 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0355 2023/04/14 14:23:05 - mmengine - INFO - Epoch(train) [88][1140/1879] lr: 2.0000e-04 eta: 2:23:36 time: 0.3766 data_time: 0.0142 memory: 6717 grad_norm: 3.4146 loss: 1.0337 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0337 2023/04/14 14:23:12 - mmengine - INFO - Epoch(train) [88][1160/1879] lr: 2.0000e-04 eta: 2:23:29 time: 0.3563 data_time: 0.0138 memory: 6717 grad_norm: 3.4745 loss: 1.0891 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0891 2023/04/14 14:23:20 - mmengine - INFO - Epoch(train) [88][1180/1879] lr: 2.0000e-04 eta: 2:23:21 time: 0.3618 data_time: 0.0158 memory: 6717 grad_norm: 3.3655 loss: 1.1713 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1713 2023/04/14 14:23:27 - mmengine - INFO - Epoch(train) [88][1200/1879] lr: 2.0000e-04 eta: 2:23:14 time: 0.3466 data_time: 0.0149 memory: 6717 grad_norm: 3.3631 loss: 1.0092 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0092 2023/04/14 14:23:34 - mmengine - INFO - Epoch(train) [88][1220/1879] lr: 2.0000e-04 eta: 2:23:07 time: 0.3920 data_time: 0.0292 memory: 6717 grad_norm: 3.4205 loss: 1.0696 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0696 2023/04/14 14:23:42 - mmengine - INFO - Epoch(train) [88][1240/1879] lr: 2.0000e-04 eta: 2:22:59 time: 0.3695 data_time: 0.0164 memory: 6717 grad_norm: 3.4931 loss: 1.0299 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0299 2023/04/14 14:23:49 - mmengine - INFO - Epoch(train) [88][1260/1879] lr: 2.0000e-04 eta: 2:22:52 time: 0.3797 data_time: 0.0134 memory: 6717 grad_norm: 3.4249 loss: 1.2055 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2055 2023/04/14 14:23:57 - mmengine - INFO - Epoch(train) [88][1280/1879] lr: 2.0000e-04 eta: 2:22:44 time: 0.3738 data_time: 0.0163 memory: 6717 grad_norm: 3.5067 loss: 1.0683 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0683 2023/04/14 14:24:04 - mmengine - INFO - Epoch(train) [88][1300/1879] lr: 2.0000e-04 eta: 2:22:37 time: 0.3424 data_time: 0.0363 memory: 6717 grad_norm: 3.4203 loss: 1.0958 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0958 2023/04/14 14:24:13 - mmengine - INFO - Epoch(train) [88][1320/1879] lr: 2.0000e-04 eta: 2:22:30 time: 0.4570 data_time: 0.0427 memory: 6717 grad_norm: 3.3852 loss: 1.1073 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1073 2023/04/14 14:24:20 - mmengine - INFO - Epoch(train) [88][1340/1879] lr: 2.0000e-04 eta: 2:22:22 time: 0.3381 data_time: 0.0117 memory: 6717 grad_norm: 3.3499 loss: 0.9966 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9966 2023/04/14 14:24:27 - mmengine - INFO - Epoch(train) [88][1360/1879] lr: 2.0000e-04 eta: 2:22:15 time: 0.3880 data_time: 0.0140 memory: 6717 grad_norm: 3.3852 loss: 1.0788 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0788 2023/04/14 14:24:34 - mmengine - INFO - Epoch(train) [88][1380/1879] lr: 2.0000e-04 eta: 2:22:07 time: 0.3320 data_time: 0.0158 memory: 6717 grad_norm: 3.4165 loss: 0.9926 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9926 2023/04/14 14:24:42 - mmengine - INFO - Epoch(train) [88][1400/1879] lr: 2.0000e-04 eta: 2:22:00 time: 0.3989 data_time: 0.0149 memory: 6717 grad_norm: 3.3947 loss: 1.1325 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1325 2023/04/14 14:24:49 - mmengine - INFO - Epoch(train) [88][1420/1879] lr: 2.0000e-04 eta: 2:21:53 time: 0.3389 data_time: 0.0144 memory: 6717 grad_norm: 3.4399 loss: 1.0638 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0638 2023/04/14 14:24:56 - mmengine - INFO - Epoch(train) [88][1440/1879] lr: 2.0000e-04 eta: 2:21:45 time: 0.3801 data_time: 0.0147 memory: 6717 grad_norm: 3.3689 loss: 0.9826 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9826 2023/04/14 14:25:03 - mmengine - INFO - Epoch(train) [88][1460/1879] lr: 2.0000e-04 eta: 2:21:38 time: 0.3381 data_time: 0.0142 memory: 6717 grad_norm: 3.3929 loss: 0.8644 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8644 2023/04/14 14:25:11 - mmengine - INFO - Epoch(train) [88][1480/1879] lr: 2.0000e-04 eta: 2:21:30 time: 0.3675 data_time: 0.0149 memory: 6717 grad_norm: 3.4953 loss: 1.1684 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1684 2023/04/14 14:25:18 - mmengine - INFO - Epoch(train) [88][1500/1879] lr: 2.0000e-04 eta: 2:21:23 time: 0.3520 data_time: 0.0135 memory: 6717 grad_norm: 3.4504 loss: 1.2150 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2150 2023/04/14 14:25:26 - mmengine - INFO - Epoch(train) [88][1520/1879] lr: 2.0000e-04 eta: 2:21:16 time: 0.3990 data_time: 0.0137 memory: 6717 grad_norm: 3.4685 loss: 1.1615 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1615 2023/04/14 14:25:29 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 14:25:33 - mmengine - INFO - Epoch(train) [88][1540/1879] lr: 2.0000e-04 eta: 2:21:08 time: 0.3691 data_time: 0.0157 memory: 6717 grad_norm: 3.5863 loss: 1.0730 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0730 2023/04/14 14:25:41 - mmengine - INFO - Epoch(train) [88][1560/1879] lr: 2.0000e-04 eta: 2:21:01 time: 0.4169 data_time: 0.0126 memory: 6717 grad_norm: 3.3530 loss: 1.1438 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1438 2023/04/14 14:25:48 - mmengine - INFO - Epoch(train) [88][1580/1879] lr: 2.0000e-04 eta: 2:20:53 time: 0.3326 data_time: 0.0144 memory: 6717 grad_norm: 3.4517 loss: 1.0556 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0556 2023/04/14 14:25:56 - mmengine - INFO - Epoch(train) [88][1600/1879] lr: 2.0000e-04 eta: 2:20:46 time: 0.4001 data_time: 0.0158 memory: 6717 grad_norm: 3.3180 loss: 0.9337 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9337 2023/04/14 14:26:02 - mmengine - INFO - Epoch(train) [88][1620/1879] lr: 2.0000e-04 eta: 2:20:39 time: 0.3237 data_time: 0.0141 memory: 6717 grad_norm: 3.5108 loss: 1.1815 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1815 2023/04/14 14:26:11 - mmengine - INFO - Epoch(train) [88][1640/1879] lr: 2.0000e-04 eta: 2:20:31 time: 0.4128 data_time: 0.0145 memory: 6717 grad_norm: 3.3791 loss: 1.0323 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0323 2023/04/14 14:26:17 - mmengine - INFO - Epoch(train) [88][1660/1879] lr: 2.0000e-04 eta: 2:20:24 time: 0.3014 data_time: 0.0135 memory: 6717 grad_norm: 3.4243 loss: 1.0838 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0838 2023/04/14 14:26:25 - mmengine - INFO - Epoch(train) [88][1680/1879] lr: 2.0000e-04 eta: 2:20:16 time: 0.4227 data_time: 0.0156 memory: 6717 grad_norm: 3.4675 loss: 1.1895 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1895 2023/04/14 14:26:32 - mmengine - INFO - Epoch(train) [88][1700/1879] lr: 2.0000e-04 eta: 2:20:09 time: 0.3158 data_time: 0.0146 memory: 6717 grad_norm: 3.4832 loss: 1.1865 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1865 2023/04/14 14:26:39 - mmengine - INFO - Epoch(train) [88][1720/1879] lr: 2.0000e-04 eta: 2:20:02 time: 0.3871 data_time: 0.0684 memory: 6717 grad_norm: 3.4594 loss: 1.0390 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0390 2023/04/14 14:26:47 - mmengine - INFO - Epoch(train) [88][1740/1879] lr: 2.0000e-04 eta: 2:19:54 time: 0.3666 data_time: 0.1484 memory: 6717 grad_norm: 3.3319 loss: 1.2432 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2432 2023/04/14 14:26:54 - mmengine - INFO - Epoch(train) [88][1760/1879] lr: 2.0000e-04 eta: 2:19:47 time: 0.3874 data_time: 0.0477 memory: 6717 grad_norm: 3.4489 loss: 1.1376 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1376 2023/04/14 14:27:01 - mmengine - INFO - Epoch(train) [88][1780/1879] lr: 2.0000e-04 eta: 2:19:39 time: 0.3296 data_time: 0.0542 memory: 6717 grad_norm: 3.3536 loss: 1.0551 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0551 2023/04/14 14:27:09 - mmengine - INFO - Epoch(train) [88][1800/1879] lr: 2.0000e-04 eta: 2:19:32 time: 0.4242 data_time: 0.0266 memory: 6717 grad_norm: 3.3874 loss: 1.1499 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1499 2023/04/14 14:27:15 - mmengine - INFO - Epoch(train) [88][1820/1879] lr: 2.0000e-04 eta: 2:19:24 time: 0.2981 data_time: 0.0136 memory: 6717 grad_norm: 3.3616 loss: 0.9983 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9983 2023/04/14 14:27:24 - mmengine - INFO - Epoch(train) [88][1840/1879] lr: 2.0000e-04 eta: 2:19:17 time: 0.4217 data_time: 0.0153 memory: 6717 grad_norm: 3.4172 loss: 1.2061 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2061 2023/04/14 14:27:31 - mmengine - INFO - Epoch(train) [88][1860/1879] lr: 2.0000e-04 eta: 2:19:10 time: 0.3407 data_time: 0.0136 memory: 6717 grad_norm: 3.4813 loss: 1.0653 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0653 2023/04/14 14:27:37 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 14:27:37 - mmengine - INFO - Epoch(train) [88][1879/1879] lr: 2.0000e-04 eta: 2:19:03 time: 0.3044 data_time: 0.0127 memory: 6717 grad_norm: 3.4682 loss: 1.1179 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.1179 2023/04/14 14:27:46 - mmengine - INFO - Epoch(val) [88][ 20/155] eta: 0:01:00 time: 0.4474 data_time: 0.4137 memory: 1391 2023/04/14 14:27:52 - mmengine - INFO - Epoch(val) [88][ 40/155] eta: 0:00:44 time: 0.3317 data_time: 0.2978 memory: 1391 2023/04/14 14:28:01 - mmengine - INFO - Epoch(val) [88][ 60/155] eta: 0:00:38 time: 0.4354 data_time: 0.4018 memory: 1391 2023/04/14 14:28:07 - mmengine - INFO - Epoch(val) [88][ 80/155] eta: 0:00:28 time: 0.3146 data_time: 0.2783 memory: 1391 2023/04/14 14:28:16 - mmengine - INFO - Epoch(val) [88][100/155] eta: 0:00:21 time: 0.4548 data_time: 0.4213 memory: 1391 2023/04/14 14:28:22 - mmengine - INFO - Epoch(val) [88][120/155] eta: 0:00:13 time: 0.2967 data_time: 0.2636 memory: 1391 2023/04/14 14:28:32 - mmengine - INFO - Epoch(val) [88][140/155] eta: 0:00:05 time: 0.4778 data_time: 0.4451 memory: 1391 2023/04/14 14:28:39 - mmengine - INFO - Epoch(val) [88][155/155] acc/top1: 0.6699 acc/top5: 0.8749 acc/mean1: 0.6698 data_time: 0.4199 time: 0.4522 2023/04/14 14:28:39 - mmengine - INFO - The previous best checkpoint /mnt/petrelfs/lilin/Repos/mmact_dev/mmaction2/work_dirs/train_mobilenet_tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb/best_acc_top1_epoch_83.pth is removed 2023/04/14 14:28:40 - mmengine - INFO - The best checkpoint with 0.6699 acc/top1 at 88 epoch is saved to best_acc_top1_epoch_88.pth. 2023/04/14 14:28:49 - mmengine - INFO - Epoch(train) [89][ 20/1879] lr: 2.0000e-04 eta: 2:18:55 time: 0.4684 data_time: 0.3341 memory: 6717 grad_norm: 3.4466 loss: 0.9695 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9695 2023/04/14 14:28:56 - mmengine - INFO - Epoch(train) [89][ 40/1879] lr: 2.0000e-04 eta: 2:18:48 time: 0.3324 data_time: 0.2010 memory: 6717 grad_norm: 3.4052 loss: 1.0072 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0072 2023/04/14 14:29:04 - mmengine - INFO - Epoch(train) [89][ 60/1879] lr: 2.0000e-04 eta: 2:18:41 time: 0.4208 data_time: 0.2893 memory: 6717 grad_norm: 3.3818 loss: 1.0812 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0812 2023/04/14 14:29:11 - mmengine - INFO - Epoch(train) [89][ 80/1879] lr: 2.0000e-04 eta: 2:18:33 time: 0.3403 data_time: 0.2102 memory: 6717 grad_norm: 3.4716 loss: 1.0609 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0609 2023/04/14 14:29:18 - mmengine - INFO - Epoch(train) [89][ 100/1879] lr: 2.0000e-04 eta: 2:18:26 time: 0.3756 data_time: 0.2137 memory: 6717 grad_norm: 3.3626 loss: 0.9743 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9743 2023/04/14 14:29:25 - mmengine - INFO - Epoch(train) [89][ 120/1879] lr: 2.0000e-04 eta: 2:18:18 time: 0.3374 data_time: 0.1113 memory: 6717 grad_norm: 3.4394 loss: 1.0651 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.0651 2023/04/14 14:29:34 - mmengine - INFO - Epoch(train) [89][ 140/1879] lr: 2.0000e-04 eta: 2:18:11 time: 0.4274 data_time: 0.1408 memory: 6717 grad_norm: 3.3794 loss: 1.0412 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0412 2023/04/14 14:29:40 - mmengine - INFO - Epoch(train) [89][ 160/1879] lr: 2.0000e-04 eta: 2:18:04 time: 0.3287 data_time: 0.0798 memory: 6717 grad_norm: 3.4795 loss: 0.9882 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9882 2023/04/14 14:29:48 - mmengine - INFO - Epoch(train) [89][ 180/1879] lr: 2.0000e-04 eta: 2:17:56 time: 0.3777 data_time: 0.0703 memory: 6717 grad_norm: 3.4608 loss: 1.1234 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1234 2023/04/14 14:29:54 - mmengine - INFO - Epoch(train) [89][ 200/1879] lr: 2.0000e-04 eta: 2:17:49 time: 0.3172 data_time: 0.1250 memory: 6717 grad_norm: 3.4567 loss: 1.2229 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2229 2023/04/14 14:30:02 - mmengine - INFO - Epoch(train) [89][ 220/1879] lr: 2.0000e-04 eta: 2:17:41 time: 0.4087 data_time: 0.2116 memory: 6717 grad_norm: 3.3638 loss: 1.0222 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0222 2023/04/14 14:30:09 - mmengine - INFO - Epoch(train) [89][ 240/1879] lr: 2.0000e-04 eta: 2:17:34 time: 0.3234 data_time: 0.0971 memory: 6717 grad_norm: 3.5660 loss: 1.0932 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0932 2023/04/14 14:30:17 - mmengine - INFO - Epoch(train) [89][ 260/1879] lr: 2.0000e-04 eta: 2:17:26 time: 0.4106 data_time: 0.0815 memory: 6717 grad_norm: 3.3900 loss: 1.1080 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1080 2023/04/14 14:30:24 - mmengine - INFO - Epoch(train) [89][ 280/1879] lr: 2.0000e-04 eta: 2:17:19 time: 0.3355 data_time: 0.0406 memory: 6717 grad_norm: 3.4938 loss: 1.0259 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0259 2023/04/14 14:30:32 - mmengine - INFO - Epoch(train) [89][ 300/1879] lr: 2.0000e-04 eta: 2:17:12 time: 0.4189 data_time: 0.0431 memory: 6717 grad_norm: 3.5253 loss: 1.0533 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0533 2023/04/14 14:30:38 - mmengine - INFO - Epoch(train) [89][ 320/1879] lr: 2.0000e-04 eta: 2:17:04 time: 0.3142 data_time: 0.0115 memory: 6717 grad_norm: 3.4220 loss: 1.0960 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0960 2023/04/14 14:30:47 - mmengine - INFO - Epoch(train) [89][ 340/1879] lr: 2.0000e-04 eta: 2:16:57 time: 0.4173 data_time: 0.0160 memory: 6717 grad_norm: 3.4605 loss: 1.0876 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0876 2023/04/14 14:30:53 - mmengine - INFO - Epoch(train) [89][ 360/1879] lr: 2.0000e-04 eta: 2:16:49 time: 0.3185 data_time: 0.0248 memory: 6717 grad_norm: 3.4712 loss: 1.2740 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2740 2023/04/14 14:31:01 - mmengine - INFO - Epoch(train) [89][ 380/1879] lr: 2.0000e-04 eta: 2:16:42 time: 0.4166 data_time: 0.0245 memory: 6717 grad_norm: 3.4088 loss: 1.0061 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0061 2023/04/14 14:31:08 - mmengine - INFO - Epoch(train) [89][ 400/1879] lr: 2.0000e-04 eta: 2:16:35 time: 0.3142 data_time: 0.0244 memory: 6717 grad_norm: 3.4451 loss: 1.1014 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1014 2023/04/14 14:31:16 - mmengine - INFO - Epoch(train) [89][ 420/1879] lr: 2.0000e-04 eta: 2:16:27 time: 0.4313 data_time: 0.0278 memory: 6717 grad_norm: 3.4196 loss: 1.0565 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0565 2023/04/14 14:31:23 - mmengine - INFO - Epoch(train) [89][ 440/1879] lr: 2.0000e-04 eta: 2:16:20 time: 0.3180 data_time: 0.0489 memory: 6717 grad_norm: 3.4815 loss: 1.2139 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2139 2023/04/14 14:31:31 - mmengine - INFO - Epoch(train) [89][ 460/1879] lr: 2.0000e-04 eta: 2:16:13 time: 0.4291 data_time: 0.1998 memory: 6717 grad_norm: 3.5039 loss: 1.1464 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1464 2023/04/14 14:31:38 - mmengine - INFO - Epoch(train) [89][ 480/1879] lr: 2.0000e-04 eta: 2:16:05 time: 0.3476 data_time: 0.1170 memory: 6717 grad_norm: 3.4719 loss: 1.1171 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1171 2023/04/14 14:31:46 - mmengine - INFO - Epoch(train) [89][ 500/1879] lr: 2.0000e-04 eta: 2:15:58 time: 0.3841 data_time: 0.1030 memory: 6717 grad_norm: 3.5236 loss: 1.1465 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1465 2023/04/14 14:31:53 - mmengine - INFO - Epoch(train) [89][ 520/1879] lr: 2.0000e-04 eta: 2:15:50 time: 0.3296 data_time: 0.1027 memory: 6717 grad_norm: 3.4319 loss: 1.1749 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1749 2023/04/14 14:32:01 - mmengine - INFO - Epoch(train) [89][ 540/1879] lr: 2.0000e-04 eta: 2:15:43 time: 0.4292 data_time: 0.1792 memory: 6717 grad_norm: 3.4001 loss: 1.0896 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0896 2023/04/14 14:32:08 - mmengine - INFO - Epoch(train) [89][ 560/1879] lr: 2.0000e-04 eta: 2:15:35 time: 0.3361 data_time: 0.1904 memory: 6717 grad_norm: 3.4376 loss: 1.2271 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2271 2023/04/14 14:32:16 - mmengine - INFO - Epoch(train) [89][ 580/1879] lr: 2.0000e-04 eta: 2:15:28 time: 0.4133 data_time: 0.2345 memory: 6717 grad_norm: 3.3542 loss: 1.1063 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1063 2023/04/14 14:32:22 - mmengine - INFO - Epoch(train) [89][ 600/1879] lr: 2.0000e-04 eta: 2:15:21 time: 0.3095 data_time: 0.1657 memory: 6717 grad_norm: 3.5155 loss: 1.0082 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.0082 2023/04/14 14:32:30 - mmengine - INFO - Epoch(train) [89][ 620/1879] lr: 2.0000e-04 eta: 2:15:13 time: 0.4041 data_time: 0.2613 memory: 6717 grad_norm: 3.4293 loss: 1.1690 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1690 2023/04/14 14:32:36 - mmengine - INFO - Epoch(train) [89][ 640/1879] lr: 2.0000e-04 eta: 2:15:06 time: 0.2932 data_time: 0.1269 memory: 6717 grad_norm: 3.4662 loss: 1.1095 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1095 2023/04/14 14:32:39 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 14:32:44 - mmengine - INFO - Epoch(train) [89][ 660/1879] lr: 2.0000e-04 eta: 2:14:58 time: 0.4066 data_time: 0.1670 memory: 6717 grad_norm: 3.4365 loss: 1.2329 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2329 2023/04/14 14:32:51 - mmengine - INFO - Epoch(train) [89][ 680/1879] lr: 2.0000e-04 eta: 2:14:51 time: 0.3277 data_time: 0.1024 memory: 6717 grad_norm: 3.4190 loss: 1.1233 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1233 2023/04/14 14:32:59 - mmengine - INFO - Epoch(train) [89][ 700/1879] lr: 2.0000e-04 eta: 2:14:44 time: 0.3989 data_time: 0.1728 memory: 6717 grad_norm: 3.5150 loss: 1.0669 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0669 2023/04/14 14:33:06 - mmengine - INFO - Epoch(train) [89][ 720/1879] lr: 2.0000e-04 eta: 2:14:36 time: 0.3592 data_time: 0.0811 memory: 6717 grad_norm: 3.3834 loss: 0.9622 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9622 2023/04/14 14:33:14 - mmengine - INFO - Epoch(train) [89][ 740/1879] lr: 2.0000e-04 eta: 2:14:29 time: 0.3856 data_time: 0.1451 memory: 6717 grad_norm: 3.4375 loss: 1.2916 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2916 2023/04/14 14:33:21 - mmengine - INFO - Epoch(train) [89][ 760/1879] lr: 2.0000e-04 eta: 2:14:21 time: 0.3309 data_time: 0.1001 memory: 6717 grad_norm: 3.4453 loss: 0.8857 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.8857 2023/04/14 14:33:28 - mmengine - INFO - Epoch(train) [89][ 780/1879] lr: 2.0000e-04 eta: 2:14:14 time: 0.3965 data_time: 0.2205 memory: 6717 grad_norm: 3.4327 loss: 0.9989 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9989 2023/04/14 14:33:36 - mmengine - INFO - Epoch(train) [89][ 800/1879] lr: 2.0000e-04 eta: 2:14:07 time: 0.3796 data_time: 0.0872 memory: 6717 grad_norm: 3.4265 loss: 1.1061 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1061 2023/04/14 14:33:43 - mmengine - INFO - Epoch(train) [89][ 820/1879] lr: 2.0000e-04 eta: 2:13:59 time: 0.3691 data_time: 0.0317 memory: 6717 grad_norm: 3.4150 loss: 0.9648 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9648 2023/04/14 14:33:51 - mmengine - INFO - Epoch(train) [89][ 840/1879] lr: 2.0000e-04 eta: 2:13:52 time: 0.3624 data_time: 0.0462 memory: 6717 grad_norm: 3.4127 loss: 1.0360 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0360 2023/04/14 14:33:58 - mmengine - INFO - Epoch(train) [89][ 860/1879] lr: 2.0000e-04 eta: 2:13:44 time: 0.3768 data_time: 0.0593 memory: 6717 grad_norm: 3.3918 loss: 1.1498 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1498 2023/04/14 14:34:06 - mmengine - INFO - Epoch(train) [89][ 880/1879] lr: 2.0000e-04 eta: 2:13:37 time: 0.3745 data_time: 0.0128 memory: 6717 grad_norm: 3.4691 loss: 1.2193 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2193 2023/04/14 14:34:13 - mmengine - INFO - Epoch(train) [89][ 900/1879] lr: 2.0000e-04 eta: 2:13:30 time: 0.3625 data_time: 0.0204 memory: 6717 grad_norm: 3.4099 loss: 1.1511 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1511 2023/04/14 14:34:20 - mmengine - INFO - Epoch(train) [89][ 920/1879] lr: 2.0000e-04 eta: 2:13:22 time: 0.3375 data_time: 0.0253 memory: 6717 grad_norm: 3.4464 loss: 1.0734 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0734 2023/04/14 14:34:28 - mmengine - INFO - Epoch(train) [89][ 940/1879] lr: 2.0000e-04 eta: 2:13:15 time: 0.4135 data_time: 0.0141 memory: 6717 grad_norm: 3.3672 loss: 1.1409 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1409 2023/04/14 14:34:34 - mmengine - INFO - Epoch(train) [89][ 960/1879] lr: 2.0000e-04 eta: 2:13:07 time: 0.3208 data_time: 0.0128 memory: 6717 grad_norm: 3.5378 loss: 1.2503 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2503 2023/04/14 14:34:42 - mmengine - INFO - Epoch(train) [89][ 980/1879] lr: 2.0000e-04 eta: 2:13:00 time: 0.3932 data_time: 0.0718 memory: 6717 grad_norm: 3.4808 loss: 1.0456 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0456 2023/04/14 14:34:49 - mmengine - INFO - Epoch(train) [89][1000/1879] lr: 2.0000e-04 eta: 2:12:52 time: 0.3324 data_time: 0.0500 memory: 6717 grad_norm: 3.4422 loss: 1.2079 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2079 2023/04/14 14:34:57 - mmengine - INFO - Epoch(train) [89][1020/1879] lr: 2.0000e-04 eta: 2:12:45 time: 0.3787 data_time: 0.1636 memory: 6717 grad_norm: 3.4602 loss: 1.1299 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1299 2023/04/14 14:35:04 - mmengine - INFO - Epoch(train) [89][1040/1879] lr: 2.0000e-04 eta: 2:12:38 time: 0.3499 data_time: 0.1643 memory: 6717 grad_norm: 3.4593 loss: 1.1510 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1510 2023/04/14 14:35:12 - mmengine - INFO - Epoch(train) [89][1060/1879] lr: 2.0000e-04 eta: 2:12:30 time: 0.4247 data_time: 0.2183 memory: 6717 grad_norm: 3.4396 loss: 1.1080 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1080 2023/04/14 14:35:19 - mmengine - INFO - Epoch(train) [89][1080/1879] lr: 2.0000e-04 eta: 2:12:23 time: 0.3533 data_time: 0.2100 memory: 6717 grad_norm: 3.3707 loss: 1.1452 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1452 2023/04/14 14:35:27 - mmengine - INFO - Epoch(train) [89][1100/1879] lr: 2.0000e-04 eta: 2:12:16 time: 0.4167 data_time: 0.2773 memory: 6717 grad_norm: 3.4554 loss: 1.1213 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1213 2023/04/14 14:35:35 - mmengine - INFO - Epoch(train) [89][1120/1879] lr: 2.0000e-04 eta: 2:12:08 time: 0.3553 data_time: 0.2119 memory: 6717 grad_norm: 3.4390 loss: 1.0141 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0141 2023/04/14 14:35:43 - mmengine - INFO - Epoch(train) [89][1140/1879] lr: 2.0000e-04 eta: 2:12:01 time: 0.4192 data_time: 0.2784 memory: 6717 grad_norm: 3.3248 loss: 1.0964 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0964 2023/04/14 14:35:50 - mmengine - INFO - Epoch(train) [89][1160/1879] lr: 2.0000e-04 eta: 2:11:53 time: 0.3404 data_time: 0.2010 memory: 6717 grad_norm: 3.4332 loss: 1.1399 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1399 2023/04/14 14:35:58 - mmengine - INFO - Epoch(train) [89][1180/1879] lr: 2.0000e-04 eta: 2:11:46 time: 0.4005 data_time: 0.2593 memory: 6717 grad_norm: 3.3709 loss: 1.1784 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1784 2023/04/14 14:36:04 - mmengine - INFO - Epoch(train) [89][1200/1879] lr: 2.0000e-04 eta: 2:11:39 time: 0.3095 data_time: 0.1652 memory: 6717 grad_norm: 3.4314 loss: 1.0758 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0758 2023/04/14 14:36:13 - mmengine - INFO - Epoch(train) [89][1220/1879] lr: 2.0000e-04 eta: 2:11:31 time: 0.4254 data_time: 0.2825 memory: 6717 grad_norm: 3.5126 loss: 1.2189 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2189 2023/04/14 14:36:20 - mmengine - INFO - Epoch(train) [89][1240/1879] lr: 2.0000e-04 eta: 2:11:24 time: 0.3494 data_time: 0.2089 memory: 6717 grad_norm: 3.4391 loss: 1.1438 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1438 2023/04/14 14:36:27 - mmengine - INFO - Epoch(train) [89][1260/1879] lr: 2.0000e-04 eta: 2:11:16 time: 0.3879 data_time: 0.2465 memory: 6717 grad_norm: 3.3197 loss: 1.0395 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0395 2023/04/14 14:36:33 - mmengine - INFO - Epoch(train) [89][1280/1879] lr: 2.0000e-04 eta: 2:11:09 time: 0.3082 data_time: 0.1691 memory: 6717 grad_norm: 3.3664 loss: 1.0437 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0437 2023/04/14 14:36:42 - mmengine - INFO - Epoch(train) [89][1300/1879] lr: 2.0000e-04 eta: 2:11:02 time: 0.4166 data_time: 0.2772 memory: 6717 grad_norm: 3.3909 loss: 1.0334 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0334 2023/04/14 14:36:48 - mmengine - INFO - Epoch(train) [89][1320/1879] lr: 2.0000e-04 eta: 2:10:54 time: 0.3152 data_time: 0.1743 memory: 6717 grad_norm: 3.3905 loss: 1.1173 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1173 2023/04/14 14:36:56 - mmengine - INFO - Epoch(train) [89][1340/1879] lr: 2.0000e-04 eta: 2:10:47 time: 0.4083 data_time: 0.2671 memory: 6717 grad_norm: 3.4296 loss: 1.0886 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0886 2023/04/14 14:37:03 - mmengine - INFO - Epoch(train) [89][1360/1879] lr: 2.0000e-04 eta: 2:10:39 time: 0.3384 data_time: 0.1159 memory: 6717 grad_norm: 3.3851 loss: 1.0517 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0517 2023/04/14 14:37:11 - mmengine - INFO - Epoch(train) [89][1380/1879] lr: 2.0000e-04 eta: 2:10:32 time: 0.4007 data_time: 0.1284 memory: 6717 grad_norm: 3.4353 loss: 1.0910 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0910 2023/04/14 14:37:18 - mmengine - INFO - Epoch(train) [89][1400/1879] lr: 2.0000e-04 eta: 2:10:24 time: 0.3323 data_time: 0.0925 memory: 6717 grad_norm: 3.3720 loss: 1.1726 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1726 2023/04/14 14:37:26 - mmengine - INFO - Epoch(train) [89][1420/1879] lr: 2.0000e-04 eta: 2:10:17 time: 0.3988 data_time: 0.1874 memory: 6717 grad_norm: 3.4587 loss: 1.0636 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0636 2023/04/14 14:37:32 - mmengine - INFO - Epoch(train) [89][1440/1879] lr: 2.0000e-04 eta: 2:10:10 time: 0.3340 data_time: 0.1732 memory: 6717 grad_norm: 3.5466 loss: 1.1771 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1771 2023/04/14 14:37:40 - mmengine - INFO - Epoch(train) [89][1460/1879] lr: 2.0000e-04 eta: 2:10:02 time: 0.3985 data_time: 0.2239 memory: 6717 grad_norm: 3.3844 loss: 1.0667 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0667 2023/04/14 14:37:47 - mmengine - INFO - Epoch(train) [89][1480/1879] lr: 2.0000e-04 eta: 2:09:55 time: 0.3544 data_time: 0.1463 memory: 6717 grad_norm: 3.4275 loss: 0.9956 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9956 2023/04/14 14:37:55 - mmengine - INFO - Epoch(train) [89][1500/1879] lr: 2.0000e-04 eta: 2:09:48 time: 0.4013 data_time: 0.2292 memory: 6717 grad_norm: 3.4173 loss: 1.2698 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2698 2023/04/14 14:38:03 - mmengine - INFO - Epoch(train) [89][1520/1879] lr: 2.0000e-04 eta: 2:09:40 time: 0.3598 data_time: 0.2173 memory: 6717 grad_norm: 3.4404 loss: 1.0243 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0243 2023/04/14 14:38:11 - mmengine - INFO - Epoch(train) [89][1540/1879] lr: 2.0000e-04 eta: 2:09:33 time: 0.3927 data_time: 0.2521 memory: 6717 grad_norm: 3.5031 loss: 1.0441 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0441 2023/04/14 14:38:17 - mmengine - INFO - Epoch(train) [89][1560/1879] lr: 2.0000e-04 eta: 2:09:25 time: 0.3189 data_time: 0.1779 memory: 6717 grad_norm: 3.4702 loss: 1.1272 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1272 2023/04/14 14:38:25 - mmengine - INFO - Epoch(train) [89][1580/1879] lr: 2.0000e-04 eta: 2:09:18 time: 0.4270 data_time: 0.2840 memory: 6717 grad_norm: 3.3780 loss: 0.9862 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9862 2023/04/14 14:38:32 - mmengine - INFO - Epoch(train) [89][1600/1879] lr: 2.0000e-04 eta: 2:09:11 time: 0.3466 data_time: 0.2044 memory: 6717 grad_norm: 3.3813 loss: 0.9032 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9032 2023/04/14 14:38:40 - mmengine - INFO - Epoch(train) [89][1620/1879] lr: 2.0000e-04 eta: 2:09:03 time: 0.3968 data_time: 0.2584 memory: 6717 grad_norm: 3.4128 loss: 1.1017 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1017 2023/04/14 14:38:50 - mmengine - INFO - Epoch(train) [89][1640/1879] lr: 2.0000e-04 eta: 2:08:56 time: 0.4601 data_time: 0.1822 memory: 6717 grad_norm: 3.4082 loss: 0.9996 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9996 2023/04/14 14:38:52 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 14:38:57 - mmengine - INFO - Epoch(train) [89][1660/1879] lr: 2.0000e-04 eta: 2:08:49 time: 0.3754 data_time: 0.2135 memory: 6717 grad_norm: 3.4101 loss: 0.9534 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9534 2023/04/14 14:39:04 - mmengine - INFO - Epoch(train) [89][1680/1879] lr: 2.0000e-04 eta: 2:08:41 time: 0.3422 data_time: 0.1473 memory: 6717 grad_norm: 3.4638 loss: 1.4437 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.4437 2023/04/14 14:39:12 - mmengine - INFO - Epoch(train) [89][1700/1879] lr: 2.0000e-04 eta: 2:08:34 time: 0.4150 data_time: 0.2645 memory: 6717 grad_norm: 3.4235 loss: 1.0487 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0487 2023/04/14 14:39:18 - mmengine - INFO - Epoch(train) [89][1720/1879] lr: 2.0000e-04 eta: 2:08:26 time: 0.3104 data_time: 0.1643 memory: 6717 grad_norm: 3.4667 loss: 0.9250 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9250 2023/04/14 14:39:26 - mmengine - INFO - Epoch(train) [89][1740/1879] lr: 2.0000e-04 eta: 2:08:19 time: 0.3870 data_time: 0.2349 memory: 6717 grad_norm: 3.5467 loss: 1.0368 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0368 2023/04/14 14:39:33 - mmengine - INFO - Epoch(train) [89][1760/1879] lr: 2.0000e-04 eta: 2:08:12 time: 0.3582 data_time: 0.2075 memory: 6717 grad_norm: 3.4507 loss: 1.1327 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1327 2023/04/14 14:39:41 - mmengine - INFO - Epoch(train) [89][1780/1879] lr: 2.0000e-04 eta: 2:08:04 time: 0.4070 data_time: 0.2677 memory: 6717 grad_norm: 3.3942 loss: 1.1631 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1631 2023/04/14 14:39:48 - mmengine - INFO - Epoch(train) [89][1800/1879] lr: 2.0000e-04 eta: 2:07:57 time: 0.3342 data_time: 0.1922 memory: 6717 grad_norm: 3.3059 loss: 1.0186 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0186 2023/04/14 14:39:56 - mmengine - INFO - Epoch(train) [89][1820/1879] lr: 2.0000e-04 eta: 2:07:49 time: 0.3832 data_time: 0.2419 memory: 6717 grad_norm: 3.4387 loss: 1.1253 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1253 2023/04/14 14:40:02 - mmengine - INFO - Epoch(train) [89][1840/1879] lr: 2.0000e-04 eta: 2:07:42 time: 0.3314 data_time: 0.1913 memory: 6717 grad_norm: 3.3713 loss: 1.0426 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0426 2023/04/14 14:40:11 - mmengine - INFO - Epoch(train) [89][1860/1879] lr: 2.0000e-04 eta: 2:07:35 time: 0.4189 data_time: 0.2758 memory: 6717 grad_norm: 3.3552 loss: 1.1395 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1395 2023/04/14 14:40:17 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 14:40:17 - mmengine - INFO - Epoch(train) [89][1879/1879] lr: 2.0000e-04 eta: 2:07:27 time: 0.3046 data_time: 0.1735 memory: 6717 grad_norm: 3.5011 loss: 1.1193 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 1.1193 2023/04/14 14:40:26 - mmengine - INFO - Epoch(val) [89][ 20/155] eta: 0:01:02 time: 0.4597 data_time: 0.4273 memory: 1391 2023/04/14 14:40:32 - mmengine - INFO - Epoch(val) [89][ 40/155] eta: 0:00:43 time: 0.2956 data_time: 0.2625 memory: 1391 2023/04/14 14:40:41 - mmengine - INFO - Epoch(val) [89][ 60/155] eta: 0:00:38 time: 0.4578 data_time: 0.4244 memory: 1391 2023/04/14 14:40:47 - mmengine - INFO - Epoch(val) [89][ 80/155] eta: 0:00:28 time: 0.3181 data_time: 0.2845 memory: 1391 2023/04/14 14:40:57 - mmengine - INFO - Epoch(val) [89][100/155] eta: 0:00:21 time: 0.4551 data_time: 0.4220 memory: 1391 2023/04/14 14:41:03 - mmengine - INFO - Epoch(val) [89][120/155] eta: 0:00:13 time: 0.2978 data_time: 0.2638 memory: 1391 2023/04/14 14:41:12 - mmengine - INFO - Epoch(val) [89][140/155] eta: 0:00:05 time: 0.4729 data_time: 0.4401 memory: 1391 2023/04/14 14:41:19 - mmengine - INFO - Epoch(val) [89][155/155] acc/top1: 0.6691 acc/top5: 0.8751 acc/mean1: 0.6691 data_time: 0.4218 time: 0.4532 2023/04/14 14:41:29 - mmengine - INFO - Epoch(train) [90][ 20/1879] lr: 2.0000e-04 eta: 2:07:20 time: 0.4841 data_time: 0.3301 memory: 6717 grad_norm: 3.4424 loss: 1.0630 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0630 2023/04/14 14:41:35 - mmengine - INFO - Epoch(train) [90][ 40/1879] lr: 2.0000e-04 eta: 2:07:13 time: 0.3230 data_time: 0.1827 memory: 6717 grad_norm: 3.4249 loss: 1.0661 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0661 2023/04/14 14:41:44 - mmengine - INFO - Epoch(train) [90][ 60/1879] lr: 2.0000e-04 eta: 2:07:06 time: 0.4222 data_time: 0.2757 memory: 6717 grad_norm: 3.5179 loss: 1.0961 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0961 2023/04/14 14:41:50 - mmengine - INFO - Epoch(train) [90][ 80/1879] lr: 2.0000e-04 eta: 2:06:58 time: 0.3179 data_time: 0.1313 memory: 6717 grad_norm: 3.5944 loss: 1.1620 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1620 2023/04/14 14:41:59 - mmengine - INFO - Epoch(train) [90][ 100/1879] lr: 2.0000e-04 eta: 2:06:51 time: 0.4316 data_time: 0.2578 memory: 6717 grad_norm: 3.3497 loss: 1.1758 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1758 2023/04/14 14:42:05 - mmengine - INFO - Epoch(train) [90][ 120/1879] lr: 2.0000e-04 eta: 2:06:43 time: 0.3380 data_time: 0.2012 memory: 6717 grad_norm: 3.5243 loss: 1.0428 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0428 2023/04/14 14:42:14 - mmengine - INFO - Epoch(train) [90][ 140/1879] lr: 2.0000e-04 eta: 2:06:36 time: 0.4415 data_time: 0.2932 memory: 6717 grad_norm: 3.3770 loss: 1.1354 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1354 2023/04/14 14:42:20 - mmengine - INFO - Epoch(train) [90][ 160/1879] lr: 2.0000e-04 eta: 2:06:28 time: 0.3036 data_time: 0.1645 memory: 6717 grad_norm: 3.4064 loss: 0.9993 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9993 2023/04/14 14:42:30 - mmengine - INFO - Epoch(train) [90][ 180/1879] lr: 2.0000e-04 eta: 2:06:21 time: 0.4724 data_time: 0.3364 memory: 6717 grad_norm: 3.4656 loss: 1.0397 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0397 2023/04/14 14:42:37 - mmengine - INFO - Epoch(train) [90][ 200/1879] lr: 2.0000e-04 eta: 2:06:14 time: 0.3514 data_time: 0.2132 memory: 6717 grad_norm: 3.3451 loss: 0.9123 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9123 2023/04/14 14:42:46 - mmengine - INFO - Epoch(train) [90][ 220/1879] lr: 2.0000e-04 eta: 2:06:07 time: 0.4417 data_time: 0.3000 memory: 6717 grad_norm: 3.4040 loss: 1.0792 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0792 2023/04/14 14:42:52 - mmengine - INFO - Epoch(train) [90][ 240/1879] lr: 2.0000e-04 eta: 2:05:59 time: 0.3101 data_time: 0.1703 memory: 6717 grad_norm: 3.3598 loss: 1.1096 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1096 2023/04/14 14:43:00 - mmengine - INFO - Epoch(train) [90][ 260/1879] lr: 2.0000e-04 eta: 2:05:52 time: 0.3955 data_time: 0.2538 memory: 6717 grad_norm: 3.4658 loss: 1.2411 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2411 2023/04/14 14:43:06 - mmengine - INFO - Epoch(train) [90][ 280/1879] lr: 2.0000e-04 eta: 2:05:44 time: 0.3104 data_time: 0.1718 memory: 6717 grad_norm: 3.3691 loss: 1.1258 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1258 2023/04/14 14:43:14 - mmengine - INFO - Epoch(train) [90][ 300/1879] lr: 2.0000e-04 eta: 2:05:37 time: 0.4120 data_time: 0.2713 memory: 6717 grad_norm: 3.4791 loss: 1.3390 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.3390 2023/04/14 14:43:21 - mmengine - INFO - Epoch(train) [90][ 320/1879] lr: 2.0000e-04 eta: 2:05:29 time: 0.3214 data_time: 0.1810 memory: 6717 grad_norm: 3.3750 loss: 1.1686 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1686 2023/04/14 14:43:28 - mmengine - INFO - Epoch(train) [90][ 340/1879] lr: 2.0000e-04 eta: 2:05:22 time: 0.3810 data_time: 0.2380 memory: 6717 grad_norm: 3.4138 loss: 0.8743 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8743 2023/04/14 14:43:35 - mmengine - INFO - Epoch(train) [90][ 360/1879] lr: 2.0000e-04 eta: 2:05:15 time: 0.3218 data_time: 0.1826 memory: 6717 grad_norm: 3.4280 loss: 1.0871 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0871 2023/04/14 14:43:43 - mmengine - INFO - Epoch(train) [90][ 380/1879] lr: 2.0000e-04 eta: 2:05:07 time: 0.4006 data_time: 0.2617 memory: 6717 grad_norm: 3.3072 loss: 1.1284 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1284 2023/04/14 14:43:50 - mmengine - INFO - Epoch(train) [90][ 400/1879] lr: 2.0000e-04 eta: 2:05:00 time: 0.3335 data_time: 0.1771 memory: 6717 grad_norm: 3.3952 loss: 1.0268 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0268 2023/04/14 14:43:57 - mmengine - INFO - Epoch(train) [90][ 420/1879] lr: 2.0000e-04 eta: 2:04:52 time: 0.3990 data_time: 0.2572 memory: 6717 grad_norm: 3.4282 loss: 1.1111 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1111 2023/04/14 14:44:05 - mmengine - INFO - Epoch(train) [90][ 440/1879] lr: 2.0000e-04 eta: 2:04:45 time: 0.3756 data_time: 0.1011 memory: 6717 grad_norm: 3.4786 loss: 1.1220 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1220 2023/04/14 14:44:12 - mmengine - INFO - Epoch(train) [90][ 460/1879] lr: 2.0000e-04 eta: 2:04:38 time: 0.3451 data_time: 0.0735 memory: 6717 grad_norm: 3.5072 loss: 1.1768 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1768 2023/04/14 14:44:20 - mmengine - INFO - Epoch(train) [90][ 480/1879] lr: 2.0000e-04 eta: 2:04:30 time: 0.4121 data_time: 0.0122 memory: 6717 grad_norm: 3.4895 loss: 1.1317 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1317 2023/04/14 14:44:27 - mmengine - INFO - Epoch(train) [90][ 500/1879] lr: 2.0000e-04 eta: 2:04:23 time: 0.3432 data_time: 0.0317 memory: 6717 grad_norm: 3.4300 loss: 1.0509 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0509 2023/04/14 14:44:34 - mmengine - INFO - Epoch(train) [90][ 520/1879] lr: 2.0000e-04 eta: 2:04:15 time: 0.3685 data_time: 0.0117 memory: 6717 grad_norm: 3.4791 loss: 1.1245 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1245 2023/04/14 14:44:42 - mmengine - INFO - Epoch(train) [90][ 540/1879] lr: 2.0000e-04 eta: 2:04:08 time: 0.3599 data_time: 0.0280 memory: 6717 grad_norm: 3.5127 loss: 1.0553 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0553 2023/04/14 14:44:50 - mmengine - INFO - Epoch(train) [90][ 560/1879] lr: 2.0000e-04 eta: 2:04:01 time: 0.3975 data_time: 0.0189 memory: 6717 grad_norm: 3.4627 loss: 1.0678 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0678 2023/04/14 14:44:56 - mmengine - INFO - Epoch(train) [90][ 580/1879] lr: 2.0000e-04 eta: 2:03:53 time: 0.3444 data_time: 0.0167 memory: 6717 grad_norm: 3.4351 loss: 1.1573 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1573 2023/04/14 14:45:04 - mmengine - INFO - Epoch(train) [90][ 600/1879] lr: 2.0000e-04 eta: 2:03:46 time: 0.3898 data_time: 0.0124 memory: 6717 grad_norm: 3.3881 loss: 1.1859 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1859 2023/04/14 14:45:11 - mmengine - INFO - Epoch(train) [90][ 620/1879] lr: 2.0000e-04 eta: 2:03:38 time: 0.3328 data_time: 0.0153 memory: 6717 grad_norm: 3.5123 loss: 1.0749 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0749 2023/04/14 14:45:19 - mmengine - INFO - Epoch(train) [90][ 640/1879] lr: 2.0000e-04 eta: 2:03:31 time: 0.3968 data_time: 0.0153 memory: 6717 grad_norm: 3.5360 loss: 1.1768 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1768 2023/04/14 14:45:25 - mmengine - INFO - Epoch(train) [90][ 660/1879] lr: 2.0000e-04 eta: 2:03:23 time: 0.3162 data_time: 0.0130 memory: 6717 grad_norm: 3.4225 loss: 1.0724 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0724 2023/04/14 14:45:33 - mmengine - INFO - Epoch(train) [90][ 680/1879] lr: 2.0000e-04 eta: 2:03:16 time: 0.4123 data_time: 0.0163 memory: 6717 grad_norm: 3.3960 loss: 1.1211 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1211 2023/04/14 14:45:40 - mmengine - INFO - Epoch(train) [90][ 700/1879] lr: 2.0000e-04 eta: 2:03:09 time: 0.3240 data_time: 0.0124 memory: 6717 grad_norm: 3.4158 loss: 1.1798 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1798 2023/04/14 14:45:48 - mmengine - INFO - Epoch(train) [90][ 720/1879] lr: 2.0000e-04 eta: 2:03:01 time: 0.4223 data_time: 0.0164 memory: 6717 grad_norm: 3.5218 loss: 1.1622 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1622 2023/04/14 14:45:55 - mmengine - INFO - Epoch(train) [90][ 740/1879] lr: 2.0000e-04 eta: 2:02:54 time: 0.3089 data_time: 0.0124 memory: 6717 grad_norm: 3.4004 loss: 1.1995 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1995 2023/04/14 14:46:02 - mmengine - INFO - Epoch(train) [90][ 760/1879] lr: 2.0000e-04 eta: 2:02:46 time: 0.3819 data_time: 0.0153 memory: 6717 grad_norm: 3.3882 loss: 0.9867 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9867 2023/04/14 14:46:05 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 14:46:09 - mmengine - INFO - Epoch(train) [90][ 780/1879] lr: 2.0000e-04 eta: 2:02:39 time: 0.3482 data_time: 0.0128 memory: 6717 grad_norm: 3.4998 loss: 1.1497 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1497 2023/04/14 14:46:17 - mmengine - INFO - Epoch(train) [90][ 800/1879] lr: 2.0000e-04 eta: 2:02:32 time: 0.3857 data_time: 0.0389 memory: 6717 grad_norm: 3.4653 loss: 1.2102 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2102 2023/04/14 14:46:24 - mmengine - INFO - Epoch(train) [90][ 820/1879] lr: 2.0000e-04 eta: 2:02:24 time: 0.3407 data_time: 0.0908 memory: 6717 grad_norm: 3.4063 loss: 1.1139 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1139 2023/04/14 14:46:31 - mmengine - INFO - Epoch(train) [90][ 840/1879] lr: 2.0000e-04 eta: 2:02:17 time: 0.3879 data_time: 0.0774 memory: 6717 grad_norm: 3.3986 loss: 1.0906 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0906 2023/04/14 14:46:39 - mmengine - INFO - Epoch(train) [90][ 860/1879] lr: 2.0000e-04 eta: 2:02:09 time: 0.3683 data_time: 0.0175 memory: 6717 grad_norm: 3.4727 loss: 1.0207 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0207 2023/04/14 14:46:47 - mmengine - INFO - Epoch(train) [90][ 880/1879] lr: 2.0000e-04 eta: 2:02:02 time: 0.4132 data_time: 0.0144 memory: 6717 grad_norm: 3.4483 loss: 1.1695 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1695 2023/04/14 14:46:53 - mmengine - INFO - Epoch(train) [90][ 900/1879] lr: 2.0000e-04 eta: 2:01:55 time: 0.3110 data_time: 0.0132 memory: 6717 grad_norm: 3.4979 loss: 1.1227 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1227 2023/04/14 14:47:02 - mmengine - INFO - Epoch(train) [90][ 920/1879] lr: 2.0000e-04 eta: 2:01:47 time: 0.4148 data_time: 0.0150 memory: 6717 grad_norm: 3.3076 loss: 1.0974 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0974 2023/04/14 14:47:08 - mmengine - INFO - Epoch(train) [90][ 940/1879] lr: 2.0000e-04 eta: 2:01:40 time: 0.3274 data_time: 0.0135 memory: 6717 grad_norm: 3.4556 loss: 0.9883 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 0.9883 2023/04/14 14:47:16 - mmengine - INFO - Epoch(train) [90][ 960/1879] lr: 2.0000e-04 eta: 2:01:32 time: 0.3730 data_time: 0.0148 memory: 6717 grad_norm: 3.3957 loss: 1.0613 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0613 2023/04/14 14:47:23 - mmengine - INFO - Epoch(train) [90][ 980/1879] lr: 2.0000e-04 eta: 2:01:25 time: 0.3883 data_time: 0.0269 memory: 6717 grad_norm: 3.5124 loss: 1.1825 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1825 2023/04/14 14:47:30 - mmengine - INFO - Epoch(train) [90][1000/1879] lr: 2.0000e-04 eta: 2:01:18 time: 0.3402 data_time: 0.0244 memory: 6717 grad_norm: 3.4437 loss: 0.9659 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9659 2023/04/14 14:47:39 - mmengine - INFO - Epoch(train) [90][1020/1879] lr: 2.0000e-04 eta: 2:01:10 time: 0.4260 data_time: 0.0127 memory: 6717 grad_norm: 3.4167 loss: 1.1806 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1806 2023/04/14 14:47:45 - mmengine - INFO - Epoch(train) [90][1040/1879] lr: 2.0000e-04 eta: 2:01:03 time: 0.3179 data_time: 0.0165 memory: 6717 grad_norm: 3.4145 loss: 1.2621 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2621 2023/04/14 14:47:53 - mmengine - INFO - Epoch(train) [90][1060/1879] lr: 2.0000e-04 eta: 2:00:55 time: 0.3963 data_time: 0.0140 memory: 6717 grad_norm: 3.4270 loss: 1.0889 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0889 2023/04/14 14:48:00 - mmengine - INFO - Epoch(train) [90][1080/1879] lr: 2.0000e-04 eta: 2:00:48 time: 0.3323 data_time: 0.0135 memory: 6717 grad_norm: 3.4347 loss: 1.1330 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1330 2023/04/14 14:48:08 - mmengine - INFO - Epoch(train) [90][1100/1879] lr: 2.0000e-04 eta: 2:00:41 time: 0.4163 data_time: 0.0155 memory: 6717 grad_norm: 3.4342 loss: 0.9608 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9608 2023/04/14 14:48:14 - mmengine - INFO - Epoch(train) [90][1120/1879] lr: 2.0000e-04 eta: 2:00:33 time: 0.3116 data_time: 0.0142 memory: 6717 grad_norm: 3.4613 loss: 1.2675 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2675 2023/04/14 14:48:22 - mmengine - INFO - Epoch(train) [90][1140/1879] lr: 2.0000e-04 eta: 2:00:26 time: 0.3989 data_time: 0.0134 memory: 6717 grad_norm: 3.4714 loss: 1.2207 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2207 2023/04/14 14:48:29 - mmengine - INFO - Epoch(train) [90][1160/1879] lr: 2.0000e-04 eta: 2:00:18 time: 0.3129 data_time: 0.0147 memory: 6717 grad_norm: 3.3509 loss: 1.1480 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1480 2023/04/14 14:48:37 - mmengine - INFO - Epoch(train) [90][1180/1879] lr: 2.0000e-04 eta: 2:00:11 time: 0.4104 data_time: 0.0153 memory: 6717 grad_norm: 3.5053 loss: 1.2438 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2438 2023/04/14 14:48:56 - mmengine - INFO - Epoch(train) [90][1200/1879] lr: 2.0000e-04 eta: 2:00:05 time: 0.9661 data_time: 0.0129 memory: 6717 grad_norm: 3.3920 loss: 1.2485 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2485 2023/04/14 14:49:04 - mmengine - INFO - Epoch(train) [90][1220/1879] lr: 2.0000e-04 eta: 1:59:58 time: 0.3973 data_time: 0.0131 memory: 6717 grad_norm: 3.4883 loss: 1.2463 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2463 2023/04/14 14:49:10 - mmengine - INFO - Epoch(train) [90][1240/1879] lr: 2.0000e-04 eta: 1:59:50 time: 0.2951 data_time: 0.0158 memory: 6717 grad_norm: 3.3985 loss: 1.1153 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1153 2023/04/14 14:49:18 - mmengine - INFO - Epoch(train) [90][1260/1879] lr: 2.0000e-04 eta: 1:59:43 time: 0.4161 data_time: 0.0130 memory: 6717 grad_norm: 3.3522 loss: 0.9956 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9956 2023/04/14 14:49:26 - mmengine - INFO - Epoch(train) [90][1280/1879] lr: 2.0000e-04 eta: 1:59:35 time: 0.3658 data_time: 0.0143 memory: 6717 grad_norm: 3.4544 loss: 1.0263 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0263 2023/04/14 14:49:33 - mmengine - INFO - Epoch(train) [90][1300/1879] lr: 2.0000e-04 eta: 1:59:28 time: 0.3827 data_time: 0.0137 memory: 6717 grad_norm: 3.4751 loss: 1.2487 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2487 2023/04/14 14:49:40 - mmengine - INFO - Epoch(train) [90][1320/1879] lr: 2.0000e-04 eta: 1:59:20 time: 0.3258 data_time: 0.0160 memory: 6717 grad_norm: 3.3844 loss: 1.1078 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1078 2023/04/14 14:49:48 - mmengine - INFO - Epoch(train) [90][1340/1879] lr: 2.0000e-04 eta: 1:59:13 time: 0.4321 data_time: 0.0131 memory: 6717 grad_norm: 3.3200 loss: 1.0248 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.0248 2023/04/14 14:49:55 - mmengine - INFO - Epoch(train) [90][1360/1879] lr: 2.0000e-04 eta: 1:59:06 time: 0.3295 data_time: 0.0152 memory: 6717 grad_norm: 3.4526 loss: 1.1496 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1496 2023/04/14 14:50:03 - mmengine - INFO - Epoch(train) [90][1380/1879] lr: 2.0000e-04 eta: 1:58:58 time: 0.3885 data_time: 0.0146 memory: 6717 grad_norm: 3.4000 loss: 1.1152 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1152 2023/04/14 14:50:09 - mmengine - INFO - Epoch(train) [90][1400/1879] lr: 2.0000e-04 eta: 1:58:51 time: 0.3237 data_time: 0.0139 memory: 6717 grad_norm: 3.4247 loss: 1.1293 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1293 2023/04/14 14:50:17 - mmengine - INFO - Epoch(train) [90][1420/1879] lr: 2.0000e-04 eta: 1:58:43 time: 0.3960 data_time: 0.0146 memory: 6717 grad_norm: 3.4349 loss: 1.0263 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0263 2023/04/14 14:50:23 - mmengine - INFO - Epoch(train) [90][1440/1879] lr: 2.0000e-04 eta: 1:58:36 time: 0.3062 data_time: 0.0133 memory: 6717 grad_norm: 3.3802 loss: 0.9979 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9979 2023/04/14 14:50:32 - mmengine - INFO - Epoch(train) [90][1460/1879] lr: 2.0000e-04 eta: 1:58:29 time: 0.4295 data_time: 0.0140 memory: 6717 grad_norm: 3.4684 loss: 1.0477 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0477 2023/04/14 14:50:38 - mmengine - INFO - Epoch(train) [90][1480/1879] lr: 2.0000e-04 eta: 1:58:21 time: 0.3143 data_time: 0.0150 memory: 6717 grad_norm: 3.5493 loss: 1.0377 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0377 2023/04/14 14:50:45 - mmengine - INFO - Epoch(train) [90][1500/1879] lr: 2.0000e-04 eta: 1:58:14 time: 0.3539 data_time: 0.0132 memory: 6717 grad_norm: 3.4318 loss: 1.1532 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1532 2023/04/14 14:50:53 - mmengine - INFO - Epoch(train) [90][1520/1879] lr: 2.0000e-04 eta: 1:58:06 time: 0.3791 data_time: 0.0155 memory: 6717 grad_norm: 3.3218 loss: 1.2276 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2276 2023/04/14 14:51:00 - mmengine - INFO - Epoch(train) [90][1540/1879] lr: 2.0000e-04 eta: 1:57:59 time: 0.3453 data_time: 0.0302 memory: 6717 grad_norm: 3.4336 loss: 1.1025 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1025 2023/04/14 14:51:07 - mmengine - INFO - Epoch(train) [90][1560/1879] lr: 2.0000e-04 eta: 1:57:51 time: 0.3738 data_time: 0.0696 memory: 6717 grad_norm: 3.3285 loss: 1.1743 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1743 2023/04/14 14:51:14 - mmengine - INFO - Epoch(train) [90][1580/1879] lr: 2.0000e-04 eta: 1:57:44 time: 0.3435 data_time: 0.0523 memory: 6717 grad_norm: 3.5021 loss: 1.2214 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2214 2023/04/14 14:51:22 - mmengine - INFO - Epoch(train) [90][1600/1879] lr: 2.0000e-04 eta: 1:57:37 time: 0.3844 data_time: 0.0823 memory: 6717 grad_norm: 3.4064 loss: 1.2184 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2184 2023/04/14 14:51:29 - mmengine - INFO - Epoch(train) [90][1620/1879] lr: 2.0000e-04 eta: 1:57:29 time: 0.3785 data_time: 0.0410 memory: 6717 grad_norm: 3.3522 loss: 1.1604 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1604 2023/04/14 14:51:37 - mmengine - INFO - Epoch(train) [90][1640/1879] lr: 2.0000e-04 eta: 1:57:22 time: 0.3950 data_time: 0.0375 memory: 6717 grad_norm: 3.4559 loss: 1.2143 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2143 2023/04/14 14:51:44 - mmengine - INFO - Epoch(train) [90][1660/1879] lr: 2.0000e-04 eta: 1:57:14 time: 0.3409 data_time: 0.0425 memory: 6717 grad_norm: 3.4827 loss: 1.1168 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1168 2023/04/14 14:51:52 - mmengine - INFO - Epoch(train) [90][1680/1879] lr: 2.0000e-04 eta: 1:57:07 time: 0.3811 data_time: 0.0880 memory: 6717 grad_norm: 3.4712 loss: 1.0420 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0420 2023/04/14 14:52:00 - mmengine - INFO - Epoch(train) [90][1700/1879] lr: 2.0000e-04 eta: 1:57:00 time: 0.3872 data_time: 0.2092 memory: 6717 grad_norm: 3.4919 loss: 1.1262 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1262 2023/04/14 14:52:06 - mmengine - INFO - Epoch(train) [90][1720/1879] lr: 2.0000e-04 eta: 1:56:52 time: 0.3416 data_time: 0.1844 memory: 6717 grad_norm: 3.5088 loss: 1.0998 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0998 2023/04/14 14:52:15 - mmengine - INFO - Epoch(train) [90][1740/1879] lr: 2.0000e-04 eta: 1:56:45 time: 0.4104 data_time: 0.2675 memory: 6717 grad_norm: 3.3895 loss: 1.0649 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0649 2023/04/14 14:52:22 - mmengine - INFO - Epoch(train) [90][1760/1879] lr: 2.0000e-04 eta: 1:56:37 time: 0.3453 data_time: 0.2039 memory: 6717 grad_norm: 3.3550 loss: 1.1015 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1015 2023/04/14 14:52:27 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 14:52:30 - mmengine - INFO - Epoch(train) [90][1780/1879] lr: 2.0000e-04 eta: 1:56:30 time: 0.4171 data_time: 0.2735 memory: 6717 grad_norm: 3.4654 loss: 1.2564 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2564 2023/04/14 14:52:36 - mmengine - INFO - Epoch(train) [90][1800/1879] lr: 2.0000e-04 eta: 1:56:23 time: 0.3201 data_time: 0.1820 memory: 6717 grad_norm: 3.3805 loss: 1.1055 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1055 2023/04/14 14:52:44 - mmengine - INFO - Epoch(train) [90][1820/1879] lr: 2.0000e-04 eta: 1:56:15 time: 0.3991 data_time: 0.2571 memory: 6717 grad_norm: 3.4814 loss: 0.9096 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9096 2023/04/14 14:52:51 - mmengine - INFO - Epoch(train) [90][1840/1879] lr: 2.0000e-04 eta: 1:56:08 time: 0.3162 data_time: 0.1757 memory: 6717 grad_norm: 3.3632 loss: 0.9973 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9973 2023/04/14 14:52:59 - mmengine - INFO - Epoch(train) [90][1860/1879] lr: 2.0000e-04 eta: 1:56:00 time: 0.4021 data_time: 0.2491 memory: 6717 grad_norm: 3.5345 loss: 1.1711 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1711 2023/04/14 14:53:04 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 14:53:04 - mmengine - INFO - Epoch(train) [90][1879/1879] lr: 2.0000e-04 eta: 1:55:53 time: 0.2864 data_time: 0.1406 memory: 6717 grad_norm: 3.5886 loss: 1.0990 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.0990 2023/04/14 14:53:04 - mmengine - INFO - Saving checkpoint at 90 epochs 2023/04/14 14:53:14 - mmengine - INFO - Epoch(val) [90][ 20/155] eta: 0:01:02 time: 0.4626 data_time: 0.4294 memory: 1391 2023/04/14 14:53:20 - mmengine - INFO - Epoch(val) [90][ 40/155] eta: 0:00:44 time: 0.3176 data_time: 0.2850 memory: 1391 2023/04/14 14:53:29 - mmengine - INFO - Epoch(val) [90][ 60/155] eta: 0:00:38 time: 0.4317 data_time: 0.3981 memory: 1391 2023/04/14 14:53:35 - mmengine - INFO - Epoch(val) [90][ 80/155] eta: 0:00:28 time: 0.3159 data_time: 0.2828 memory: 1391 2023/04/14 14:53:45 - mmengine - INFO - Epoch(val) [90][100/155] eta: 0:00:21 time: 0.4597 data_time: 0.4270 memory: 1391 2023/04/14 14:53:50 - mmengine - INFO - Epoch(val) [90][120/155] eta: 0:00:13 time: 0.2920 data_time: 0.2582 memory: 1391 2023/04/14 14:53:59 - mmengine - INFO - Epoch(val) [90][140/155] eta: 0:00:05 time: 0.4412 data_time: 0.4077 memory: 1391 2023/04/14 14:54:06 - mmengine - INFO - Epoch(val) [90][155/155] acc/top1: 0.6696 acc/top5: 0.8753 acc/mean1: 0.6696 data_time: 0.3636 time: 0.3967 2023/04/14 14:54:16 - mmengine - INFO - Epoch(train) [91][ 20/1879] lr: 2.0000e-04 eta: 1:55:46 time: 0.4976 data_time: 0.3109 memory: 6717 grad_norm: 3.4449 loss: 0.9279 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9279 2023/04/14 14:54:22 - mmengine - INFO - Epoch(train) [91][ 40/1879] lr: 2.0000e-04 eta: 1:55:39 time: 0.3278 data_time: 0.1953 memory: 6717 grad_norm: 3.4231 loss: 1.1536 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1536 2023/04/14 14:54:31 - mmengine - INFO - Epoch(train) [91][ 60/1879] lr: 2.0000e-04 eta: 1:55:31 time: 0.4228 data_time: 0.1550 memory: 6717 grad_norm: 3.3915 loss: 1.0217 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.0217 2023/04/14 14:54:37 - mmengine - INFO - Epoch(train) [91][ 80/1879] lr: 2.0000e-04 eta: 1:55:24 time: 0.3011 data_time: 0.1419 memory: 6717 grad_norm: 3.4435 loss: 1.0470 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0470 2023/04/14 14:54:45 - mmengine - INFO - Epoch(train) [91][ 100/1879] lr: 2.0000e-04 eta: 1:55:16 time: 0.4189 data_time: 0.2541 memory: 6717 grad_norm: 3.4054 loss: 1.1910 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1910 2023/04/14 14:54:52 - mmengine - INFO - Epoch(train) [91][ 120/1879] lr: 2.0000e-04 eta: 1:55:09 time: 0.3186 data_time: 0.1736 memory: 6717 grad_norm: 3.5373 loss: 1.1560 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.1560 2023/04/14 14:55:01 - mmengine - INFO - Epoch(train) [91][ 140/1879] lr: 2.0000e-04 eta: 1:55:02 time: 0.4447 data_time: 0.2426 memory: 6717 grad_norm: 3.2939 loss: 1.1789 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1789 2023/04/14 14:55:07 - mmengine - INFO - Epoch(train) [91][ 160/1879] lr: 2.0000e-04 eta: 1:54:54 time: 0.3460 data_time: 0.1592 memory: 6717 grad_norm: 3.3045 loss: 1.0971 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0971 2023/04/14 14:55:16 - mmengine - INFO - Epoch(train) [91][ 180/1879] lr: 2.0000e-04 eta: 1:54:47 time: 0.4106 data_time: 0.1195 memory: 6717 grad_norm: 3.4990 loss: 1.0192 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0192 2023/04/14 14:55:22 - mmengine - INFO - Epoch(train) [91][ 200/1879] lr: 2.0000e-04 eta: 1:54:39 time: 0.2994 data_time: 0.1291 memory: 6717 grad_norm: 3.4958 loss: 1.1903 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1903 2023/04/14 14:55:30 - mmengine - INFO - Epoch(train) [91][ 220/1879] lr: 2.0000e-04 eta: 1:54:32 time: 0.4138 data_time: 0.2496 memory: 6717 grad_norm: 3.4197 loss: 1.0885 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0885 2023/04/14 14:55:36 - mmengine - INFO - Epoch(train) [91][ 240/1879] lr: 2.0000e-04 eta: 1:54:25 time: 0.3278 data_time: 0.1883 memory: 6717 grad_norm: 3.4623 loss: 1.2729 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2729 2023/04/14 14:55:44 - mmengine - INFO - Epoch(train) [91][ 260/1879] lr: 2.0000e-04 eta: 1:54:17 time: 0.3870 data_time: 0.2504 memory: 6717 grad_norm: 3.3479 loss: 0.9937 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9937 2023/04/14 14:55:51 - mmengine - INFO - Epoch(train) [91][ 280/1879] lr: 2.0000e-04 eta: 1:54:10 time: 0.3181 data_time: 0.1812 memory: 6717 grad_norm: 3.3857 loss: 1.2321 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2321 2023/04/14 14:55:59 - mmengine - INFO - Epoch(train) [91][ 300/1879] lr: 2.0000e-04 eta: 1:54:02 time: 0.4196 data_time: 0.2571 memory: 6717 grad_norm: 3.4666 loss: 1.0136 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0136 2023/04/14 14:56:06 - mmengine - INFO - Epoch(train) [91][ 320/1879] lr: 2.0000e-04 eta: 1:53:55 time: 0.3297 data_time: 0.1251 memory: 6717 grad_norm: 3.4948 loss: 1.2676 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2676 2023/04/14 14:56:13 - mmengine - INFO - Epoch(train) [91][ 340/1879] lr: 2.0000e-04 eta: 1:53:48 time: 0.3876 data_time: 0.1740 memory: 6717 grad_norm: 3.3934 loss: 1.1811 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1811 2023/04/14 14:56:20 - mmengine - INFO - Epoch(train) [91][ 360/1879] lr: 2.0000e-04 eta: 1:53:40 time: 0.3381 data_time: 0.1623 memory: 6717 grad_norm: 3.4124 loss: 1.0632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0632 2023/04/14 14:56:28 - mmengine - INFO - Epoch(train) [91][ 380/1879] lr: 2.0000e-04 eta: 1:53:33 time: 0.4110 data_time: 0.1456 memory: 6717 grad_norm: 3.4678 loss: 1.1499 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1499 2023/04/14 14:56:36 - mmengine - INFO - Epoch(train) [91][ 400/1879] lr: 2.0000e-04 eta: 1:53:25 time: 0.3672 data_time: 0.0319 memory: 6717 grad_norm: 3.5016 loss: 1.0675 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0675 2023/04/14 14:56:43 - mmengine - INFO - Epoch(train) [91][ 420/1879] lr: 2.0000e-04 eta: 1:53:18 time: 0.3474 data_time: 0.0155 memory: 6717 grad_norm: 3.3959 loss: 1.2257 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2257 2023/04/14 14:56:50 - mmengine - INFO - Epoch(train) [91][ 440/1879] lr: 2.0000e-04 eta: 1:53:11 time: 0.3703 data_time: 0.0127 memory: 6717 grad_norm: 3.5236 loss: 1.1564 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1564 2023/04/14 14:56:57 - mmengine - INFO - Epoch(train) [91][ 460/1879] lr: 2.0000e-04 eta: 1:53:03 time: 0.3599 data_time: 0.0247 memory: 6717 grad_norm: 3.3777 loss: 0.9632 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9632 2023/04/14 14:57:05 - mmengine - INFO - Epoch(train) [91][ 480/1879] lr: 2.0000e-04 eta: 1:52:56 time: 0.3837 data_time: 0.0131 memory: 6717 grad_norm: 3.4234 loss: 1.0460 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.0460 2023/04/14 14:57:12 - mmengine - INFO - Epoch(train) [91][ 500/1879] lr: 2.0000e-04 eta: 1:52:48 time: 0.3578 data_time: 0.0150 memory: 6717 grad_norm: 3.4945 loss: 1.0668 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0668 2023/04/14 14:57:19 - mmengine - INFO - Epoch(train) [91][ 520/1879] lr: 2.0000e-04 eta: 1:52:41 time: 0.3366 data_time: 0.0126 memory: 6717 grad_norm: 3.3939 loss: 1.0386 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0386 2023/04/14 14:57:28 - mmengine - INFO - Epoch(train) [91][ 540/1879] lr: 2.0000e-04 eta: 1:52:34 time: 0.4350 data_time: 0.0188 memory: 6717 grad_norm: 3.4231 loss: 1.1237 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1237 2023/04/14 14:57:34 - mmengine - INFO - Epoch(train) [91][ 560/1879] lr: 2.0000e-04 eta: 1:52:26 time: 0.3274 data_time: 0.0126 memory: 6717 grad_norm: 3.4749 loss: 1.0334 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0334 2023/04/14 14:57:42 - mmengine - INFO - Epoch(train) [91][ 580/1879] lr: 2.0000e-04 eta: 1:52:19 time: 0.3973 data_time: 0.0154 memory: 6717 grad_norm: 3.5226 loss: 1.1957 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1957 2023/04/14 14:57:48 - mmengine - INFO - Epoch(train) [91][ 600/1879] lr: 2.0000e-04 eta: 1:52:11 time: 0.3152 data_time: 0.0119 memory: 6717 grad_norm: 3.5203 loss: 1.2261 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2261 2023/04/14 14:57:57 - mmengine - INFO - Epoch(train) [91][ 620/1879] lr: 2.0000e-04 eta: 1:52:04 time: 0.4299 data_time: 0.0148 memory: 6717 grad_norm: 3.4275 loss: 1.1873 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1873 2023/04/14 14:58:03 - mmengine - INFO - Epoch(train) [91][ 640/1879] lr: 2.0000e-04 eta: 1:51:56 time: 0.2856 data_time: 0.0116 memory: 6717 grad_norm: 3.4889 loss: 0.9587 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9587 2023/04/14 14:58:11 - mmengine - INFO - Epoch(train) [91][ 660/1879] lr: 2.0000e-04 eta: 1:51:49 time: 0.4221 data_time: 0.0164 memory: 6717 grad_norm: 3.5209 loss: 1.1622 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1622 2023/04/14 14:58:17 - mmengine - INFO - Epoch(train) [91][ 680/1879] lr: 2.0000e-04 eta: 1:51:41 time: 0.2921 data_time: 0.0126 memory: 6717 grad_norm: 3.4993 loss: 1.2608 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2608 2023/04/14 14:58:26 - mmengine - INFO - Epoch(train) [91][ 700/1879] lr: 2.0000e-04 eta: 1:51:34 time: 0.4350 data_time: 0.1324 memory: 6717 grad_norm: 3.3886 loss: 1.1096 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1096 2023/04/14 14:58:32 - mmengine - INFO - Epoch(train) [91][ 720/1879] lr: 2.0000e-04 eta: 1:51:27 time: 0.3181 data_time: 0.1083 memory: 6717 grad_norm: 3.3981 loss: 1.1015 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1015 2023/04/14 14:58:40 - mmengine - INFO - Epoch(train) [91][ 740/1879] lr: 2.0000e-04 eta: 1:51:19 time: 0.3935 data_time: 0.0539 memory: 6717 grad_norm: 3.4371 loss: 1.0267 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0267 2023/04/14 14:58:51 - mmengine - INFO - Epoch(train) [91][ 760/1879] lr: 2.0000e-04 eta: 1:51:12 time: 0.5431 data_time: 0.0117 memory: 6717 grad_norm: 3.4253 loss: 1.1255 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1255 2023/04/14 14:58:58 - mmengine - INFO - Epoch(train) [91][ 780/1879] lr: 2.0000e-04 eta: 1:51:05 time: 0.3493 data_time: 0.0134 memory: 6717 grad_norm: 3.4259 loss: 1.1209 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1209 2023/04/14 14:59:06 - mmengine - INFO - Epoch(train) [91][ 800/1879] lr: 2.0000e-04 eta: 1:50:58 time: 0.4048 data_time: 0.0137 memory: 6717 grad_norm: 3.4101 loss: 1.0722 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0722 2023/04/14 14:59:13 - mmengine - INFO - Epoch(train) [91][ 820/1879] lr: 2.0000e-04 eta: 1:50:50 time: 0.3616 data_time: 0.0139 memory: 6717 grad_norm: 3.4995 loss: 1.2421 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2421 2023/04/14 14:59:20 - mmengine - INFO - Epoch(train) [91][ 840/1879] lr: 2.0000e-04 eta: 1:50:43 time: 0.3638 data_time: 0.0148 memory: 6717 grad_norm: 3.5020 loss: 1.0845 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0845 2023/04/14 14:59:28 - mmengine - INFO - Epoch(train) [91][ 860/1879] lr: 2.0000e-04 eta: 1:50:35 time: 0.3789 data_time: 0.0157 memory: 6717 grad_norm: 3.4370 loss: 0.9673 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9673 2023/04/14 14:59:34 - mmengine - INFO - Epoch(train) [91][ 880/1879] lr: 2.0000e-04 eta: 1:50:28 time: 0.3139 data_time: 0.0128 memory: 6717 grad_norm: 3.4374 loss: 1.0259 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0259 2023/04/14 14:59:39 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 14:59:42 - mmengine - INFO - Epoch(train) [91][ 900/1879] lr: 2.0000e-04 eta: 1:50:20 time: 0.3668 data_time: 0.0222 memory: 6717 grad_norm: 3.4643 loss: 1.0465 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0465 2023/04/14 14:59:49 - mmengine - INFO - Epoch(train) [91][ 920/1879] lr: 2.0000e-04 eta: 1:50:13 time: 0.3879 data_time: 0.0382 memory: 6717 grad_norm: 3.4037 loss: 1.1537 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1537 2023/04/14 14:59:56 - mmengine - INFO - Epoch(train) [91][ 940/1879] lr: 2.0000e-04 eta: 1:50:06 time: 0.3412 data_time: 0.0410 memory: 6717 grad_norm: 3.4046 loss: 0.9586 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9586 2023/04/14 15:00:04 - mmengine - INFO - Epoch(train) [91][ 960/1879] lr: 2.0000e-04 eta: 1:49:58 time: 0.3913 data_time: 0.0135 memory: 6717 grad_norm: 3.4287 loss: 1.1474 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1474 2023/04/14 15:00:11 - mmengine - INFO - Epoch(train) [91][ 980/1879] lr: 2.0000e-04 eta: 1:49:51 time: 0.3572 data_time: 0.0308 memory: 6717 grad_norm: 3.3622 loss: 1.0262 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0262 2023/04/14 15:00:19 - mmengine - INFO - Epoch(train) [91][1000/1879] lr: 2.0000e-04 eta: 1:49:43 time: 0.3800 data_time: 0.0996 memory: 6717 grad_norm: 3.4212 loss: 1.1153 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.1153 2023/04/14 15:00:26 - mmengine - INFO - Epoch(train) [91][1020/1879] lr: 2.0000e-04 eta: 1:49:36 time: 0.3647 data_time: 0.1207 memory: 6717 grad_norm: 3.3311 loss: 0.9726 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9726 2023/04/14 15:00:33 - mmengine - INFO - Epoch(train) [91][1040/1879] lr: 2.0000e-04 eta: 1:49:29 time: 0.3609 data_time: 0.1309 memory: 6717 grad_norm: 3.4174 loss: 0.9660 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9660 2023/04/14 15:00:40 - mmengine - INFO - Epoch(train) [91][1060/1879] lr: 2.0000e-04 eta: 1:49:21 time: 0.3578 data_time: 0.0911 memory: 6717 grad_norm: 3.4754 loss: 1.1266 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1266 2023/04/14 15:00:49 - mmengine - INFO - Epoch(train) [91][1080/1879] lr: 2.0000e-04 eta: 1:49:14 time: 0.4081 data_time: 0.0200 memory: 6717 grad_norm: 3.3952 loss: 1.0577 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0577 2023/04/14 15:00:56 - mmengine - INFO - Epoch(train) [91][1100/1879] lr: 2.0000e-04 eta: 1:49:06 time: 0.3550 data_time: 0.0172 memory: 6717 grad_norm: 3.3856 loss: 0.9831 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9831 2023/04/14 15:01:03 - mmengine - INFO - Epoch(train) [91][1120/1879] lr: 2.0000e-04 eta: 1:48:59 time: 0.3849 data_time: 0.0124 memory: 6717 grad_norm: 3.4376 loss: 1.0579 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0579 2023/04/14 15:01:10 - mmengine - INFO - Epoch(train) [91][1140/1879] lr: 2.0000e-04 eta: 1:48:52 time: 0.3271 data_time: 0.0180 memory: 6717 grad_norm: 3.4104 loss: 1.1454 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1454 2023/04/14 15:01:19 - mmengine - INFO - Epoch(train) [91][1160/1879] lr: 2.0000e-04 eta: 1:48:44 time: 0.4248 data_time: 0.0122 memory: 6717 grad_norm: 3.5160 loss: 1.0341 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0341 2023/04/14 15:01:25 - mmengine - INFO - Epoch(train) [91][1180/1879] lr: 2.0000e-04 eta: 1:48:37 time: 0.3403 data_time: 0.0168 memory: 6717 grad_norm: 3.5037 loss: 1.0953 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0953 2023/04/14 15:01:34 - mmengine - INFO - Epoch(train) [91][1200/1879] lr: 2.0000e-04 eta: 1:48:29 time: 0.4236 data_time: 0.0129 memory: 6717 grad_norm: 3.4391 loss: 1.0016 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0016 2023/04/14 15:01:40 - mmengine - INFO - Epoch(train) [91][1220/1879] lr: 2.0000e-04 eta: 1:48:22 time: 0.3152 data_time: 0.0175 memory: 6717 grad_norm: 3.3931 loss: 1.0582 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0582 2023/04/14 15:01:48 - mmengine - INFO - Epoch(train) [91][1240/1879] lr: 2.0000e-04 eta: 1:48:15 time: 0.4016 data_time: 0.0279 memory: 6717 grad_norm: 3.3973 loss: 0.9727 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9727 2023/04/14 15:01:55 - mmengine - INFO - Epoch(train) [91][1260/1879] lr: 2.0000e-04 eta: 1:48:07 time: 0.3285 data_time: 0.0153 memory: 6717 grad_norm: 3.4553 loss: 1.0948 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0948 2023/04/14 15:02:03 - mmengine - INFO - Epoch(train) [91][1280/1879] lr: 2.0000e-04 eta: 1:48:00 time: 0.4144 data_time: 0.0126 memory: 6717 grad_norm: 3.3437 loss: 1.0933 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0933 2023/04/14 15:02:10 - mmengine - INFO - Epoch(train) [91][1300/1879] lr: 2.0000e-04 eta: 1:47:52 time: 0.3269 data_time: 0.0165 memory: 6717 grad_norm: 3.3564 loss: 1.0309 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0309 2023/04/14 15:02:17 - mmengine - INFO - Epoch(train) [91][1320/1879] lr: 2.0000e-04 eta: 1:47:45 time: 0.3646 data_time: 0.0128 memory: 6717 grad_norm: 3.4304 loss: 1.0318 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0318 2023/04/14 15:02:24 - mmengine - INFO - Epoch(train) [91][1340/1879] lr: 2.0000e-04 eta: 1:47:37 time: 0.3363 data_time: 0.0159 memory: 6717 grad_norm: 3.5020 loss: 1.0685 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0685 2023/04/14 15:02:31 - mmengine - INFO - Epoch(train) [91][1360/1879] lr: 2.0000e-04 eta: 1:47:30 time: 0.3653 data_time: 0.0128 memory: 6717 grad_norm: 3.4038 loss: 0.9477 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9477 2023/04/14 15:02:40 - mmengine - INFO - Epoch(train) [91][1380/1879] lr: 2.0000e-04 eta: 1:47:23 time: 0.4341 data_time: 0.0157 memory: 6717 grad_norm: 3.3697 loss: 1.0807 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0807 2023/04/14 15:02:46 - mmengine - INFO - Epoch(train) [91][1400/1879] lr: 2.0000e-04 eta: 1:47:15 time: 0.3087 data_time: 0.0126 memory: 6717 grad_norm: 3.4371 loss: 1.1053 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1053 2023/04/14 15:02:54 - mmengine - INFO - Epoch(train) [91][1420/1879] lr: 2.0000e-04 eta: 1:47:08 time: 0.4035 data_time: 0.0146 memory: 6717 grad_norm: 3.5455 loss: 1.1474 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1474 2023/04/14 15:03:00 - mmengine - INFO - Epoch(train) [91][1440/1879] lr: 2.0000e-04 eta: 1:47:00 time: 0.3023 data_time: 0.0130 memory: 6717 grad_norm: 3.5026 loss: 1.0495 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0495 2023/04/14 15:03:08 - mmengine - INFO - Epoch(train) [91][1460/1879] lr: 2.0000e-04 eta: 1:46:53 time: 0.4141 data_time: 0.0160 memory: 6717 grad_norm: 3.4972 loss: 0.9253 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9253 2023/04/14 15:03:14 - mmengine - INFO - Epoch(train) [91][1480/1879] lr: 2.0000e-04 eta: 1:46:46 time: 0.3067 data_time: 0.0130 memory: 6717 grad_norm: 3.4541 loss: 1.1379 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1379 2023/04/14 15:03:22 - mmengine - INFO - Epoch(train) [91][1500/1879] lr: 2.0000e-04 eta: 1:46:38 time: 0.3843 data_time: 0.0159 memory: 6717 grad_norm: 3.4889 loss: 1.1079 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1079 2023/04/14 15:03:29 - mmengine - INFO - Epoch(train) [91][1520/1879] lr: 2.0000e-04 eta: 1:46:31 time: 0.3361 data_time: 0.0170 memory: 6717 grad_norm: 3.3810 loss: 1.1185 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1185 2023/04/14 15:03:37 - mmengine - INFO - Epoch(train) [91][1540/1879] lr: 2.0000e-04 eta: 1:46:23 time: 0.4037 data_time: 0.0161 memory: 6717 grad_norm: 3.4367 loss: 1.0486 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0486 2023/04/14 15:03:43 - mmengine - INFO - Epoch(train) [91][1560/1879] lr: 2.0000e-04 eta: 1:46:16 time: 0.3301 data_time: 0.0129 memory: 6717 grad_norm: 3.4738 loss: 0.9980 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 0.9980 2023/04/14 15:03:52 - mmengine - INFO - Epoch(train) [91][1580/1879] lr: 2.0000e-04 eta: 1:46:09 time: 0.4249 data_time: 0.0161 memory: 6717 grad_norm: 3.4302 loss: 1.1947 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1947 2023/04/14 15:03:58 - mmengine - INFO - Epoch(train) [91][1600/1879] lr: 2.0000e-04 eta: 1:46:01 time: 0.2858 data_time: 0.0121 memory: 6717 grad_norm: 3.5297 loss: 1.2337 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2337 2023/04/14 15:04:06 - mmengine - INFO - Epoch(train) [91][1620/1879] lr: 2.0000e-04 eta: 1:45:54 time: 0.4377 data_time: 0.0267 memory: 6717 grad_norm: 3.5392 loss: 1.1234 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1234 2023/04/14 15:04:13 - mmengine - INFO - Epoch(train) [91][1640/1879] lr: 2.0000e-04 eta: 1:45:46 time: 0.3129 data_time: 0.0153 memory: 6717 grad_norm: 3.4422 loss: 1.1074 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1074 2023/04/14 15:04:22 - mmengine - INFO - Epoch(train) [91][1660/1879] lr: 2.0000e-04 eta: 1:45:39 time: 0.4466 data_time: 0.0154 memory: 6717 grad_norm: 3.5470 loss: 1.1399 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1399 2023/04/14 15:04:28 - mmengine - INFO - Epoch(train) [91][1680/1879] lr: 2.0000e-04 eta: 1:45:31 time: 0.3158 data_time: 0.0132 memory: 6717 grad_norm: 3.5883 loss: 1.2333 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2333 2023/04/14 15:04:36 - mmengine - INFO - Epoch(train) [91][1700/1879] lr: 2.0000e-04 eta: 1:45:24 time: 0.4109 data_time: 0.0176 memory: 6717 grad_norm: 3.4832 loss: 1.0450 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0450 2023/04/14 15:04:43 - mmengine - INFO - Epoch(train) [91][1720/1879] lr: 2.0000e-04 eta: 1:45:17 time: 0.3438 data_time: 0.0129 memory: 6717 grad_norm: 3.4884 loss: 0.9861 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9861 2023/04/14 15:04:51 - mmengine - INFO - Epoch(train) [91][1740/1879] lr: 2.0000e-04 eta: 1:45:09 time: 0.4125 data_time: 0.0145 memory: 6717 grad_norm: 3.3726 loss: 1.0551 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0551 2023/04/14 15:04:58 - mmengine - INFO - Epoch(train) [91][1760/1879] lr: 2.0000e-04 eta: 1:45:02 time: 0.3109 data_time: 0.0123 memory: 6717 grad_norm: 3.4100 loss: 1.1180 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1180 2023/04/14 15:05:06 - mmengine - INFO - Epoch(train) [91][1780/1879] lr: 2.0000e-04 eta: 1:44:55 time: 0.4036 data_time: 0.0158 memory: 6717 grad_norm: 3.4010 loss: 1.0974 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0974 2023/04/14 15:05:13 - mmengine - INFO - Epoch(train) [91][1800/1879] lr: 2.0000e-04 eta: 1:44:47 time: 0.3553 data_time: 0.0118 memory: 6717 grad_norm: 3.3132 loss: 1.1355 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1355 2023/04/14 15:05:21 - mmengine - INFO - Epoch(train) [91][1820/1879] lr: 2.0000e-04 eta: 1:44:40 time: 0.3902 data_time: 0.0138 memory: 6717 grad_norm: 3.4572 loss: 0.9612 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9612 2023/04/14 15:05:27 - mmengine - INFO - Epoch(train) [91][1840/1879] lr: 2.0000e-04 eta: 1:44:32 time: 0.3123 data_time: 0.0137 memory: 6717 grad_norm: 3.4183 loss: 1.2228 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2228 2023/04/14 15:05:36 - mmengine - INFO - Epoch(train) [91][1860/1879] lr: 2.0000e-04 eta: 1:44:25 time: 0.4546 data_time: 0.0131 memory: 6717 grad_norm: 3.4201 loss: 1.1382 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1382 2023/04/14 15:05:41 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 15:05:41 - mmengine - INFO - Epoch(train) [91][1879/1879] lr: 2.0000e-04 eta: 1:44:18 time: 0.3787 data_time: 0.0125 memory: 6717 grad_norm: 3.4933 loss: 1.2635 top1_acc: 0.4286 top5_acc: 0.7143 loss_cls: 1.2635 2023/04/14 15:05:51 - mmengine - INFO - Epoch(val) [91][ 20/155] eta: 0:01:03 time: 0.4710 data_time: 0.4377 memory: 1391 2023/04/14 15:05:57 - mmengine - INFO - Epoch(val) [91][ 40/155] eta: 0:00:45 time: 0.3171 data_time: 0.2839 memory: 1391 2023/04/14 15:06:06 - mmengine - INFO - Epoch(val) [91][ 60/155] eta: 0:00:38 time: 0.4300 data_time: 0.3967 memory: 1391 2023/04/14 15:06:12 - mmengine - INFO - Epoch(val) [91][ 80/155] eta: 0:00:28 time: 0.3171 data_time: 0.2837 memory: 1391 2023/04/14 15:06:21 - mmengine - INFO - Epoch(val) [91][100/155] eta: 0:00:21 time: 0.4528 data_time: 0.4192 memory: 1391 2023/04/14 15:06:27 - mmengine - INFO - Epoch(val) [91][120/155] eta: 0:00:13 time: 0.2929 data_time: 0.2596 memory: 1391 2023/04/14 15:06:36 - mmengine - INFO - Epoch(val) [91][140/155] eta: 0:00:05 time: 0.4450 data_time: 0.4118 memory: 1391 2023/04/14 15:06:43 - mmengine - INFO - Epoch(val) [91][155/155] acc/top1: 0.6680 acc/top5: 0.8739 acc/mean1: 0.6680 data_time: 0.3828 time: 0.4153 2023/04/14 15:06:50 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 15:06:53 - mmengine - INFO - Epoch(train) [92][ 20/1879] lr: 2.0000e-04 eta: 1:44:11 time: 0.4786 data_time: 0.2879 memory: 6717 grad_norm: 3.5065 loss: 1.2139 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2139 2023/04/14 15:07:00 - mmengine - INFO - Epoch(train) [92][ 40/1879] lr: 2.0000e-04 eta: 1:44:03 time: 0.3351 data_time: 0.1321 memory: 6717 grad_norm: 3.3964 loss: 1.2269 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2269 2023/04/14 15:07:08 - mmengine - INFO - Epoch(train) [92][ 60/1879] lr: 2.0000e-04 eta: 1:43:56 time: 0.4189 data_time: 0.0890 memory: 6717 grad_norm: 3.5267 loss: 1.1296 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1296 2023/04/14 15:07:15 - mmengine - INFO - Epoch(train) [92][ 80/1879] lr: 2.0000e-04 eta: 1:43:48 time: 0.3232 data_time: 0.0331 memory: 6717 grad_norm: 3.4333 loss: 1.2267 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2267 2023/04/14 15:07:22 - mmengine - INFO - Epoch(train) [92][ 100/1879] lr: 2.0000e-04 eta: 1:43:41 time: 0.3865 data_time: 0.0154 memory: 6717 grad_norm: 3.3815 loss: 1.0954 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0954 2023/04/14 15:07:29 - mmengine - INFO - Epoch(train) [92][ 120/1879] lr: 2.0000e-04 eta: 1:43:34 time: 0.3316 data_time: 0.0126 memory: 6717 grad_norm: 3.3829 loss: 1.1544 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1544 2023/04/14 15:07:37 - mmengine - INFO - Epoch(train) [92][ 140/1879] lr: 2.0000e-04 eta: 1:43:26 time: 0.4104 data_time: 0.0155 memory: 6717 grad_norm: 3.4344 loss: 0.9734 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9734 2023/04/14 15:07:44 - mmengine - INFO - Epoch(train) [92][ 160/1879] lr: 2.0000e-04 eta: 1:43:19 time: 0.3208 data_time: 0.0128 memory: 6717 grad_norm: 3.5780 loss: 1.1466 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1466 2023/04/14 15:07:52 - mmengine - INFO - Epoch(train) [92][ 180/1879] lr: 2.0000e-04 eta: 1:43:11 time: 0.4178 data_time: 0.0152 memory: 6717 grad_norm: 3.3976 loss: 1.2973 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2973 2023/04/14 15:07:59 - mmengine - INFO - Epoch(train) [92][ 200/1879] lr: 2.0000e-04 eta: 1:43:04 time: 0.3302 data_time: 0.0140 memory: 6717 grad_norm: 3.4191 loss: 1.0907 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0907 2023/04/14 15:08:07 - mmengine - INFO - Epoch(train) [92][ 220/1879] lr: 2.0000e-04 eta: 1:42:57 time: 0.4077 data_time: 0.0150 memory: 6717 grad_norm: 3.3276 loss: 0.9880 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9880 2023/04/14 15:08:13 - mmengine - INFO - Epoch(train) [92][ 240/1879] lr: 2.0000e-04 eta: 1:42:49 time: 0.3263 data_time: 0.0143 memory: 6717 grad_norm: 3.5742 loss: 1.1449 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1449 2023/04/14 15:08:22 - mmengine - INFO - Epoch(train) [92][ 260/1879] lr: 2.0000e-04 eta: 1:42:42 time: 0.4118 data_time: 0.0152 memory: 6717 grad_norm: 3.3472 loss: 1.1600 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1600 2023/04/14 15:08:28 - mmengine - INFO - Epoch(train) [92][ 280/1879] lr: 2.0000e-04 eta: 1:42:34 time: 0.3036 data_time: 0.0134 memory: 6717 grad_norm: 3.3987 loss: 1.0769 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0769 2023/04/14 15:08:36 - mmengine - INFO - Epoch(train) [92][ 300/1879] lr: 2.0000e-04 eta: 1:42:27 time: 0.4094 data_time: 0.0152 memory: 6717 grad_norm: 3.4263 loss: 1.0790 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0790 2023/04/14 15:08:43 - mmengine - INFO - Epoch(train) [92][ 320/1879] lr: 2.0000e-04 eta: 1:42:19 time: 0.3365 data_time: 0.0129 memory: 6717 grad_norm: 3.4671 loss: 1.2173 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2173 2023/04/14 15:08:51 - mmengine - INFO - Epoch(train) [92][ 340/1879] lr: 2.0000e-04 eta: 1:42:12 time: 0.3993 data_time: 0.0151 memory: 6717 grad_norm: 3.4367 loss: 1.1519 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1519 2023/04/14 15:08:57 - mmengine - INFO - Epoch(train) [92][ 360/1879] lr: 2.0000e-04 eta: 1:42:05 time: 0.3448 data_time: 0.0133 memory: 6717 grad_norm: 3.4042 loss: 1.2509 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2509 2023/04/14 15:09:06 - mmengine - INFO - Epoch(train) [92][ 380/1879] lr: 2.0000e-04 eta: 1:41:57 time: 0.4124 data_time: 0.0152 memory: 6717 grad_norm: 3.4113 loss: 1.0309 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0309 2023/04/14 15:09:13 - mmengine - INFO - Epoch(train) [92][ 400/1879] lr: 2.0000e-04 eta: 1:41:50 time: 0.3490 data_time: 0.0128 memory: 6717 grad_norm: 3.4607 loss: 1.2385 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2385 2023/04/14 15:09:20 - mmengine - INFO - Epoch(train) [92][ 420/1879] lr: 2.0000e-04 eta: 1:41:42 time: 0.3631 data_time: 0.0159 memory: 6717 grad_norm: 3.4373 loss: 1.0566 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0566 2023/04/14 15:09:27 - mmengine - INFO - Epoch(train) [92][ 440/1879] lr: 2.0000e-04 eta: 1:41:35 time: 0.3579 data_time: 0.0111 memory: 6717 grad_norm: 3.4508 loss: 1.1659 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1659 2023/04/14 15:09:35 - mmengine - INFO - Epoch(train) [92][ 460/1879] lr: 2.0000e-04 eta: 1:41:28 time: 0.4053 data_time: 0.0142 memory: 6717 grad_norm: 3.4210 loss: 1.0486 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0486 2023/04/14 15:09:43 - mmengine - INFO - Epoch(train) [92][ 480/1879] lr: 2.0000e-04 eta: 1:41:20 time: 0.3697 data_time: 0.0139 memory: 6717 grad_norm: 3.4643 loss: 1.2173 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2173 2023/04/14 15:09:50 - mmengine - INFO - Epoch(train) [92][ 500/1879] lr: 2.0000e-04 eta: 1:41:13 time: 0.3672 data_time: 0.0140 memory: 6717 grad_norm: 3.3512 loss: 1.0006 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0006 2023/04/14 15:09:56 - mmengine - INFO - Epoch(train) [92][ 520/1879] lr: 2.0000e-04 eta: 1:41:05 time: 0.3097 data_time: 0.0141 memory: 6717 grad_norm: 3.4784 loss: 1.0139 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0139 2023/04/14 15:10:05 - mmengine - INFO - Epoch(train) [92][ 540/1879] lr: 2.0000e-04 eta: 1:40:58 time: 0.4455 data_time: 0.0134 memory: 6717 grad_norm: 3.2720 loss: 1.0014 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0014 2023/04/14 15:10:12 - mmengine - INFO - Epoch(train) [92][ 560/1879] lr: 2.0000e-04 eta: 1:40:51 time: 0.3442 data_time: 0.0146 memory: 6717 grad_norm: 3.4253 loss: 1.0745 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0745 2023/04/14 15:10:21 - mmengine - INFO - Epoch(train) [92][ 580/1879] lr: 2.0000e-04 eta: 1:40:43 time: 0.4307 data_time: 0.0133 memory: 6717 grad_norm: 3.3229 loss: 1.0094 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0094 2023/04/14 15:10:27 - mmengine - INFO - Epoch(train) [92][ 600/1879] lr: 2.0000e-04 eta: 1:40:36 time: 0.3243 data_time: 0.0152 memory: 6717 grad_norm: 3.5809 loss: 1.0526 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0526 2023/04/14 15:10:36 - mmengine - INFO - Epoch(train) [92][ 620/1879] lr: 2.0000e-04 eta: 1:40:29 time: 0.4363 data_time: 0.0127 memory: 6717 grad_norm: 3.3646 loss: 0.9736 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9736 2023/04/14 15:10:42 - mmengine - INFO - Epoch(train) [92][ 640/1879] lr: 2.0000e-04 eta: 1:40:21 time: 0.3007 data_time: 0.0155 memory: 6717 grad_norm: 3.4844 loss: 1.0471 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0471 2023/04/14 15:10:50 - mmengine - INFO - Epoch(train) [92][ 660/1879] lr: 2.0000e-04 eta: 1:40:14 time: 0.3881 data_time: 0.0124 memory: 6717 grad_norm: 3.4181 loss: 1.1331 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1331 2023/04/14 15:10:56 - mmengine - INFO - Epoch(train) [92][ 680/1879] lr: 2.0000e-04 eta: 1:40:06 time: 0.3278 data_time: 0.0157 memory: 6717 grad_norm: 3.3963 loss: 1.1865 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1865 2023/04/14 15:11:04 - mmengine - INFO - Epoch(train) [92][ 700/1879] lr: 2.0000e-04 eta: 1:39:59 time: 0.3848 data_time: 0.0132 memory: 6717 grad_norm: 3.4727 loss: 1.0742 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0742 2023/04/14 15:11:11 - mmengine - INFO - Epoch(train) [92][ 720/1879] lr: 2.0000e-04 eta: 1:39:51 time: 0.3377 data_time: 0.0218 memory: 6717 grad_norm: 3.4767 loss: 1.1250 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1250 2023/04/14 15:11:19 - mmengine - INFO - Epoch(train) [92][ 740/1879] lr: 2.0000e-04 eta: 1:39:44 time: 0.3957 data_time: 0.0142 memory: 6717 grad_norm: 3.4308 loss: 1.0172 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.0172 2023/04/14 15:11:25 - mmengine - INFO - Epoch(train) [92][ 760/1879] lr: 2.0000e-04 eta: 1:39:37 time: 0.3378 data_time: 0.0257 memory: 6717 grad_norm: 3.3829 loss: 1.1703 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1703 2023/04/14 15:11:33 - mmengine - INFO - Epoch(train) [92][ 780/1879] lr: 2.0000e-04 eta: 1:39:29 time: 0.4027 data_time: 0.0117 memory: 6717 grad_norm: 3.4013 loss: 1.0485 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0485 2023/04/14 15:11:39 - mmengine - INFO - Epoch(train) [92][ 800/1879] lr: 2.0000e-04 eta: 1:39:22 time: 0.3053 data_time: 0.0153 memory: 6717 grad_norm: 3.4038 loss: 1.1042 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1042 2023/04/14 15:11:48 - mmengine - INFO - Epoch(train) [92][ 820/1879] lr: 2.0000e-04 eta: 1:39:14 time: 0.4085 data_time: 0.0126 memory: 6717 grad_norm: 3.4424 loss: 1.0672 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0672 2023/04/14 15:11:54 - mmengine - INFO - Epoch(train) [92][ 840/1879] lr: 2.0000e-04 eta: 1:39:07 time: 0.3150 data_time: 0.0262 memory: 6717 grad_norm: 3.4780 loss: 1.1566 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1566 2023/04/14 15:12:02 - mmengine - INFO - Epoch(train) [92][ 860/1879] lr: 2.0000e-04 eta: 1:39:00 time: 0.3817 data_time: 0.0168 memory: 6717 grad_norm: 3.3768 loss: 1.0265 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0265 2023/04/14 15:12:10 - mmengine - INFO - Epoch(train) [92][ 880/1879] lr: 2.0000e-04 eta: 1:38:52 time: 0.4009 data_time: 0.0303 memory: 6717 grad_norm: 3.5407 loss: 1.1019 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1019 2023/04/14 15:12:16 - mmengine - INFO - Epoch(train) [92][ 900/1879] lr: 2.0000e-04 eta: 1:38:45 time: 0.3060 data_time: 0.0625 memory: 6717 grad_norm: 3.4751 loss: 1.0639 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0639 2023/04/14 15:12:24 - mmengine - INFO - Epoch(train) [92][ 920/1879] lr: 2.0000e-04 eta: 1:38:37 time: 0.4180 data_time: 0.1479 memory: 6717 grad_norm: 3.4752 loss: 1.1972 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1972 2023/04/14 15:12:30 - mmengine - INFO - Epoch(train) [92][ 940/1879] lr: 2.0000e-04 eta: 1:38:30 time: 0.3049 data_time: 0.1604 memory: 6717 grad_norm: 3.3995 loss: 1.0439 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0439 2023/04/14 15:12:38 - mmengine - INFO - Epoch(train) [92][ 960/1879] lr: 2.0000e-04 eta: 1:38:22 time: 0.3904 data_time: 0.2315 memory: 6717 grad_norm: 3.4356 loss: 1.0235 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0235 2023/04/14 15:12:45 - mmengine - INFO - Epoch(train) [92][ 980/1879] lr: 2.0000e-04 eta: 1:38:15 time: 0.3295 data_time: 0.1198 memory: 6717 grad_norm: 3.5166 loss: 1.2592 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2592 2023/04/14 15:12:53 - mmengine - INFO - Epoch(train) [92][1000/1879] lr: 2.0000e-04 eta: 1:38:08 time: 0.4231 data_time: 0.0885 memory: 6717 grad_norm: 3.3931 loss: 1.0840 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0840 2023/04/14 15:12:57 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 15:13:00 - mmengine - INFO - Epoch(train) [92][1020/1879] lr: 2.0000e-04 eta: 1:38:00 time: 0.3354 data_time: 0.0309 memory: 6717 grad_norm: 3.4007 loss: 1.0961 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0961 2023/04/14 15:13:07 - mmengine - INFO - Epoch(train) [92][1040/1879] lr: 2.0000e-04 eta: 1:37:53 time: 0.3811 data_time: 0.0249 memory: 6717 grad_norm: 3.4648 loss: 1.2757 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2757 2023/04/14 15:13:15 - mmengine - INFO - Epoch(train) [92][1060/1879] lr: 2.0000e-04 eta: 1:37:45 time: 0.3553 data_time: 0.1442 memory: 6717 grad_norm: 3.4183 loss: 1.0459 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0459 2023/04/14 15:13:23 - mmengine - INFO - Epoch(train) [92][1080/1879] lr: 2.0000e-04 eta: 1:37:38 time: 0.4071 data_time: 0.0587 memory: 6717 grad_norm: 3.4352 loss: 1.0616 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0616 2023/04/14 15:13:29 - mmengine - INFO - Epoch(train) [92][1100/1879] lr: 2.0000e-04 eta: 1:37:31 time: 0.3254 data_time: 0.0139 memory: 6717 grad_norm: 3.4309 loss: 1.1679 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1679 2023/04/14 15:13:38 - mmengine - INFO - Epoch(train) [92][1120/1879] lr: 2.0000e-04 eta: 1:37:23 time: 0.4141 data_time: 0.0251 memory: 6717 grad_norm: 3.4757 loss: 1.0818 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0818 2023/04/14 15:13:45 - mmengine - INFO - Epoch(train) [92][1140/1879] lr: 2.0000e-04 eta: 1:37:16 time: 0.3682 data_time: 0.0141 memory: 6717 grad_norm: 3.4421 loss: 1.1383 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1383 2023/04/14 15:13:52 - mmengine - INFO - Epoch(train) [92][1160/1879] lr: 2.0000e-04 eta: 1:37:08 time: 0.3665 data_time: 0.0126 memory: 6717 grad_norm: 3.4658 loss: 1.1128 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1128 2023/04/14 15:13:59 - mmengine - INFO - Epoch(train) [92][1180/1879] lr: 2.0000e-04 eta: 1:37:01 time: 0.3247 data_time: 0.0161 memory: 6717 grad_norm: 3.5215 loss: 1.2518 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2518 2023/04/14 15:14:07 - mmengine - INFO - Epoch(train) [92][1200/1879] lr: 2.0000e-04 eta: 1:36:54 time: 0.3932 data_time: 0.0136 memory: 6717 grad_norm: 3.4982 loss: 1.1011 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1011 2023/04/14 15:14:14 - mmengine - INFO - Epoch(train) [92][1220/1879] lr: 2.0000e-04 eta: 1:36:46 time: 0.3583 data_time: 0.0230 memory: 6717 grad_norm: 3.3752 loss: 1.0709 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0709 2023/04/14 15:14:21 - mmengine - INFO - Epoch(train) [92][1240/1879] lr: 2.0000e-04 eta: 1:36:39 time: 0.3717 data_time: 0.0235 memory: 6717 grad_norm: 3.3652 loss: 1.2636 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.2636 2023/04/14 15:14:29 - mmengine - INFO - Epoch(train) [92][1260/1879] lr: 2.0000e-04 eta: 1:36:31 time: 0.3686 data_time: 0.0148 memory: 6717 grad_norm: 3.4590 loss: 1.0047 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0047 2023/04/14 15:14:36 - mmengine - INFO - Epoch(train) [92][1280/1879] lr: 2.0000e-04 eta: 1:36:24 time: 0.3866 data_time: 0.0123 memory: 6717 grad_norm: 3.4541 loss: 1.2234 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2234 2023/04/14 15:14:43 - mmengine - INFO - Epoch(train) [92][1300/1879] lr: 2.0000e-04 eta: 1:36:17 time: 0.3585 data_time: 0.0156 memory: 6717 grad_norm: 3.3728 loss: 1.0707 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0707 2023/04/14 15:14:51 - mmengine - INFO - Epoch(train) [92][1320/1879] lr: 2.0000e-04 eta: 1:36:09 time: 0.3700 data_time: 0.0124 memory: 6717 grad_norm: 3.4217 loss: 1.1514 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1514 2023/04/14 15:14:58 - mmengine - INFO - Epoch(train) [92][1340/1879] lr: 2.0000e-04 eta: 1:36:02 time: 0.3632 data_time: 0.0160 memory: 6717 grad_norm: 3.3958 loss: 0.9821 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9821 2023/04/14 15:15:06 - mmengine - INFO - Epoch(train) [92][1360/1879] lr: 2.0000e-04 eta: 1:35:54 time: 0.3764 data_time: 0.0124 memory: 6717 grad_norm: 3.4536 loss: 1.1189 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1189 2023/04/14 15:15:13 - mmengine - INFO - Epoch(train) [92][1380/1879] lr: 2.0000e-04 eta: 1:35:47 time: 0.3569 data_time: 0.0148 memory: 6717 grad_norm: 3.4449 loss: 1.2064 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2064 2023/04/14 15:15:21 - mmengine - INFO - Epoch(train) [92][1400/1879] lr: 2.0000e-04 eta: 1:35:40 time: 0.4146 data_time: 0.0132 memory: 6717 grad_norm: 3.3681 loss: 1.1732 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1732 2023/04/14 15:15:28 - mmengine - INFO - Epoch(train) [92][1420/1879] lr: 2.0000e-04 eta: 1:35:32 time: 0.3324 data_time: 0.0171 memory: 6717 grad_norm: 3.4795 loss: 1.1145 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1145 2023/04/14 15:15:36 - mmengine - INFO - Epoch(train) [92][1440/1879] lr: 2.0000e-04 eta: 1:35:25 time: 0.3942 data_time: 0.0126 memory: 6717 grad_norm: 3.5064 loss: 1.0985 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0985 2023/04/14 15:15:42 - mmengine - INFO - Epoch(train) [92][1460/1879] lr: 2.0000e-04 eta: 1:35:17 time: 0.3305 data_time: 0.0165 memory: 6717 grad_norm: 3.4802 loss: 1.1522 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1522 2023/04/14 15:15:50 - mmengine - INFO - Epoch(train) [92][1480/1879] lr: 2.0000e-04 eta: 1:35:10 time: 0.3953 data_time: 0.0126 memory: 6717 grad_norm: 3.4244 loss: 1.0586 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0586 2023/04/14 15:15:57 - mmengine - INFO - Epoch(train) [92][1500/1879] lr: 2.0000e-04 eta: 1:35:02 time: 0.3203 data_time: 0.0154 memory: 6717 grad_norm: 3.4360 loss: 1.1117 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1117 2023/04/14 15:16:05 - mmengine - INFO - Epoch(train) [92][1520/1879] lr: 2.0000e-04 eta: 1:34:55 time: 0.4200 data_time: 0.0129 memory: 6717 grad_norm: 3.4943 loss: 1.1941 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1941 2023/04/14 15:16:11 - mmengine - INFO - Epoch(train) [92][1540/1879] lr: 2.0000e-04 eta: 1:34:48 time: 0.3160 data_time: 0.0157 memory: 6717 grad_norm: 3.5387 loss: 0.9472 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9472 2023/04/14 15:16:20 - mmengine - INFO - Epoch(train) [92][1560/1879] lr: 2.0000e-04 eta: 1:34:40 time: 0.4246 data_time: 0.0324 memory: 6717 grad_norm: 3.5213 loss: 1.0908 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0908 2023/04/14 15:16:27 - mmengine - INFO - Epoch(train) [92][1580/1879] lr: 2.0000e-04 eta: 1:34:33 time: 0.3444 data_time: 0.0178 memory: 6717 grad_norm: 3.4229 loss: 1.1560 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1560 2023/04/14 15:16:35 - mmengine - INFO - Epoch(train) [92][1600/1879] lr: 2.0000e-04 eta: 1:34:26 time: 0.4362 data_time: 0.0119 memory: 6717 grad_norm: 3.5293 loss: 1.1647 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1647 2023/04/14 15:16:42 - mmengine - INFO - Epoch(train) [92][1620/1879] lr: 2.0000e-04 eta: 1:34:18 time: 0.3247 data_time: 0.0163 memory: 6717 grad_norm: 3.4391 loss: 1.2409 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2409 2023/04/14 15:16:50 - mmengine - INFO - Epoch(train) [92][1640/1879] lr: 2.0000e-04 eta: 1:34:11 time: 0.3990 data_time: 0.0129 memory: 6717 grad_norm: 3.4115 loss: 0.9990 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9990 2023/04/14 15:16:56 - mmengine - INFO - Epoch(train) [92][1660/1879] lr: 2.0000e-04 eta: 1:34:03 time: 0.3208 data_time: 0.0161 memory: 6717 grad_norm: 3.4317 loss: 1.0427 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0427 2023/04/14 15:17:04 - mmengine - INFO - Epoch(train) [92][1680/1879] lr: 2.0000e-04 eta: 1:33:56 time: 0.3947 data_time: 0.0128 memory: 6717 grad_norm: 3.4318 loss: 1.1065 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1065 2023/04/14 15:17:11 - mmengine - INFO - Epoch(train) [92][1700/1879] lr: 2.0000e-04 eta: 1:33:48 time: 0.3129 data_time: 0.0156 memory: 6717 grad_norm: 3.4205 loss: 1.0894 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0894 2023/04/14 15:17:19 - mmengine - INFO - Epoch(train) [92][1720/1879] lr: 2.0000e-04 eta: 1:33:41 time: 0.4027 data_time: 0.0122 memory: 6717 grad_norm: 3.4618 loss: 1.0575 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0575 2023/04/14 15:17:25 - mmengine - INFO - Epoch(train) [92][1740/1879] lr: 2.0000e-04 eta: 1:33:34 time: 0.3169 data_time: 0.0148 memory: 6717 grad_norm: 3.4156 loss: 1.1292 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1292 2023/04/14 15:17:34 - mmengine - INFO - Epoch(train) [92][1760/1879] lr: 2.0000e-04 eta: 1:33:26 time: 0.4299 data_time: 0.0125 memory: 6717 grad_norm: 3.4706 loss: 1.0363 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0363 2023/04/14 15:17:39 - mmengine - INFO - Epoch(train) [92][1780/1879] lr: 2.0000e-04 eta: 1:33:19 time: 0.2960 data_time: 0.0166 memory: 6717 grad_norm: 3.5670 loss: 0.9668 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9668 2023/04/14 15:17:48 - mmengine - INFO - Epoch(train) [92][1800/1879] lr: 2.0000e-04 eta: 1:33:11 time: 0.4195 data_time: 0.0135 memory: 6717 grad_norm: 3.4890 loss: 1.3106 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3106 2023/04/14 15:17:55 - mmengine - INFO - Epoch(train) [92][1820/1879] lr: 2.0000e-04 eta: 1:33:04 time: 0.3310 data_time: 0.0148 memory: 6717 grad_norm: 3.2868 loss: 1.0106 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0106 2023/04/14 15:18:03 - mmengine - INFO - Epoch(train) [92][1840/1879] lr: 2.0000e-04 eta: 1:32:57 time: 0.4346 data_time: 0.0125 memory: 6717 grad_norm: 3.3989 loss: 1.1144 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1144 2023/04/14 15:18:10 - mmengine - INFO - Epoch(train) [92][1860/1879] lr: 2.0000e-04 eta: 1:32:49 time: 0.3350 data_time: 0.0151 memory: 6717 grad_norm: 3.4491 loss: 1.2965 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2965 2023/04/14 15:18:16 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 15:18:16 - mmengine - INFO - Epoch(train) [92][1879/1879] lr: 2.0000e-04 eta: 1:32:42 time: 0.2966 data_time: 0.0133 memory: 6717 grad_norm: 3.6033 loss: 1.0234 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0234 2023/04/14 15:18:25 - mmengine - INFO - Epoch(val) [92][ 20/155] eta: 0:01:02 time: 0.4608 data_time: 0.4276 memory: 1391 2023/04/14 15:18:31 - mmengine - INFO - Epoch(val) [92][ 40/155] eta: 0:00:44 time: 0.3206 data_time: 0.2875 memory: 1391 2023/04/14 15:18:40 - mmengine - INFO - Epoch(val) [92][ 60/155] eta: 0:00:38 time: 0.4217 data_time: 0.3880 memory: 1391 2023/04/14 15:18:46 - mmengine - INFO - Epoch(val) [92][ 80/155] eta: 0:00:28 time: 0.3229 data_time: 0.2895 memory: 1391 2023/04/14 15:18:55 - mmengine - INFO - Epoch(val) [92][100/155] eta: 0:00:21 time: 0.4263 data_time: 0.3926 memory: 1391 2023/04/14 15:19:01 - mmengine - INFO - Epoch(val) [92][120/155] eta: 0:00:13 time: 0.3281 data_time: 0.2944 memory: 1391 2023/04/14 15:19:11 - mmengine - INFO - Epoch(val) [92][140/155] eta: 0:00:05 time: 0.4871 data_time: 0.4542 memory: 1391 2023/04/14 15:19:18 - mmengine - INFO - Epoch(val) [92][155/155] acc/top1: 0.6686 acc/top5: 0.8737 acc/mean1: 0.6685 data_time: 0.4221 time: 0.4544 2023/04/14 15:19:28 - mmengine - INFO - Epoch(train) [93][ 20/1879] lr: 2.0000e-04 eta: 1:32:35 time: 0.4829 data_time: 0.3280 memory: 6717 grad_norm: 3.5070 loss: 0.9968 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9968 2023/04/14 15:19:35 - mmengine - INFO - Epoch(train) [93][ 40/1879] lr: 2.0000e-04 eta: 1:32:27 time: 0.3372 data_time: 0.1724 memory: 6717 grad_norm: 3.4031 loss: 0.9567 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 0.9567 2023/04/14 15:19:44 - mmengine - INFO - Epoch(train) [93][ 60/1879] lr: 2.0000e-04 eta: 1:32:20 time: 0.4399 data_time: 0.1655 memory: 6717 grad_norm: 3.3636 loss: 1.1316 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1316 2023/04/14 15:19:50 - mmengine - INFO - Epoch(train) [93][ 80/1879] lr: 2.0000e-04 eta: 1:32:13 time: 0.3481 data_time: 0.0857 memory: 6717 grad_norm: 3.4280 loss: 1.1429 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1429 2023/04/14 15:19:59 - mmengine - INFO - Epoch(train) [93][ 100/1879] lr: 2.0000e-04 eta: 1:32:05 time: 0.4217 data_time: 0.1268 memory: 6717 grad_norm: 3.4934 loss: 1.1596 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1596 2023/04/14 15:20:06 - mmengine - INFO - Epoch(train) [93][ 120/1879] lr: 2.0000e-04 eta: 1:31:58 time: 0.3363 data_time: 0.0273 memory: 6717 grad_norm: 3.5006 loss: 1.0700 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0700 2023/04/14 15:20:11 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 15:20:14 - mmengine - INFO - Epoch(train) [93][ 140/1879] lr: 2.0000e-04 eta: 1:31:51 time: 0.4367 data_time: 0.0282 memory: 6717 grad_norm: 3.4203 loss: 1.0321 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0321 2023/04/14 15:20:21 - mmengine - INFO - Epoch(train) [93][ 160/1879] lr: 2.0000e-04 eta: 1:31:43 time: 0.3131 data_time: 0.0122 memory: 6717 grad_norm: 3.4286 loss: 1.2415 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2415 2023/04/14 15:20:29 - mmengine - INFO - Epoch(train) [93][ 180/1879] lr: 2.0000e-04 eta: 1:31:36 time: 0.4068 data_time: 0.0157 memory: 6717 grad_norm: 3.4530 loss: 1.1152 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1152 2023/04/14 15:20:36 - mmengine - INFO - Epoch(train) [93][ 200/1879] lr: 2.0000e-04 eta: 1:31:28 time: 0.3401 data_time: 0.0147 memory: 6717 grad_norm: 3.4790 loss: 1.0777 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0777 2023/04/14 15:20:44 - mmengine - INFO - Epoch(train) [93][ 220/1879] lr: 2.0000e-04 eta: 1:31:21 time: 0.4279 data_time: 0.0138 memory: 6717 grad_norm: 3.5264 loss: 1.1886 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1886 2023/04/14 15:20:51 - mmengine - INFO - Epoch(train) [93][ 240/1879] lr: 2.0000e-04 eta: 1:31:14 time: 0.3347 data_time: 0.0132 memory: 6717 grad_norm: 3.4488 loss: 1.1205 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1205 2023/04/14 15:20:59 - mmengine - INFO - Epoch(train) [93][ 260/1879] lr: 2.0000e-04 eta: 1:31:06 time: 0.3906 data_time: 0.0143 memory: 6717 grad_norm: 3.5057 loss: 1.1269 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1269 2023/04/14 15:21:05 - mmengine - INFO - Epoch(train) [93][ 280/1879] lr: 2.0000e-04 eta: 1:30:59 time: 0.3020 data_time: 0.0143 memory: 6717 grad_norm: 3.4077 loss: 1.0391 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0391 2023/04/14 15:21:13 - mmengine - INFO - Epoch(train) [93][ 300/1879] lr: 2.0000e-04 eta: 1:30:51 time: 0.4183 data_time: 0.0141 memory: 6717 grad_norm: 3.5035 loss: 1.0974 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0974 2023/04/14 15:21:20 - mmengine - INFO - Epoch(train) [93][ 320/1879] lr: 2.0000e-04 eta: 1:30:44 time: 0.3300 data_time: 0.0143 memory: 6717 grad_norm: 3.4020 loss: 1.0454 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0454 2023/04/14 15:21:28 - mmengine - INFO - Epoch(train) [93][ 340/1879] lr: 2.0000e-04 eta: 1:30:37 time: 0.4128 data_time: 0.0134 memory: 6717 grad_norm: 3.4759 loss: 1.1691 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1691 2023/04/14 15:21:35 - mmengine - INFO - Epoch(train) [93][ 360/1879] lr: 2.0000e-04 eta: 1:30:29 time: 0.3332 data_time: 0.0152 memory: 6717 grad_norm: 3.2898 loss: 0.9865 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9865 2023/04/14 15:21:42 - mmengine - INFO - Epoch(train) [93][ 380/1879] lr: 2.0000e-04 eta: 1:30:22 time: 0.3900 data_time: 0.0145 memory: 6717 grad_norm: 3.4667 loss: 1.1903 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1903 2023/04/14 15:21:49 - mmengine - INFO - Epoch(train) [93][ 400/1879] lr: 2.0000e-04 eta: 1:30:14 time: 0.3086 data_time: 0.0138 memory: 6717 grad_norm: 3.5231 loss: 1.0100 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.0100 2023/04/14 15:21:57 - mmengine - INFO - Epoch(train) [93][ 420/1879] lr: 2.0000e-04 eta: 1:30:07 time: 0.4330 data_time: 0.0144 memory: 6717 grad_norm: 3.4675 loss: 1.2063 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2063 2023/04/14 15:22:04 - mmengine - INFO - Epoch(train) [93][ 440/1879] lr: 2.0000e-04 eta: 1:30:00 time: 0.3183 data_time: 0.0142 memory: 6717 grad_norm: 3.5522 loss: 1.1288 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1288 2023/04/14 15:22:12 - mmengine - INFO - Epoch(train) [93][ 460/1879] lr: 2.0000e-04 eta: 1:29:52 time: 0.4090 data_time: 0.0149 memory: 6717 grad_norm: 3.4897 loss: 1.4242 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4242 2023/04/14 15:22:18 - mmengine - INFO - Epoch(train) [93][ 480/1879] lr: 2.0000e-04 eta: 1:29:45 time: 0.3140 data_time: 0.0133 memory: 6717 grad_norm: 3.4246 loss: 1.0658 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0658 2023/04/14 15:22:27 - mmengine - INFO - Epoch(train) [93][ 500/1879] lr: 2.0000e-04 eta: 1:29:37 time: 0.4212 data_time: 0.0147 memory: 6717 grad_norm: 3.4144 loss: 1.1768 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1768 2023/04/14 15:22:33 - mmengine - INFO - Epoch(train) [93][ 520/1879] lr: 2.0000e-04 eta: 1:29:30 time: 0.3272 data_time: 0.0131 memory: 6717 grad_norm: 3.4693 loss: 1.1951 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1951 2023/04/14 15:22:41 - mmengine - INFO - Epoch(train) [93][ 540/1879] lr: 2.0000e-04 eta: 1:29:23 time: 0.3953 data_time: 0.0151 memory: 6717 grad_norm: 3.4542 loss: 1.1555 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1555 2023/04/14 15:22:47 - mmengine - INFO - Epoch(train) [93][ 560/1879] lr: 2.0000e-04 eta: 1:29:15 time: 0.3111 data_time: 0.0128 memory: 6717 grad_norm: 3.4322 loss: 1.0105 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0105 2023/04/14 15:22:56 - mmengine - INFO - Epoch(train) [93][ 580/1879] lr: 2.0000e-04 eta: 1:29:08 time: 0.4125 data_time: 0.0155 memory: 6717 grad_norm: 3.4295 loss: 1.1877 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1877 2023/04/14 15:23:02 - mmengine - INFO - Epoch(train) [93][ 600/1879] lr: 2.0000e-04 eta: 1:29:00 time: 0.3327 data_time: 0.0122 memory: 6717 grad_norm: 3.5250 loss: 1.1541 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1541 2023/04/14 15:23:11 - mmengine - INFO - Epoch(train) [93][ 620/1879] lr: 2.0000e-04 eta: 1:28:53 time: 0.4402 data_time: 0.0153 memory: 6717 grad_norm: 3.3332 loss: 0.9750 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9750 2023/04/14 15:23:18 - mmengine - INFO - Epoch(train) [93][ 640/1879] lr: 2.0000e-04 eta: 1:28:45 time: 0.3377 data_time: 0.0120 memory: 6717 grad_norm: 3.4905 loss: 1.2104 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2104 2023/04/14 15:23:27 - mmengine - INFO - Epoch(train) [93][ 660/1879] lr: 2.0000e-04 eta: 1:28:38 time: 0.4616 data_time: 0.0150 memory: 6717 grad_norm: 3.4660 loss: 0.9013 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9013 2023/04/14 15:23:33 - mmengine - INFO - Epoch(train) [93][ 680/1879] lr: 2.0000e-04 eta: 1:28:31 time: 0.2906 data_time: 0.0119 memory: 6717 grad_norm: 3.4724 loss: 1.0439 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0439 2023/04/14 15:23:41 - mmengine - INFO - Epoch(train) [93][ 700/1879] lr: 2.0000e-04 eta: 1:28:23 time: 0.3929 data_time: 0.0150 memory: 6717 grad_norm: 3.3764 loss: 1.1905 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1905 2023/04/14 15:23:47 - mmengine - INFO - Epoch(train) [93][ 720/1879] lr: 2.0000e-04 eta: 1:28:16 time: 0.3381 data_time: 0.0127 memory: 6717 grad_norm: 3.4351 loss: 1.0502 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0502 2023/04/14 15:23:56 - mmengine - INFO - Epoch(train) [93][ 740/1879] lr: 2.0000e-04 eta: 1:28:09 time: 0.4307 data_time: 0.0155 memory: 6717 grad_norm: 3.4409 loss: 1.1289 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1289 2023/04/14 15:24:02 - mmengine - INFO - Epoch(train) [93][ 760/1879] lr: 2.0000e-04 eta: 1:28:01 time: 0.3017 data_time: 0.0133 memory: 6717 grad_norm: 3.4893 loss: 1.0707 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0707 2023/04/14 15:24:10 - mmengine - INFO - Epoch(train) [93][ 780/1879] lr: 2.0000e-04 eta: 1:27:54 time: 0.3974 data_time: 0.0164 memory: 6717 grad_norm: 3.4379 loss: 1.1413 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1413 2023/04/14 15:24:17 - mmengine - INFO - Epoch(train) [93][ 800/1879] lr: 2.0000e-04 eta: 1:27:46 time: 0.3261 data_time: 0.0122 memory: 6717 grad_norm: 3.3714 loss: 1.1282 top1_acc: 0.5000 top5_acc: 0.5625 loss_cls: 1.1282 2023/04/14 15:24:25 - mmengine - INFO - Epoch(train) [93][ 820/1879] lr: 2.0000e-04 eta: 1:27:39 time: 0.4014 data_time: 0.0154 memory: 6717 grad_norm: 3.4141 loss: 1.1209 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1209 2023/04/14 15:24:31 - mmengine - INFO - Epoch(train) [93][ 840/1879] lr: 2.0000e-04 eta: 1:27:31 time: 0.3383 data_time: 0.0132 memory: 6717 grad_norm: 3.3396 loss: 1.0492 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0492 2023/04/14 15:24:40 - mmengine - INFO - Epoch(train) [93][ 860/1879] lr: 2.0000e-04 eta: 1:27:24 time: 0.4462 data_time: 0.0168 memory: 6717 grad_norm: 3.5228 loss: 1.1303 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.1303 2023/04/14 15:24:47 - mmengine - INFO - Epoch(train) [93][ 880/1879] lr: 2.0000e-04 eta: 1:27:17 time: 0.3420 data_time: 0.0145 memory: 6717 grad_norm: 3.4422 loss: 1.0743 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0743 2023/04/14 15:24:54 - mmengine - INFO - Epoch(train) [93][ 900/1879] lr: 2.0000e-04 eta: 1:27:09 time: 0.3634 data_time: 0.0150 memory: 6717 grad_norm: 3.4945 loss: 1.1246 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1246 2023/04/14 15:25:02 - mmengine - INFO - Epoch(train) [93][ 920/1879] lr: 2.0000e-04 eta: 1:27:02 time: 0.3857 data_time: 0.0149 memory: 6717 grad_norm: 3.4654 loss: 1.1427 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1427 2023/04/14 15:25:09 - mmengine - INFO - Epoch(train) [93][ 940/1879] lr: 2.0000e-04 eta: 1:26:54 time: 0.3480 data_time: 0.0138 memory: 6717 grad_norm: 3.4606 loss: 1.0778 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0778 2023/04/14 15:25:17 - mmengine - INFO - Epoch(train) [93][ 960/1879] lr: 2.0000e-04 eta: 1:26:47 time: 0.4000 data_time: 0.0153 memory: 6717 grad_norm: 3.4199 loss: 1.2545 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2545 2023/04/14 15:25:24 - mmengine - INFO - Epoch(train) [93][ 980/1879] lr: 2.0000e-04 eta: 1:26:40 time: 0.3389 data_time: 0.0142 memory: 6717 grad_norm: 3.4000 loss: 0.9795 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9795 2023/04/14 15:25:31 - mmengine - INFO - Epoch(train) [93][1000/1879] lr: 2.0000e-04 eta: 1:26:32 time: 0.3543 data_time: 0.0140 memory: 6717 grad_norm: 3.4868 loss: 1.0701 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0701 2023/04/14 15:25:39 - mmengine - INFO - Epoch(train) [93][1020/1879] lr: 2.0000e-04 eta: 1:26:25 time: 0.3818 data_time: 0.0145 memory: 6717 grad_norm: 3.5228 loss: 1.1834 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1834 2023/04/14 15:25:45 - mmengine - INFO - Epoch(train) [93][1040/1879] lr: 2.0000e-04 eta: 1:26:17 time: 0.3408 data_time: 0.0144 memory: 6717 grad_norm: 3.5014 loss: 1.0193 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0193 2023/04/14 15:25:53 - mmengine - INFO - Epoch(train) [93][1060/1879] lr: 2.0000e-04 eta: 1:26:10 time: 0.3709 data_time: 0.0150 memory: 6717 grad_norm: 3.5035 loss: 1.2459 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2459 2023/04/14 15:26:00 - mmengine - INFO - Epoch(train) [93][1080/1879] lr: 2.0000e-04 eta: 1:26:03 time: 0.3755 data_time: 0.0135 memory: 6717 grad_norm: 3.4536 loss: 0.9983 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9983 2023/04/14 15:26:07 - mmengine - INFO - Epoch(train) [93][1100/1879] lr: 2.0000e-04 eta: 1:25:55 time: 0.3468 data_time: 0.0150 memory: 6717 grad_norm: 3.5531 loss: 1.1564 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1564 2023/04/14 15:26:15 - mmengine - INFO - Epoch(train) [93][1120/1879] lr: 2.0000e-04 eta: 1:25:48 time: 0.3695 data_time: 0.0139 memory: 6717 grad_norm: 3.3775 loss: 0.9331 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9331 2023/04/14 15:26:19 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 15:26:22 - mmengine - INFO - Epoch(train) [93][1140/1879] lr: 2.0000e-04 eta: 1:25:40 time: 0.3723 data_time: 0.0158 memory: 6717 grad_norm: 3.3732 loss: 1.1253 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1253 2023/04/14 15:26:30 - mmengine - INFO - Epoch(train) [93][1160/1879] lr: 2.0000e-04 eta: 1:25:33 time: 0.3751 data_time: 0.0134 memory: 6717 grad_norm: 3.4553 loss: 0.9864 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9864 2023/04/14 15:26:37 - mmengine - INFO - Epoch(train) [93][1180/1879] lr: 2.0000e-04 eta: 1:25:26 time: 0.3750 data_time: 0.0152 memory: 6717 grad_norm: 3.3828 loss: 1.0831 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0831 2023/04/14 15:26:44 - mmengine - INFO - Epoch(train) [93][1200/1879] lr: 2.0000e-04 eta: 1:25:18 time: 0.3503 data_time: 0.0132 memory: 6717 grad_norm: 3.4025 loss: 1.2334 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2334 2023/04/14 15:26:52 - mmengine - INFO - Epoch(train) [93][1220/1879] lr: 2.0000e-04 eta: 1:25:11 time: 0.3842 data_time: 0.0135 memory: 6717 grad_norm: 3.4528 loss: 1.2403 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2403 2023/04/14 15:26:59 - mmengine - INFO - Epoch(train) [93][1240/1879] lr: 2.0000e-04 eta: 1:25:03 time: 0.3380 data_time: 0.0152 memory: 6717 grad_norm: 3.4067 loss: 1.1703 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1703 2023/04/14 15:27:07 - mmengine - INFO - Epoch(train) [93][1260/1879] lr: 2.0000e-04 eta: 1:24:56 time: 0.3986 data_time: 0.0141 memory: 6717 grad_norm: 3.4576 loss: 1.1890 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1890 2023/04/14 15:27:14 - mmengine - INFO - Epoch(train) [93][1280/1879] lr: 2.0000e-04 eta: 1:24:49 time: 0.3562 data_time: 0.0141 memory: 6717 grad_norm: 3.4843 loss: 1.2223 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2223 2023/04/14 15:27:21 - mmengine - INFO - Epoch(train) [93][1300/1879] lr: 2.0000e-04 eta: 1:24:41 time: 0.3792 data_time: 0.0140 memory: 6717 grad_norm: 3.4855 loss: 1.1090 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1090 2023/04/14 15:27:28 - mmengine - INFO - Epoch(train) [93][1320/1879] lr: 2.0000e-04 eta: 1:24:34 time: 0.3238 data_time: 0.0155 memory: 6717 grad_norm: 3.4392 loss: 1.1912 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1912 2023/04/14 15:27:36 - mmengine - INFO - Epoch(train) [93][1340/1879] lr: 2.0000e-04 eta: 1:24:26 time: 0.3847 data_time: 0.0125 memory: 6717 grad_norm: 3.4940 loss: 1.2026 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2026 2023/04/14 15:27:42 - mmengine - INFO - Epoch(train) [93][1360/1879] lr: 2.0000e-04 eta: 1:24:19 time: 0.3422 data_time: 0.0149 memory: 6717 grad_norm: 3.4635 loss: 1.0454 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0454 2023/04/14 15:27:51 - mmengine - INFO - Epoch(train) [93][1380/1879] lr: 2.0000e-04 eta: 1:24:12 time: 0.4157 data_time: 0.0135 memory: 6717 grad_norm: 3.3561 loss: 1.1396 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1396 2023/04/14 15:27:57 - mmengine - INFO - Epoch(train) [93][1400/1879] lr: 2.0000e-04 eta: 1:24:04 time: 0.3293 data_time: 0.0134 memory: 6717 grad_norm: 3.4732 loss: 1.0201 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0201 2023/04/14 15:28:06 - mmengine - INFO - Epoch(train) [93][1420/1879] lr: 2.0000e-04 eta: 1:23:57 time: 0.4288 data_time: 0.0149 memory: 6717 grad_norm: 3.4734 loss: 1.2404 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2404 2023/04/14 15:28:13 - mmengine - INFO - Epoch(train) [93][1440/1879] lr: 2.0000e-04 eta: 1:23:49 time: 0.3524 data_time: 0.0121 memory: 6717 grad_norm: 3.4392 loss: 1.1592 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1592 2023/04/14 15:28:21 - mmengine - INFO - Epoch(train) [93][1460/1879] lr: 2.0000e-04 eta: 1:23:42 time: 0.3892 data_time: 0.0149 memory: 6717 grad_norm: 3.4861 loss: 1.0999 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0999 2023/04/14 15:28:28 - mmengine - INFO - Epoch(train) [93][1480/1879] lr: 2.0000e-04 eta: 1:23:35 time: 0.3362 data_time: 0.0131 memory: 6717 grad_norm: 3.4548 loss: 1.2151 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2151 2023/04/14 15:28:36 - mmengine - INFO - Epoch(train) [93][1500/1879] lr: 2.0000e-04 eta: 1:23:27 time: 0.4165 data_time: 0.0148 memory: 6717 grad_norm: 3.3658 loss: 0.9181 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9181 2023/04/14 15:28:42 - mmengine - INFO - Epoch(train) [93][1520/1879] lr: 2.0000e-04 eta: 1:23:20 time: 0.3210 data_time: 0.0507 memory: 6717 grad_norm: 3.4463 loss: 1.0741 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0741 2023/04/14 15:28:50 - mmengine - INFO - Epoch(train) [93][1540/1879] lr: 2.0000e-04 eta: 1:23:12 time: 0.4003 data_time: 0.1439 memory: 6717 grad_norm: 3.4720 loss: 1.1100 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.1100 2023/04/14 15:28:57 - mmengine - INFO - Epoch(train) [93][1560/1879] lr: 2.0000e-04 eta: 1:23:05 time: 0.3483 data_time: 0.1054 memory: 6717 grad_norm: 3.4100 loss: 1.0491 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0491 2023/04/14 15:29:05 - mmengine - INFO - Epoch(train) [93][1580/1879] lr: 2.0000e-04 eta: 1:22:58 time: 0.3987 data_time: 0.0192 memory: 6717 grad_norm: 3.3259 loss: 0.9334 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9334 2023/04/14 15:29:11 - mmengine - INFO - Epoch(train) [93][1600/1879] lr: 2.0000e-04 eta: 1:22:50 time: 0.3099 data_time: 0.0650 memory: 6717 grad_norm: 3.4283 loss: 1.1148 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1148 2023/04/14 15:29:20 - mmengine - INFO - Epoch(train) [93][1620/1879] lr: 2.0000e-04 eta: 1:22:43 time: 0.4262 data_time: 0.2097 memory: 6717 grad_norm: 3.4343 loss: 1.0496 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0496 2023/04/14 15:29:28 - mmengine - INFO - Epoch(train) [93][1640/1879] lr: 2.0000e-04 eta: 1:22:35 time: 0.4116 data_time: 0.0984 memory: 6717 grad_norm: 3.4856 loss: 1.2072 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2072 2023/04/14 15:29:35 - mmengine - INFO - Epoch(train) [93][1660/1879] lr: 2.0000e-04 eta: 1:22:28 time: 0.3203 data_time: 0.0130 memory: 6717 grad_norm: 3.5255 loss: 1.2012 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2012 2023/04/14 15:29:41 - mmengine - INFO - Epoch(train) [93][1680/1879] lr: 2.0000e-04 eta: 1:22:20 time: 0.3357 data_time: 0.1559 memory: 6717 grad_norm: 3.4305 loss: 1.1039 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1039 2023/04/14 15:29:49 - mmengine - INFO - Epoch(train) [93][1700/1879] lr: 2.0000e-04 eta: 1:22:13 time: 0.3628 data_time: 0.1970 memory: 6717 grad_norm: 3.4526 loss: 1.0954 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0954 2023/04/14 15:29:56 - mmengine - INFO - Epoch(train) [93][1720/1879] lr: 2.0000e-04 eta: 1:22:06 time: 0.3789 data_time: 0.1904 memory: 6717 grad_norm: 3.4434 loss: 0.9728 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9728 2023/04/14 15:30:04 - mmengine - INFO - Epoch(train) [93][1740/1879] lr: 2.0000e-04 eta: 1:21:58 time: 0.3743 data_time: 0.0995 memory: 6717 grad_norm: 3.3411 loss: 1.0155 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0155 2023/04/14 15:30:11 - mmengine - INFO - Epoch(train) [93][1760/1879] lr: 2.0000e-04 eta: 1:21:51 time: 0.3516 data_time: 0.1715 memory: 6717 grad_norm: 3.3800 loss: 1.2180 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2180 2023/04/14 15:30:18 - mmengine - INFO - Epoch(train) [93][1780/1879] lr: 2.0000e-04 eta: 1:21:43 time: 0.3871 data_time: 0.2156 memory: 6717 grad_norm: 3.5935 loss: 1.2202 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2202 2023/04/14 15:30:26 - mmengine - INFO - Epoch(train) [93][1800/1879] lr: 2.0000e-04 eta: 1:21:36 time: 0.3616 data_time: 0.1994 memory: 6717 grad_norm: 3.4386 loss: 1.1906 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1906 2023/04/14 15:30:33 - mmengine - INFO - Epoch(train) [93][1820/1879] lr: 2.0000e-04 eta: 1:21:29 time: 0.3778 data_time: 0.1034 memory: 6717 grad_norm: 3.4367 loss: 1.0536 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0536 2023/04/14 15:30:40 - mmengine - INFO - Epoch(train) [93][1840/1879] lr: 2.0000e-04 eta: 1:21:21 time: 0.3509 data_time: 0.0477 memory: 6717 grad_norm: 3.4895 loss: 1.2012 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2012 2023/04/14 15:30:49 - mmengine - INFO - Epoch(train) [93][1860/1879] lr: 2.0000e-04 eta: 1:21:14 time: 0.4381 data_time: 0.0190 memory: 6717 grad_norm: 3.4427 loss: 1.0897 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0897 2023/04/14 15:30:55 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 15:30:55 - mmengine - INFO - Epoch(train) [93][1879/1879] lr: 2.0000e-04 eta: 1:21:07 time: 0.4122 data_time: 0.0115 memory: 6717 grad_norm: 3.6271 loss: 1.1897 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 1.1897 2023/04/14 15:30:55 - mmengine - INFO - Saving checkpoint at 93 epochs 2023/04/14 15:31:05 - mmengine - INFO - Epoch(val) [93][ 20/155] eta: 0:00:59 time: 0.4383 data_time: 0.4045 memory: 1391 2023/04/14 15:31:11 - mmengine - INFO - Epoch(val) [93][ 40/155] eta: 0:00:44 time: 0.3405 data_time: 0.3070 memory: 1391 2023/04/14 15:31:19 - mmengine - INFO - Epoch(val) [93][ 60/155] eta: 0:00:36 time: 0.3702 data_time: 0.3363 memory: 1391 2023/04/14 15:31:26 - mmengine - INFO - Epoch(val) [93][ 80/155] eta: 0:00:28 time: 0.3733 data_time: 0.3400 memory: 1391 2023/04/14 15:31:35 - mmengine - INFO - Epoch(val) [93][100/155] eta: 0:00:21 time: 0.4265 data_time: 0.3927 memory: 1391 2023/04/14 15:31:41 - mmengine - INFO - Epoch(val) [93][120/155] eta: 0:00:13 time: 0.3152 data_time: 0.2818 memory: 1391 2023/04/14 15:31:49 - mmengine - INFO - Epoch(val) [93][140/155] eta: 0:00:05 time: 0.3747 data_time: 0.3413 memory: 1391 2023/04/14 15:31:57 - mmengine - INFO - Epoch(val) [93][155/155] acc/top1: 0.6689 acc/top5: 0.8744 acc/mean1: 0.6689 data_time: 0.3182 time: 0.3513 2023/04/14 15:32:07 - mmengine - INFO - Epoch(train) [94][ 20/1879] lr: 2.0000e-04 eta: 1:21:00 time: 0.4927 data_time: 0.3425 memory: 6717 grad_norm: 3.4630 loss: 1.1038 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1038 2023/04/14 15:32:14 - mmengine - INFO - Epoch(train) [94][ 40/1879] lr: 2.0000e-04 eta: 1:20:52 time: 0.3580 data_time: 0.1532 memory: 6717 grad_norm: 3.4531 loss: 1.0543 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0543 2023/04/14 15:32:22 - mmengine - INFO - Epoch(train) [94][ 60/1879] lr: 2.0000e-04 eta: 1:20:45 time: 0.4071 data_time: 0.1961 memory: 6717 grad_norm: 3.4436 loss: 1.1566 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1566 2023/04/14 15:32:30 - mmengine - INFO - Epoch(train) [94][ 80/1879] lr: 2.0000e-04 eta: 1:20:37 time: 0.3754 data_time: 0.0376 memory: 6717 grad_norm: 3.5301 loss: 1.1770 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1770 2023/04/14 15:32:38 - mmengine - INFO - Epoch(train) [94][ 100/1879] lr: 2.0000e-04 eta: 1:20:30 time: 0.3892 data_time: 0.0142 memory: 6717 grad_norm: 3.3904 loss: 1.2969 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2969 2023/04/14 15:32:44 - mmengine - INFO - Epoch(train) [94][ 120/1879] lr: 2.0000e-04 eta: 1:20:23 time: 0.3388 data_time: 0.0143 memory: 6717 grad_norm: 3.4639 loss: 1.1786 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1786 2023/04/14 15:32:52 - mmengine - INFO - Epoch(train) [94][ 140/1879] lr: 2.0000e-04 eta: 1:20:15 time: 0.4033 data_time: 0.0153 memory: 6717 grad_norm: 3.5274 loss: 1.0094 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0094 2023/04/14 15:32:59 - mmengine - INFO - Epoch(train) [94][ 160/1879] lr: 2.0000e-04 eta: 1:20:08 time: 0.3327 data_time: 0.0132 memory: 6717 grad_norm: 3.4501 loss: 1.0134 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0134 2023/04/14 15:33:08 - mmengine - INFO - Epoch(train) [94][ 180/1879] lr: 2.0000e-04 eta: 1:20:01 time: 0.4445 data_time: 0.0156 memory: 6717 grad_norm: 3.4971 loss: 1.1224 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.1224 2023/04/14 15:33:14 - mmengine - INFO - Epoch(train) [94][ 200/1879] lr: 2.0000e-04 eta: 1:19:53 time: 0.3064 data_time: 0.0139 memory: 6717 grad_norm: 3.4470 loss: 1.0503 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0503 2023/04/14 15:33:22 - mmengine - INFO - Epoch(train) [94][ 220/1879] lr: 2.0000e-04 eta: 1:19:46 time: 0.3980 data_time: 0.0154 memory: 6717 grad_norm: 3.4329 loss: 1.1309 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1309 2023/04/14 15:33:29 - mmengine - INFO - Epoch(train) [94][ 240/1879] lr: 2.0000e-04 eta: 1:19:38 time: 0.3532 data_time: 0.0121 memory: 6717 grad_norm: 3.4449 loss: 1.2749 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2749 2023/04/14 15:33:34 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 15:33:36 - mmengine - INFO - Epoch(train) [94][ 260/1879] lr: 2.0000e-04 eta: 1:19:31 time: 0.3516 data_time: 0.0159 memory: 6717 grad_norm: 3.4783 loss: 1.0599 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0599 2023/04/14 15:33:44 - mmengine - INFO - Epoch(train) [94][ 280/1879] lr: 2.0000e-04 eta: 1:19:23 time: 0.3938 data_time: 0.0489 memory: 6717 grad_norm: 3.4303 loss: 1.3031 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3031 2023/04/14 15:33:51 - mmengine - INFO - Epoch(train) [94][ 300/1879] lr: 2.0000e-04 eta: 1:19:16 time: 0.3511 data_time: 0.0721 memory: 6717 grad_norm: 3.5418 loss: 1.0602 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0602 2023/04/14 15:33:59 - mmengine - INFO - Epoch(train) [94][ 320/1879] lr: 2.0000e-04 eta: 1:19:09 time: 0.3923 data_time: 0.1541 memory: 6717 grad_norm: 3.3159 loss: 0.9984 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9984 2023/04/14 15:34:06 - mmengine - INFO - Epoch(train) [94][ 340/1879] lr: 2.0000e-04 eta: 1:19:01 time: 0.3707 data_time: 0.0921 memory: 6717 grad_norm: 3.4727 loss: 1.1086 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1086 2023/04/14 15:34:14 - mmengine - INFO - Epoch(train) [94][ 360/1879] lr: 2.0000e-04 eta: 1:18:54 time: 0.3756 data_time: 0.1047 memory: 6717 grad_norm: 3.3634 loss: 0.8845 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.8845 2023/04/14 15:34:21 - mmengine - INFO - Epoch(train) [94][ 380/1879] lr: 2.0000e-04 eta: 1:18:46 time: 0.3500 data_time: 0.0205 memory: 6717 grad_norm: 3.4035 loss: 1.1659 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1659 2023/04/14 15:34:28 - mmengine - INFO - Epoch(train) [94][ 400/1879] lr: 2.0000e-04 eta: 1:18:39 time: 0.3757 data_time: 0.1068 memory: 6717 grad_norm: 3.4153 loss: 1.0283 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0283 2023/04/14 15:34:35 - mmengine - INFO - Epoch(train) [94][ 420/1879] lr: 2.0000e-04 eta: 1:18:32 time: 0.3327 data_time: 0.1015 memory: 6717 grad_norm: 3.4934 loss: 1.2135 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2135 2023/04/14 15:34:43 - mmengine - INFO - Epoch(train) [94][ 440/1879] lr: 2.0000e-04 eta: 1:18:24 time: 0.4026 data_time: 0.1051 memory: 6717 grad_norm: 3.4253 loss: 1.1731 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1731 2023/04/14 15:34:50 - mmengine - INFO - Epoch(train) [94][ 460/1879] lr: 2.0000e-04 eta: 1:18:17 time: 0.3181 data_time: 0.0708 memory: 6717 grad_norm: 3.4592 loss: 1.0584 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0584 2023/04/14 15:34:57 - mmengine - INFO - Epoch(train) [94][ 480/1879] lr: 2.0000e-04 eta: 1:18:09 time: 0.3947 data_time: 0.0715 memory: 6717 grad_norm: 3.3871 loss: 1.0585 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0585 2023/04/14 15:35:04 - mmengine - INFO - Epoch(train) [94][ 500/1879] lr: 2.0000e-04 eta: 1:18:02 time: 0.3317 data_time: 0.0269 memory: 6717 grad_norm: 3.3806 loss: 1.0238 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0238 2023/04/14 15:35:13 - mmengine - INFO - Epoch(train) [94][ 520/1879] lr: 2.0000e-04 eta: 1:17:55 time: 0.4290 data_time: 0.0158 memory: 6717 grad_norm: 3.4930 loss: 1.1639 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1639 2023/04/14 15:35:19 - mmengine - INFO - Epoch(train) [94][ 540/1879] lr: 2.0000e-04 eta: 1:17:47 time: 0.3234 data_time: 0.0170 memory: 6717 grad_norm: 3.5531 loss: 1.2152 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2152 2023/04/14 15:35:28 - mmengine - INFO - Epoch(train) [94][ 560/1879] lr: 2.0000e-04 eta: 1:17:40 time: 0.4240 data_time: 0.0345 memory: 6717 grad_norm: 3.3845 loss: 1.0081 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0081 2023/04/14 15:35:34 - mmengine - INFO - Epoch(train) [94][ 580/1879] lr: 2.0000e-04 eta: 1:17:32 time: 0.3102 data_time: 0.0401 memory: 6717 grad_norm: 3.4455 loss: 1.1137 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1137 2023/04/14 15:35:42 - mmengine - INFO - Epoch(train) [94][ 600/1879] lr: 2.0000e-04 eta: 1:17:25 time: 0.3982 data_time: 0.1804 memory: 6717 grad_norm: 3.4588 loss: 1.1114 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.1114 2023/04/14 15:35:48 - mmengine - INFO - Epoch(train) [94][ 620/1879] lr: 2.0000e-04 eta: 1:17:18 time: 0.3135 data_time: 0.1299 memory: 6717 grad_norm: 3.3598 loss: 0.9959 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9959 2023/04/14 15:35:56 - mmengine - INFO - Epoch(train) [94][ 640/1879] lr: 2.0000e-04 eta: 1:17:10 time: 0.4159 data_time: 0.2656 memory: 6717 grad_norm: 3.4369 loss: 1.2146 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2146 2023/04/14 15:36:03 - mmengine - INFO - Epoch(train) [94][ 660/1879] lr: 2.0000e-04 eta: 1:17:03 time: 0.3374 data_time: 0.1971 memory: 6717 grad_norm: 3.4774 loss: 1.2058 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2058 2023/04/14 15:36:11 - mmengine - INFO - Epoch(train) [94][ 680/1879] lr: 2.0000e-04 eta: 1:16:55 time: 0.3982 data_time: 0.2520 memory: 6717 grad_norm: 3.4422 loss: 0.9606 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9606 2023/04/14 15:36:18 - mmengine - INFO - Epoch(train) [94][ 700/1879] lr: 2.0000e-04 eta: 1:16:48 time: 0.3298 data_time: 0.1345 memory: 6717 grad_norm: 3.4595 loss: 1.2367 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2367 2023/04/14 15:36:27 - mmengine - INFO - Epoch(train) [94][ 720/1879] lr: 2.0000e-04 eta: 1:16:41 time: 0.4375 data_time: 0.0966 memory: 6717 grad_norm: 3.5067 loss: 1.1018 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1018 2023/04/14 15:36:33 - mmengine - INFO - Epoch(train) [94][ 740/1879] lr: 2.0000e-04 eta: 1:16:33 time: 0.3082 data_time: 0.0308 memory: 6717 grad_norm: 3.3852 loss: 1.0238 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0238 2023/04/14 15:36:41 - mmengine - INFO - Epoch(train) [94][ 760/1879] lr: 2.0000e-04 eta: 1:16:26 time: 0.4196 data_time: 0.1249 memory: 6717 grad_norm: 3.4124 loss: 1.1115 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1115 2023/04/14 15:36:48 - mmengine - INFO - Epoch(train) [94][ 780/1879] lr: 2.0000e-04 eta: 1:16:18 time: 0.3324 data_time: 0.0417 memory: 6717 grad_norm: 3.4359 loss: 1.0753 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0753 2023/04/14 15:36:56 - mmengine - INFO - Epoch(train) [94][ 800/1879] lr: 2.0000e-04 eta: 1:16:11 time: 0.4072 data_time: 0.1235 memory: 6717 grad_norm: 3.3745 loss: 1.0405 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0405 2023/04/14 15:37:02 - mmengine - INFO - Epoch(train) [94][ 820/1879] lr: 2.0000e-04 eta: 1:16:03 time: 0.2939 data_time: 0.0569 memory: 6717 grad_norm: 3.3911 loss: 1.0708 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0708 2023/04/14 15:37:10 - mmengine - INFO - Epoch(train) [94][ 840/1879] lr: 2.0000e-04 eta: 1:15:56 time: 0.4072 data_time: 0.1528 memory: 6717 grad_norm: 3.3933 loss: 0.9615 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9615 2023/04/14 15:37:17 - mmengine - INFO - Epoch(train) [94][ 860/1879] lr: 2.0000e-04 eta: 1:15:49 time: 0.3601 data_time: 0.0612 memory: 6717 grad_norm: 3.4295 loss: 1.1256 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1256 2023/04/14 15:37:24 - mmengine - INFO - Epoch(train) [94][ 880/1879] lr: 2.0000e-04 eta: 1:15:41 time: 0.3604 data_time: 0.0347 memory: 6717 grad_norm: 3.4407 loss: 1.0485 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0485 2023/04/14 15:37:32 - mmengine - INFO - Epoch(train) [94][ 900/1879] lr: 2.0000e-04 eta: 1:15:34 time: 0.3804 data_time: 0.0237 memory: 6717 grad_norm: 3.4501 loss: 1.1644 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1644 2023/04/14 15:37:39 - mmengine - INFO - Epoch(train) [94][ 920/1879] lr: 2.0000e-04 eta: 1:15:27 time: 0.3721 data_time: 0.0147 memory: 6717 grad_norm: 3.4251 loss: 1.0200 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0200 2023/04/14 15:37:47 - mmengine - INFO - Epoch(train) [94][ 940/1879] lr: 2.0000e-04 eta: 1:15:19 time: 0.3823 data_time: 0.0131 memory: 6717 grad_norm: 3.4804 loss: 1.2622 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2622 2023/04/14 15:37:55 - mmengine - INFO - Epoch(train) [94][ 960/1879] lr: 2.0000e-04 eta: 1:15:12 time: 0.3814 data_time: 0.0153 memory: 6717 grad_norm: 3.3783 loss: 1.1649 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1649 2023/04/14 15:38:02 - mmengine - INFO - Epoch(train) [94][ 980/1879] lr: 2.0000e-04 eta: 1:15:04 time: 0.3425 data_time: 0.0131 memory: 6717 grad_norm: 3.4900 loss: 1.3556 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.3556 2023/04/14 15:38:09 - mmengine - INFO - Epoch(train) [94][1000/1879] lr: 2.0000e-04 eta: 1:14:57 time: 0.3735 data_time: 0.0144 memory: 6717 grad_norm: 3.4395 loss: 1.0575 top1_acc: 0.4375 top5_acc: 0.6250 loss_cls: 1.0575 2023/04/14 15:38:17 - mmengine - INFO - Epoch(train) [94][1020/1879] lr: 2.0000e-04 eta: 1:14:50 time: 0.4059 data_time: 0.0141 memory: 6717 grad_norm: 3.4443 loss: 1.1404 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1404 2023/04/14 15:38:24 - mmengine - INFO - Epoch(train) [94][1040/1879] lr: 2.0000e-04 eta: 1:14:42 time: 0.3324 data_time: 0.0145 memory: 6717 grad_norm: 3.3766 loss: 1.1081 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1081 2023/04/14 15:38:32 - mmengine - INFO - Epoch(train) [94][1060/1879] lr: 2.0000e-04 eta: 1:14:35 time: 0.4000 data_time: 0.0144 memory: 6717 grad_norm: 3.4495 loss: 1.0717 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0717 2023/04/14 15:38:39 - mmengine - INFO - Epoch(train) [94][1080/1879] lr: 2.0000e-04 eta: 1:14:27 time: 0.3350 data_time: 0.0136 memory: 6717 grad_norm: 3.4520 loss: 1.1908 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1908 2023/04/14 15:38:47 - mmengine - INFO - Epoch(train) [94][1100/1879] lr: 2.0000e-04 eta: 1:14:20 time: 0.4041 data_time: 0.0144 memory: 6717 grad_norm: 3.5339 loss: 1.2155 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2155 2023/04/14 15:38:53 - mmengine - INFO - Epoch(train) [94][1120/1879] lr: 2.0000e-04 eta: 1:14:12 time: 0.3245 data_time: 0.0147 memory: 6717 grad_norm: 3.4355 loss: 1.1572 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1572 2023/04/14 15:39:01 - mmengine - INFO - Epoch(train) [94][1140/1879] lr: 2.0000e-04 eta: 1:14:05 time: 0.4023 data_time: 0.0162 memory: 6717 grad_norm: 3.3689 loss: 1.0214 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0214 2023/04/14 15:39:08 - mmengine - INFO - Epoch(train) [94][1160/1879] lr: 2.0000e-04 eta: 1:13:58 time: 0.3447 data_time: 0.0375 memory: 6717 grad_norm: 3.4521 loss: 1.1756 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1756 2023/04/14 15:39:17 - mmengine - INFO - Epoch(train) [94][1180/1879] lr: 2.0000e-04 eta: 1:13:50 time: 0.4282 data_time: 0.0346 memory: 6717 grad_norm: 3.3983 loss: 1.1668 top1_acc: 0.4375 top5_acc: 0.6875 loss_cls: 1.1668 2023/04/14 15:39:23 - mmengine - INFO - Epoch(train) [94][1200/1879] lr: 2.0000e-04 eta: 1:13:43 time: 0.3009 data_time: 0.0133 memory: 6717 grad_norm: 3.3901 loss: 1.3045 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.3045 2023/04/14 15:39:31 - mmengine - INFO - Epoch(train) [94][1220/1879] lr: 2.0000e-04 eta: 1:13:36 time: 0.4199 data_time: 0.2080 memory: 6717 grad_norm: 3.4302 loss: 1.0604 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0604 2023/04/14 15:39:38 - mmengine - INFO - Epoch(train) [94][1240/1879] lr: 2.0000e-04 eta: 1:13:28 time: 0.3336 data_time: 0.1129 memory: 6717 grad_norm: 3.5144 loss: 1.0199 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0199 2023/04/14 15:39:43 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 15:39:46 - mmengine - INFO - Epoch(train) [94][1260/1879] lr: 2.0000e-04 eta: 1:13:21 time: 0.4270 data_time: 0.2865 memory: 6717 grad_norm: 3.4114 loss: 1.1157 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1157 2023/04/14 15:39:53 - mmengine - INFO - Epoch(train) [94][1280/1879] lr: 2.0000e-04 eta: 1:13:13 time: 0.3338 data_time: 0.1914 memory: 6717 grad_norm: 3.4164 loss: 1.2505 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.2505 2023/04/14 15:40:01 - mmengine - INFO - Epoch(train) [94][1300/1879] lr: 2.0000e-04 eta: 1:13:06 time: 0.4212 data_time: 0.2805 memory: 6717 grad_norm: 3.4909 loss: 1.1194 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1194 2023/04/14 15:40:08 - mmengine - INFO - Epoch(train) [94][1320/1879] lr: 2.0000e-04 eta: 1:12:59 time: 0.3443 data_time: 0.2013 memory: 6717 grad_norm: 3.3690 loss: 1.0676 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0676 2023/04/14 15:40:17 - mmengine - INFO - Epoch(train) [94][1340/1879] lr: 2.0000e-04 eta: 1:12:51 time: 0.4272 data_time: 0.2893 memory: 6717 grad_norm: 3.3380 loss: 1.2080 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2080 2023/04/14 15:40:23 - mmengine - INFO - Epoch(train) [94][1360/1879] lr: 2.0000e-04 eta: 1:12:44 time: 0.3276 data_time: 0.1872 memory: 6717 grad_norm: 3.4935 loss: 1.1787 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1787 2023/04/14 15:40:32 - mmengine - INFO - Epoch(train) [94][1380/1879] lr: 2.0000e-04 eta: 1:12:36 time: 0.4472 data_time: 0.3065 memory: 6717 grad_norm: 3.4035 loss: 1.1164 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1164 2023/04/14 15:40:39 - mmengine - INFO - Epoch(train) [94][1400/1879] lr: 2.0000e-04 eta: 1:12:29 time: 0.3078 data_time: 0.1672 memory: 6717 grad_norm: 3.4687 loss: 1.0403 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0403 2023/04/14 15:40:47 - mmengine - INFO - Epoch(train) [94][1420/1879] lr: 2.0000e-04 eta: 1:12:22 time: 0.3998 data_time: 0.2605 memory: 6717 grad_norm: 3.5410 loss: 1.2303 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2303 2023/04/14 15:40:53 - mmengine - INFO - Epoch(train) [94][1440/1879] lr: 2.0000e-04 eta: 1:12:14 time: 0.3411 data_time: 0.1935 memory: 6717 grad_norm: 3.5621 loss: 1.1391 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1391 2023/04/14 15:41:01 - mmengine - INFO - Epoch(train) [94][1460/1879] lr: 2.0000e-04 eta: 1:12:07 time: 0.3579 data_time: 0.2179 memory: 6717 grad_norm: 3.4577 loss: 1.1092 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1092 2023/04/14 15:41:07 - mmengine - INFO - Epoch(train) [94][1480/1879] lr: 2.0000e-04 eta: 1:11:59 time: 0.3042 data_time: 0.1604 memory: 6717 grad_norm: 3.4449 loss: 1.0473 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0473 2023/04/14 15:41:15 - mmengine - INFO - Epoch(train) [94][1500/1879] lr: 2.0000e-04 eta: 1:11:52 time: 0.4023 data_time: 0.2408 memory: 6717 grad_norm: 3.4005 loss: 0.9164 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9164 2023/04/14 15:41:21 - mmengine - INFO - Epoch(train) [94][1520/1879] lr: 2.0000e-04 eta: 1:11:44 time: 0.3285 data_time: 0.1869 memory: 6717 grad_norm: 3.3965 loss: 1.1125 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1125 2023/04/14 15:41:30 - mmengine - INFO - Epoch(train) [94][1540/1879] lr: 2.0000e-04 eta: 1:11:37 time: 0.4203 data_time: 0.2341 memory: 6717 grad_norm: 3.5029 loss: 1.1443 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1443 2023/04/14 15:41:36 - mmengine - INFO - Epoch(train) [94][1560/1879] lr: 2.0000e-04 eta: 1:11:30 time: 0.3175 data_time: 0.0968 memory: 6717 grad_norm: 3.4866 loss: 1.1720 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1720 2023/04/14 15:41:44 - mmengine - INFO - Epoch(train) [94][1580/1879] lr: 2.0000e-04 eta: 1:11:22 time: 0.3842 data_time: 0.0550 memory: 6717 grad_norm: 3.4536 loss: 0.9975 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9975 2023/04/14 15:41:50 - mmengine - INFO - Epoch(train) [94][1600/1879] lr: 2.0000e-04 eta: 1:11:15 time: 0.3376 data_time: 0.0624 memory: 6717 grad_norm: 3.5295 loss: 1.0670 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0670 2023/04/14 15:41:58 - mmengine - INFO - Epoch(train) [94][1620/1879] lr: 2.0000e-04 eta: 1:11:07 time: 0.3921 data_time: 0.0162 memory: 6717 grad_norm: 3.5022 loss: 1.0445 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0445 2023/04/14 15:42:05 - mmengine - INFO - Epoch(train) [94][1640/1879] lr: 2.0000e-04 eta: 1:11:00 time: 0.3585 data_time: 0.0206 memory: 6717 grad_norm: 3.5124 loss: 1.1451 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1451 2023/04/14 15:42:13 - mmengine - INFO - Epoch(train) [94][1660/1879] lr: 2.0000e-04 eta: 1:10:53 time: 0.4000 data_time: 0.1107 memory: 6717 grad_norm: 3.4736 loss: 1.0999 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0999 2023/04/14 15:42:20 - mmengine - INFO - Epoch(train) [94][1680/1879] lr: 2.0000e-04 eta: 1:10:45 time: 0.3406 data_time: 0.0644 memory: 6717 grad_norm: 3.5766 loss: 1.1446 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1446 2023/04/14 15:42:29 - mmengine - INFO - Epoch(train) [94][1700/1879] lr: 2.0000e-04 eta: 1:10:38 time: 0.4280 data_time: 0.0854 memory: 6717 grad_norm: 3.5667 loss: 1.0592 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0592 2023/04/14 15:42:35 - mmengine - INFO - Epoch(train) [94][1720/1879] lr: 2.0000e-04 eta: 1:10:30 time: 0.3091 data_time: 0.0608 memory: 6717 grad_norm: 3.4687 loss: 1.1365 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1365 2023/04/14 15:42:43 - mmengine - INFO - Epoch(train) [94][1740/1879] lr: 2.0000e-04 eta: 1:10:23 time: 0.4109 data_time: 0.0977 memory: 6717 grad_norm: 3.4584 loss: 1.2184 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2184 2023/04/14 15:42:50 - mmengine - INFO - Epoch(train) [94][1760/1879] lr: 2.0000e-04 eta: 1:10:16 time: 0.3290 data_time: 0.1113 memory: 6717 grad_norm: 3.4754 loss: 1.1813 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1813 2023/04/14 15:42:58 - mmengine - INFO - Epoch(train) [94][1780/1879] lr: 2.0000e-04 eta: 1:10:08 time: 0.3997 data_time: 0.1051 memory: 6717 grad_norm: 3.5130 loss: 1.1456 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1456 2023/04/14 15:43:04 - mmengine - INFO - Epoch(train) [94][1800/1879] lr: 2.0000e-04 eta: 1:10:01 time: 0.3277 data_time: 0.0396 memory: 6717 grad_norm: 3.2841 loss: 1.0019 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0019 2023/04/14 15:43:13 - mmengine - INFO - Epoch(train) [94][1820/1879] lr: 2.0000e-04 eta: 1:09:53 time: 0.4367 data_time: 0.0153 memory: 6717 grad_norm: 3.5043 loss: 1.1795 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1795 2023/04/14 15:43:20 - mmengine - INFO - Epoch(train) [94][1840/1879] lr: 2.0000e-04 eta: 1:09:46 time: 0.3409 data_time: 0.0113 memory: 6717 grad_norm: 3.3480 loss: 1.2467 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.2467 2023/04/14 15:43:28 - mmengine - INFO - Epoch(train) [94][1860/1879] lr: 2.0000e-04 eta: 1:09:39 time: 0.4165 data_time: 0.0151 memory: 6717 grad_norm: 3.5270 loss: 1.1063 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1063 2023/04/14 15:43:34 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 15:43:34 - mmengine - INFO - Epoch(train) [94][1879/1879] lr: 2.0000e-04 eta: 1:09:32 time: 0.2916 data_time: 0.0129 memory: 6717 grad_norm: 3.6804 loss: 1.2896 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.2896 2023/04/14 15:43:43 - mmengine - INFO - Epoch(val) [94][ 20/155] eta: 0:01:00 time: 0.4492 data_time: 0.4156 memory: 1391 2023/04/14 15:43:49 - mmengine - INFO - Epoch(val) [94][ 40/155] eta: 0:00:44 time: 0.3228 data_time: 0.2895 memory: 1391 2023/04/14 15:43:58 - mmengine - INFO - Epoch(val) [94][ 60/155] eta: 0:00:38 time: 0.4383 data_time: 0.4048 memory: 1391 2023/04/14 15:44:05 - mmengine - INFO - Epoch(val) [94][ 80/155] eta: 0:00:28 time: 0.3183 data_time: 0.2848 memory: 1391 2023/04/14 15:44:14 - mmengine - INFO - Epoch(val) [94][100/155] eta: 0:00:21 time: 0.4536 data_time: 0.4199 memory: 1391 2023/04/14 15:44:20 - mmengine - INFO - Epoch(val) [94][120/155] eta: 0:00:13 time: 0.2957 data_time: 0.2624 memory: 1391 2023/04/14 15:44:29 - mmengine - INFO - Epoch(val) [94][140/155] eta: 0:00:05 time: 0.4812 data_time: 0.4479 memory: 1391 2023/04/14 15:44:36 - mmengine - INFO - Epoch(val) [94][155/155] acc/top1: 0.6683 acc/top5: 0.8741 acc/mean1: 0.6683 data_time: 0.4185 time: 0.4511 2023/04/14 15:44:46 - mmengine - INFO - Epoch(train) [95][ 20/1879] lr: 2.0000e-04 eta: 1:09:24 time: 0.4804 data_time: 0.2637 memory: 6717 grad_norm: 3.4119 loss: 1.1621 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1621 2023/04/14 15:44:52 - mmengine - INFO - Epoch(train) [95][ 40/1879] lr: 2.0000e-04 eta: 1:09:17 time: 0.3185 data_time: 0.0876 memory: 6717 grad_norm: 3.4590 loss: 1.0842 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0842 2023/04/14 15:45:01 - mmengine - INFO - Epoch(train) [95][ 60/1879] lr: 2.0000e-04 eta: 1:09:10 time: 0.4244 data_time: 0.0623 memory: 6717 grad_norm: 3.4251 loss: 1.2179 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2179 2023/04/14 15:45:08 - mmengine - INFO - Epoch(train) [95][ 80/1879] lr: 2.0000e-04 eta: 1:09:02 time: 0.3370 data_time: 0.0136 memory: 6717 grad_norm: 3.3968 loss: 1.0228 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0228 2023/04/14 15:45:17 - mmengine - INFO - Epoch(train) [95][ 100/1879] lr: 2.0000e-04 eta: 1:08:55 time: 0.4406 data_time: 0.0161 memory: 6717 grad_norm: 3.4854 loss: 1.0367 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0367 2023/04/14 15:45:23 - mmengine - INFO - Epoch(train) [95][ 120/1879] lr: 2.0000e-04 eta: 1:08:47 time: 0.3143 data_time: 0.0134 memory: 6717 grad_norm: 3.5180 loss: 1.1106 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1106 2023/04/14 15:45:30 - mmengine - INFO - Epoch(train) [95][ 140/1879] lr: 2.0000e-04 eta: 1:08:40 time: 0.3779 data_time: 0.0136 memory: 6717 grad_norm: 3.3375 loss: 1.0166 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0166 2023/04/14 15:45:38 - mmengine - INFO - Epoch(train) [95][ 160/1879] lr: 2.0000e-04 eta: 1:08:32 time: 0.3549 data_time: 0.0152 memory: 6717 grad_norm: 3.5086 loss: 1.1089 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1089 2023/04/14 15:45:46 - mmengine - INFO - Epoch(train) [95][ 180/1879] lr: 2.0000e-04 eta: 1:08:25 time: 0.4247 data_time: 0.0159 memory: 6717 grad_norm: 3.3300 loss: 1.0498 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0498 2023/04/14 15:45:53 - mmengine - INFO - Epoch(train) [95][ 200/1879] lr: 2.0000e-04 eta: 1:08:18 time: 0.3293 data_time: 0.0155 memory: 6717 grad_norm: 3.4868 loss: 1.2789 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2789 2023/04/14 15:46:01 - mmengine - INFO - Epoch(train) [95][ 220/1879] lr: 2.0000e-04 eta: 1:08:10 time: 0.4075 data_time: 0.0153 memory: 6717 grad_norm: 3.4027 loss: 1.1825 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1825 2023/04/14 15:46:06 - mmengine - INFO - Epoch(train) [95][ 240/1879] lr: 2.0000e-04 eta: 1:08:03 time: 0.2805 data_time: 0.0148 memory: 6717 grad_norm: 3.3858 loss: 1.1490 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1490 2023/04/14 15:46:15 - mmengine - INFO - Epoch(train) [95][ 260/1879] lr: 2.0000e-04 eta: 1:07:55 time: 0.4096 data_time: 0.0145 memory: 6717 grad_norm: 3.4126 loss: 1.0384 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0384 2023/04/14 15:46:21 - mmengine - INFO - Epoch(train) [95][ 280/1879] lr: 2.0000e-04 eta: 1:07:48 time: 0.3179 data_time: 0.0147 memory: 6717 grad_norm: 3.4065 loss: 1.1203 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1203 2023/04/14 15:46:30 - mmengine - INFO - Epoch(train) [95][ 300/1879] lr: 2.0000e-04 eta: 1:07:41 time: 0.4465 data_time: 0.0138 memory: 6717 grad_norm: 3.4557 loss: 1.0752 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0752 2023/04/14 15:46:37 - mmengine - INFO - Epoch(train) [95][ 320/1879] lr: 2.0000e-04 eta: 1:07:33 time: 0.3312 data_time: 0.0141 memory: 6717 grad_norm: 3.4119 loss: 1.1662 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1662 2023/04/14 15:46:45 - mmengine - INFO - Epoch(train) [95][ 340/1879] lr: 2.0000e-04 eta: 1:07:26 time: 0.4005 data_time: 0.0136 memory: 6717 grad_norm: 3.4924 loss: 1.0038 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0038 2023/04/14 15:46:51 - mmengine - INFO - Epoch(train) [95][ 360/1879] lr: 2.0000e-04 eta: 1:07:18 time: 0.3338 data_time: 0.0145 memory: 6717 grad_norm: 3.5692 loss: 1.2428 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2428 2023/04/14 15:46:56 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 15:46:58 - mmengine - INFO - Epoch(train) [95][ 380/1879] lr: 2.0000e-04 eta: 1:07:11 time: 0.3595 data_time: 0.0151 memory: 6717 grad_norm: 3.4449 loss: 1.2888 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2888 2023/04/14 15:47:06 - mmengine - INFO - Epoch(train) [95][ 400/1879] lr: 2.0000e-04 eta: 1:07:04 time: 0.3902 data_time: 0.0129 memory: 6717 grad_norm: 3.4483 loss: 1.1008 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1008 2023/04/14 15:47:14 - mmengine - INFO - Epoch(train) [95][ 420/1879] lr: 2.0000e-04 eta: 1:06:56 time: 0.3718 data_time: 0.0144 memory: 6717 grad_norm: 3.3597 loss: 0.9447 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9447 2023/04/14 15:47:21 - mmengine - INFO - Epoch(train) [95][ 440/1879] lr: 2.0000e-04 eta: 1:06:49 time: 0.3642 data_time: 0.0134 memory: 6717 grad_norm: 3.5297 loss: 1.0745 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0745 2023/04/14 15:47:28 - mmengine - INFO - Epoch(train) [95][ 460/1879] lr: 2.0000e-04 eta: 1:06:41 time: 0.3478 data_time: 0.0154 memory: 6717 grad_norm: 3.4252 loss: 1.1931 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1931 2023/04/14 15:47:36 - mmengine - INFO - Epoch(train) [95][ 480/1879] lr: 2.0000e-04 eta: 1:06:34 time: 0.4025 data_time: 0.0153 memory: 6717 grad_norm: 3.5093 loss: 1.1839 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1839 2023/04/14 15:47:42 - mmengine - INFO - Epoch(train) [95][ 500/1879] lr: 2.0000e-04 eta: 1:06:27 time: 0.3205 data_time: 0.0131 memory: 6717 grad_norm: 3.4627 loss: 1.1903 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1903 2023/04/14 15:47:50 - mmengine - INFO - Epoch(train) [95][ 520/1879] lr: 2.0000e-04 eta: 1:06:19 time: 0.3852 data_time: 0.0691 memory: 6717 grad_norm: 3.4061 loss: 1.1382 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1382 2023/04/14 15:47:57 - mmengine - INFO - Epoch(train) [95][ 540/1879] lr: 2.0000e-04 eta: 1:06:12 time: 0.3302 data_time: 0.0558 memory: 6717 grad_norm: 3.5825 loss: 1.2101 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2101 2023/04/14 15:48:04 - mmengine - INFO - Epoch(train) [95][ 560/1879] lr: 2.0000e-04 eta: 1:06:04 time: 0.3751 data_time: 0.1315 memory: 6717 grad_norm: 3.3893 loss: 1.0155 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0155 2023/04/14 15:48:12 - mmengine - INFO - Epoch(train) [95][ 580/1879] lr: 2.0000e-04 eta: 1:05:57 time: 0.3653 data_time: 0.0760 memory: 6717 grad_norm: 3.5154 loss: 1.1938 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1938 2023/04/14 15:48:19 - mmengine - INFO - Epoch(train) [95][ 600/1879] lr: 2.0000e-04 eta: 1:05:50 time: 0.3947 data_time: 0.1634 memory: 6717 grad_norm: 3.5510 loss: 1.1116 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1116 2023/04/14 15:48:26 - mmengine - INFO - Epoch(train) [95][ 620/1879] lr: 2.0000e-04 eta: 1:05:42 time: 0.3454 data_time: 0.1877 memory: 6717 grad_norm: 3.3861 loss: 1.0394 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0394 2023/04/14 15:48:34 - mmengine - INFO - Epoch(train) [95][ 640/1879] lr: 2.0000e-04 eta: 1:05:35 time: 0.3841 data_time: 0.1468 memory: 6717 grad_norm: 3.4357 loss: 1.1025 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1025 2023/04/14 15:48:42 - mmengine - INFO - Epoch(train) [95][ 660/1879] lr: 2.0000e-04 eta: 1:05:27 time: 0.3960 data_time: 0.0568 memory: 6717 grad_norm: 3.4633 loss: 1.4050 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4050 2023/04/14 15:48:49 - mmengine - INFO - Epoch(train) [95][ 680/1879] lr: 2.0000e-04 eta: 1:05:20 time: 0.3453 data_time: 0.0152 memory: 6717 grad_norm: 3.4363 loss: 1.0400 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0400 2023/04/14 15:48:57 - mmengine - INFO - Epoch(train) [95][ 700/1879] lr: 2.0000e-04 eta: 1:05:13 time: 0.3967 data_time: 0.0127 memory: 6717 grad_norm: 3.5642 loss: 1.1608 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1608 2023/04/14 15:49:03 - mmengine - INFO - Epoch(train) [95][ 720/1879] lr: 2.0000e-04 eta: 1:05:05 time: 0.3144 data_time: 0.0160 memory: 6717 grad_norm: 3.4017 loss: 1.0727 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0727 2023/04/14 15:49:12 - mmengine - INFO - Epoch(train) [95][ 740/1879] lr: 2.0000e-04 eta: 1:04:58 time: 0.4234 data_time: 0.0125 memory: 6717 grad_norm: 3.4749 loss: 1.1231 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1231 2023/04/14 15:49:18 - mmengine - INFO - Epoch(train) [95][ 760/1879] lr: 2.0000e-04 eta: 1:04:50 time: 0.3359 data_time: 0.0149 memory: 6717 grad_norm: 3.4259 loss: 1.0274 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0274 2023/04/14 15:49:27 - mmengine - INFO - Epoch(train) [95][ 780/1879] lr: 2.0000e-04 eta: 1:04:43 time: 0.4150 data_time: 0.0140 memory: 6717 grad_norm: 3.5949 loss: 1.2211 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2211 2023/04/14 15:49:34 - mmengine - INFO - Epoch(train) [95][ 800/1879] lr: 2.0000e-04 eta: 1:04:36 time: 0.3467 data_time: 0.0147 memory: 6717 grad_norm: 3.5404 loss: 0.9808 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9808 2023/04/14 15:49:41 - mmengine - INFO - Epoch(train) [95][ 820/1879] lr: 2.0000e-04 eta: 1:04:28 time: 0.3862 data_time: 0.0134 memory: 6717 grad_norm: 3.4143 loss: 1.2733 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2733 2023/04/14 15:49:48 - mmengine - INFO - Epoch(train) [95][ 840/1879] lr: 2.0000e-04 eta: 1:04:21 time: 0.3510 data_time: 0.0152 memory: 6717 grad_norm: 3.4444 loss: 1.1040 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1040 2023/04/14 15:49:56 - mmengine - INFO - Epoch(train) [95][ 860/1879] lr: 2.0000e-04 eta: 1:04:13 time: 0.3678 data_time: 0.0124 memory: 6717 grad_norm: 3.4370 loss: 1.1597 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1597 2023/04/14 15:50:04 - mmengine - INFO - Epoch(train) [95][ 880/1879] lr: 2.0000e-04 eta: 1:04:06 time: 0.4114 data_time: 0.0166 memory: 6717 grad_norm: 3.4480 loss: 1.0437 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0437 2023/04/14 15:50:11 - mmengine - INFO - Epoch(train) [95][ 900/1879] lr: 2.0000e-04 eta: 1:03:59 time: 0.3728 data_time: 0.0133 memory: 6717 grad_norm: 3.4584 loss: 1.0804 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0804 2023/04/14 15:50:18 - mmengine - INFO - Epoch(train) [95][ 920/1879] lr: 2.0000e-04 eta: 1:03:51 time: 0.3404 data_time: 0.0144 memory: 6717 grad_norm: 3.4893 loss: 1.2592 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2592 2023/04/14 15:50:26 - mmengine - INFO - Epoch(train) [95][ 940/1879] lr: 2.0000e-04 eta: 1:03:44 time: 0.3939 data_time: 0.0137 memory: 6717 grad_norm: 3.5215 loss: 1.3160 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3160 2023/04/14 15:50:33 - mmengine - INFO - Epoch(train) [95][ 960/1879] lr: 2.0000e-04 eta: 1:03:36 time: 0.3457 data_time: 0.0150 memory: 6717 grad_norm: 3.4371 loss: 1.1059 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1059 2023/04/14 15:50:40 - mmengine - INFO - Epoch(train) [95][ 980/1879] lr: 2.0000e-04 eta: 1:03:29 time: 0.3491 data_time: 0.0133 memory: 6717 grad_norm: 3.4245 loss: 1.1602 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1602 2023/04/14 15:50:48 - mmengine - INFO - Epoch(train) [95][1000/1879] lr: 2.0000e-04 eta: 1:03:22 time: 0.3859 data_time: 0.0153 memory: 6717 grad_norm: 3.4730 loss: 1.0675 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0675 2023/04/14 15:50:54 - mmengine - INFO - Epoch(train) [95][1020/1879] lr: 2.0000e-04 eta: 1:03:14 time: 0.3347 data_time: 0.0144 memory: 6717 grad_norm: 3.5429 loss: 1.1523 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1523 2023/04/14 15:51:02 - mmengine - INFO - Epoch(train) [95][1040/1879] lr: 2.0000e-04 eta: 1:03:07 time: 0.3740 data_time: 0.0145 memory: 6717 grad_norm: 3.4311 loss: 1.2081 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2081 2023/04/14 15:51:10 - mmengine - INFO - Epoch(train) [95][1060/1879] lr: 2.0000e-04 eta: 1:02:59 time: 0.3913 data_time: 0.0145 memory: 6717 grad_norm: 3.4221 loss: 1.0781 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0781 2023/04/14 15:51:17 - mmengine - INFO - Epoch(train) [95][1080/1879] lr: 2.0000e-04 eta: 1:02:52 time: 0.3512 data_time: 0.0140 memory: 6717 grad_norm: 3.4543 loss: 1.1095 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1095 2023/04/14 15:51:25 - mmengine - INFO - Epoch(train) [95][1100/1879] lr: 2.0000e-04 eta: 1:02:45 time: 0.4073 data_time: 0.0150 memory: 6717 grad_norm: 3.4661 loss: 1.0154 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.0154 2023/04/14 15:51:32 - mmengine - INFO - Epoch(train) [95][1120/1879] lr: 2.0000e-04 eta: 1:02:37 time: 0.3394 data_time: 0.0156 memory: 6717 grad_norm: 3.4269 loss: 1.0734 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0734 2023/04/14 15:51:40 - mmengine - INFO - Epoch(train) [95][1140/1879] lr: 2.0000e-04 eta: 1:02:30 time: 0.3920 data_time: 0.0131 memory: 6717 grad_norm: 3.5109 loss: 1.0944 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0944 2023/04/14 15:51:46 - mmengine - INFO - Epoch(train) [95][1160/1879] lr: 2.0000e-04 eta: 1:02:22 time: 0.3402 data_time: 0.0157 memory: 6717 grad_norm: 3.4756 loss: 1.1269 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1269 2023/04/14 15:51:55 - mmengine - INFO - Epoch(train) [95][1180/1879] lr: 2.0000e-04 eta: 1:02:15 time: 0.4376 data_time: 0.0131 memory: 6717 grad_norm: 3.3867 loss: 1.1874 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1874 2023/04/14 15:52:01 - mmengine - INFO - Epoch(train) [95][1200/1879] lr: 2.0000e-04 eta: 1:02:08 time: 0.3064 data_time: 0.0147 memory: 6717 grad_norm: 3.4450 loss: 1.0816 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0816 2023/04/14 15:52:09 - mmengine - INFO - Epoch(train) [95][1220/1879] lr: 2.0000e-04 eta: 1:02:00 time: 0.3752 data_time: 0.0150 memory: 6717 grad_norm: 3.5210 loss: 1.2331 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2331 2023/04/14 15:52:16 - mmengine - INFO - Epoch(train) [95][1240/1879] lr: 2.0000e-04 eta: 1:01:53 time: 0.3479 data_time: 0.0159 memory: 6717 grad_norm: 3.4084 loss: 1.1298 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1298 2023/04/14 15:52:23 - mmengine - INFO - Epoch(train) [95][1260/1879] lr: 2.0000e-04 eta: 1:01:45 time: 0.3583 data_time: 0.0130 memory: 6717 grad_norm: 3.4381 loss: 1.0290 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0290 2023/04/14 15:52:30 - mmengine - INFO - Epoch(train) [95][1280/1879] lr: 2.0000e-04 eta: 1:01:38 time: 0.3365 data_time: 0.0185 memory: 6717 grad_norm: 3.3887 loss: 1.0295 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0295 2023/04/14 15:52:37 - mmengine - INFO - Epoch(train) [95][1300/1879] lr: 2.0000e-04 eta: 1:01:30 time: 0.3928 data_time: 0.0124 memory: 6717 grad_norm: 3.4632 loss: 0.9667 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9667 2023/04/14 15:52:44 - mmengine - INFO - Epoch(train) [95][1320/1879] lr: 2.0000e-04 eta: 1:01:23 time: 0.3233 data_time: 0.0164 memory: 6717 grad_norm: 3.4203 loss: 1.0939 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0939 2023/04/14 15:52:52 - mmengine - INFO - Epoch(train) [95][1340/1879] lr: 2.0000e-04 eta: 1:01:16 time: 0.4090 data_time: 0.0130 memory: 6717 grad_norm: 3.5094 loss: 1.3112 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.3112 2023/04/14 15:52:59 - mmengine - INFO - Epoch(train) [95][1360/1879] lr: 2.0000e-04 eta: 1:01:08 time: 0.3412 data_time: 0.0170 memory: 6717 grad_norm: 3.4881 loss: 1.2288 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2288 2023/04/14 15:53:05 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 15:53:06 - mmengine - INFO - Epoch(train) [95][1380/1879] lr: 2.0000e-04 eta: 1:01:01 time: 0.3694 data_time: 0.0230 memory: 6717 grad_norm: 3.3775 loss: 0.9097 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9097 2023/04/14 15:53:13 - mmengine - INFO - Epoch(train) [95][1400/1879] lr: 2.0000e-04 eta: 1:00:53 time: 0.3511 data_time: 0.0161 memory: 6717 grad_norm: 3.4145 loss: 0.9976 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9976 2023/04/14 15:53:21 - mmengine - INFO - Epoch(train) [95][1420/1879] lr: 2.0000e-04 eta: 1:00:46 time: 0.4020 data_time: 0.0128 memory: 6717 grad_norm: 3.4178 loss: 1.1114 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1114 2023/04/14 15:53:28 - mmengine - INFO - Epoch(train) [95][1440/1879] lr: 2.0000e-04 eta: 1:00:39 time: 0.3181 data_time: 0.0164 memory: 6717 grad_norm: 3.3550 loss: 1.1757 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1757 2023/04/14 15:53:35 - mmengine - INFO - Epoch(train) [95][1460/1879] lr: 2.0000e-04 eta: 1:00:31 time: 0.3781 data_time: 0.0123 memory: 6717 grad_norm: 3.4155 loss: 1.0838 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0838 2023/04/14 15:53:43 - mmengine - INFO - Epoch(train) [95][1480/1879] lr: 2.0000e-04 eta: 1:00:24 time: 0.3661 data_time: 0.0160 memory: 6717 grad_norm: 3.4563 loss: 1.2508 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2508 2023/04/14 15:53:50 - mmengine - INFO - Epoch(train) [95][1500/1879] lr: 2.0000e-04 eta: 1:00:16 time: 0.3674 data_time: 0.0145 memory: 6717 grad_norm: 3.4436 loss: 1.0798 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0798 2023/04/14 15:53:57 - mmengine - INFO - Epoch(train) [95][1520/1879] lr: 2.0000e-04 eta: 1:00:09 time: 0.3478 data_time: 0.0180 memory: 6717 grad_norm: 3.4020 loss: 1.0388 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0388 2023/04/14 15:54:05 - mmengine - INFO - Epoch(train) [95][1540/1879] lr: 2.0000e-04 eta: 1:00:02 time: 0.4014 data_time: 0.0126 memory: 6717 grad_norm: 3.4570 loss: 1.0904 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0904 2023/04/14 15:54:12 - mmengine - INFO - Epoch(train) [95][1560/1879] lr: 2.0000e-04 eta: 0:59:54 time: 0.3319 data_time: 0.0157 memory: 6717 grad_norm: 3.4535 loss: 1.2323 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2323 2023/04/14 15:54:20 - mmengine - INFO - Epoch(train) [95][1580/1879] lr: 2.0000e-04 eta: 0:59:47 time: 0.3940 data_time: 0.0126 memory: 6717 grad_norm: 3.4294 loss: 0.9420 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9420 2023/04/14 15:54:27 - mmengine - INFO - Epoch(train) [95][1600/1879] lr: 2.0000e-04 eta: 0:59:39 time: 0.3519 data_time: 0.0159 memory: 6717 grad_norm: 3.4219 loss: 1.0568 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0568 2023/04/14 15:54:35 - mmengine - INFO - Epoch(train) [95][1620/1879] lr: 2.0000e-04 eta: 0:59:32 time: 0.4240 data_time: 0.0131 memory: 6717 grad_norm: 3.4902 loss: 1.0904 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0904 2023/04/14 15:54:42 - mmengine - INFO - Epoch(train) [95][1640/1879] lr: 2.0000e-04 eta: 0:59:25 time: 0.3602 data_time: 0.0151 memory: 6717 grad_norm: 3.4399 loss: 1.1211 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1211 2023/04/14 15:54:50 - mmengine - INFO - Epoch(train) [95][1660/1879] lr: 2.0000e-04 eta: 0:59:17 time: 0.4082 data_time: 0.0117 memory: 6717 grad_norm: 3.4287 loss: 1.1762 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1762 2023/04/14 15:54:57 - mmengine - INFO - Epoch(train) [95][1680/1879] lr: 2.0000e-04 eta: 0:59:10 time: 0.3241 data_time: 0.0159 memory: 6717 grad_norm: 3.4867 loss: 1.1416 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1416 2023/04/14 15:55:05 - mmengine - INFO - Epoch(train) [95][1700/1879] lr: 2.0000e-04 eta: 0:59:02 time: 0.4064 data_time: 0.0131 memory: 6717 grad_norm: 3.4935 loss: 1.1888 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1888 2023/04/14 15:55:11 - mmengine - INFO - Epoch(train) [95][1720/1879] lr: 2.0000e-04 eta: 0:58:55 time: 0.3164 data_time: 0.0155 memory: 6717 grad_norm: 3.4758 loss: 1.2315 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2315 2023/04/14 15:55:20 - mmengine - INFO - Epoch(train) [95][1740/1879] lr: 2.0000e-04 eta: 0:58:48 time: 0.4204 data_time: 0.0120 memory: 6717 grad_norm: 3.4175 loss: 1.0715 top1_acc: 0.4375 top5_acc: 0.8750 loss_cls: 1.0715 2023/04/14 15:55:26 - mmengine - INFO - Epoch(train) [95][1760/1879] lr: 2.0000e-04 eta: 0:58:40 time: 0.3262 data_time: 0.0153 memory: 6717 grad_norm: 3.4098 loss: 1.0453 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0453 2023/04/14 15:55:35 - mmengine - INFO - Epoch(train) [95][1780/1879] lr: 2.0000e-04 eta: 0:58:33 time: 0.4083 data_time: 0.0127 memory: 6717 grad_norm: 3.4516 loss: 1.1472 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1472 2023/04/14 15:55:41 - mmengine - INFO - Epoch(train) [95][1800/1879] lr: 2.0000e-04 eta: 0:58:25 time: 0.3279 data_time: 0.0154 memory: 6717 grad_norm: 3.5013 loss: 1.0016 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0016 2023/04/14 15:55:49 - mmengine - INFO - Epoch(train) [95][1820/1879] lr: 2.0000e-04 eta: 0:58:18 time: 0.3776 data_time: 0.0130 memory: 6717 grad_norm: 3.5252 loss: 1.2088 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2088 2023/04/14 15:55:56 - mmengine - INFO - Epoch(train) [95][1840/1879] lr: 2.0000e-04 eta: 0:58:11 time: 0.3633 data_time: 0.0184 memory: 6717 grad_norm: 3.4811 loss: 1.1488 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1488 2023/04/14 15:56:04 - mmengine - INFO - Epoch(train) [95][1860/1879] lr: 2.0000e-04 eta: 0:58:03 time: 0.4209 data_time: 0.0169 memory: 6717 grad_norm: 3.4194 loss: 1.0860 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0860 2023/04/14 15:56:10 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 15:56:10 - mmengine - INFO - Epoch(train) [95][1879/1879] lr: 2.0000e-04 eta: 0:57:56 time: 0.3440 data_time: 0.0129 memory: 6717 grad_norm: 3.5624 loss: 1.1885 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 1.1885 2023/04/14 15:56:20 - mmengine - INFO - Epoch(val) [95][ 20/155] eta: 0:01:02 time: 0.4610 data_time: 0.4275 memory: 1391 2023/04/14 15:56:26 - mmengine - INFO - Epoch(val) [95][ 40/155] eta: 0:00:44 time: 0.3105 data_time: 0.2769 memory: 1391 2023/04/14 15:56:34 - mmengine - INFO - Epoch(val) [95][ 60/155] eta: 0:00:38 time: 0.4343 data_time: 0.4007 memory: 1391 2023/04/14 15:56:41 - mmengine - INFO - Epoch(val) [95][ 80/155] eta: 0:00:28 time: 0.3192 data_time: 0.2863 memory: 1391 2023/04/14 15:56:49 - mmengine - INFO - Epoch(val) [95][100/155] eta: 0:00:21 time: 0.4211 data_time: 0.3879 memory: 1391 2023/04/14 15:56:56 - mmengine - INFO - Epoch(val) [95][120/155] eta: 0:00:13 time: 0.3370 data_time: 0.3039 memory: 1391 2023/04/14 15:57:06 - mmengine - INFO - Epoch(val) [95][140/155] eta: 0:00:05 time: 0.4811 data_time: 0.4480 memory: 1391 2023/04/14 15:57:13 - mmengine - INFO - Epoch(val) [95][155/155] acc/top1: 0.6684 acc/top5: 0.8751 acc/mean1: 0.6684 data_time: 0.4193 time: 0.4513 2023/04/14 15:57:23 - mmengine - INFO - Epoch(train) [96][ 20/1879] lr: 2.0000e-04 eta: 0:57:49 time: 0.4856 data_time: 0.2784 memory: 6717 grad_norm: 3.4339 loss: 1.1443 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1443 2023/04/14 15:57:29 - mmengine - INFO - Epoch(train) [96][ 40/1879] lr: 2.0000e-04 eta: 0:57:41 time: 0.3335 data_time: 0.1462 memory: 6717 grad_norm: 3.5063 loss: 1.0387 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0387 2023/04/14 15:57:38 - mmengine - INFO - Epoch(train) [96][ 60/1879] lr: 2.0000e-04 eta: 0:57:34 time: 0.4286 data_time: 0.1958 memory: 6717 grad_norm: 3.4566 loss: 1.0255 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0255 2023/04/14 15:57:45 - mmengine - INFO - Epoch(train) [96][ 80/1879] lr: 2.0000e-04 eta: 0:57:27 time: 0.3498 data_time: 0.1448 memory: 6717 grad_norm: 3.4566 loss: 1.0835 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0835 2023/04/14 15:57:53 - mmengine - INFO - Epoch(train) [96][ 100/1879] lr: 2.0000e-04 eta: 0:57:19 time: 0.4184 data_time: 0.1811 memory: 6717 grad_norm: 3.4013 loss: 0.9099 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9099 2023/04/14 15:58:00 - mmengine - INFO - Epoch(train) [96][ 120/1879] lr: 2.0000e-04 eta: 0:57:12 time: 0.3409 data_time: 0.1724 memory: 6717 grad_norm: 3.4183 loss: 1.1172 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1172 2023/04/14 15:58:08 - mmengine - INFO - Epoch(train) [96][ 140/1879] lr: 2.0000e-04 eta: 0:57:05 time: 0.4102 data_time: 0.2686 memory: 6717 grad_norm: 3.4083 loss: 1.0417 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0417 2023/04/14 15:58:14 - mmengine - INFO - Epoch(train) [96][ 160/1879] lr: 2.0000e-04 eta: 0:56:57 time: 0.2986 data_time: 0.1409 memory: 6717 grad_norm: 3.4067 loss: 1.1568 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1568 2023/04/14 15:58:22 - mmengine - INFO - Epoch(train) [96][ 180/1879] lr: 2.0000e-04 eta: 0:56:50 time: 0.4144 data_time: 0.2139 memory: 6717 grad_norm: 3.4255 loss: 1.1616 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1616 2023/04/14 15:58:29 - mmengine - INFO - Epoch(train) [96][ 200/1879] lr: 2.0000e-04 eta: 0:56:42 time: 0.3234 data_time: 0.1763 memory: 6717 grad_norm: 3.4601 loss: 0.9508 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9508 2023/04/14 15:58:38 - mmengine - INFO - Epoch(train) [96][ 220/1879] lr: 2.0000e-04 eta: 0:56:35 time: 0.4389 data_time: 0.2514 memory: 6717 grad_norm: 3.3655 loss: 1.1355 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1355 2023/04/14 15:58:44 - mmengine - INFO - Epoch(train) [96][ 240/1879] lr: 2.0000e-04 eta: 0:56:27 time: 0.3376 data_time: 0.1926 memory: 6717 grad_norm: 3.4728 loss: 0.9521 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 0.9521 2023/04/14 15:58:52 - mmengine - INFO - Epoch(train) [96][ 260/1879] lr: 2.0000e-04 eta: 0:56:20 time: 0.3878 data_time: 0.2445 memory: 6717 grad_norm: 3.3762 loss: 1.1338 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1338 2023/04/14 15:58:58 - mmengine - INFO - Epoch(train) [96][ 280/1879] lr: 2.0000e-04 eta: 0:56:13 time: 0.2887 data_time: 0.1497 memory: 6717 grad_norm: 3.3414 loss: 1.0724 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0724 2023/04/14 15:59:07 - mmengine - INFO - Epoch(train) [96][ 300/1879] lr: 2.0000e-04 eta: 0:56:05 time: 0.4345 data_time: 0.2850 memory: 6717 grad_norm: 3.4348 loss: 1.1296 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1296 2023/04/14 15:59:13 - mmengine - INFO - Epoch(train) [96][ 320/1879] lr: 2.0000e-04 eta: 0:55:58 time: 0.3137 data_time: 0.1366 memory: 6717 grad_norm: 3.4500 loss: 1.0641 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0641 2023/04/14 15:59:21 - mmengine - INFO - Epoch(train) [96][ 340/1879] lr: 2.0000e-04 eta: 0:55:50 time: 0.4115 data_time: 0.2357 memory: 6717 grad_norm: 3.4776 loss: 1.1235 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1235 2023/04/14 15:59:28 - mmengine - INFO - Epoch(train) [96][ 360/1879] lr: 2.0000e-04 eta: 0:55:43 time: 0.3618 data_time: 0.1449 memory: 6717 grad_norm: 3.5310 loss: 1.2820 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2820 2023/04/14 15:59:38 - mmengine - INFO - Epoch(train) [96][ 380/1879] lr: 2.0000e-04 eta: 0:55:36 time: 0.4541 data_time: 0.0840 memory: 6717 grad_norm: 3.4412 loss: 1.2089 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2089 2023/04/14 15:59:44 - mmengine - INFO - Epoch(train) [96][ 400/1879] lr: 2.0000e-04 eta: 0:55:28 time: 0.3048 data_time: 0.0115 memory: 6717 grad_norm: 3.4064 loss: 1.2166 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2166 2023/04/14 15:59:52 - mmengine - INFO - Epoch(train) [96][ 420/1879] lr: 2.0000e-04 eta: 0:55:21 time: 0.4206 data_time: 0.0138 memory: 6717 grad_norm: 3.4583 loss: 1.1540 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1540 2023/04/14 15:59:59 - mmengine - INFO - Epoch(train) [96][ 440/1879] lr: 2.0000e-04 eta: 0:55:13 time: 0.3551 data_time: 0.0164 memory: 6717 grad_norm: 3.4844 loss: 1.2692 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2692 2023/04/14 16:00:07 - mmengine - INFO - Epoch(train) [96][ 460/1879] lr: 2.0000e-04 eta: 0:55:06 time: 0.4133 data_time: 0.0144 memory: 6717 grad_norm: 3.5088 loss: 1.1858 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1858 2023/04/14 16:00:14 - mmengine - INFO - Epoch(train) [96][ 480/1879] lr: 2.0000e-04 eta: 0:54:59 time: 0.3107 data_time: 0.0145 memory: 6717 grad_norm: 3.4666 loss: 1.1976 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1976 2023/04/14 16:00:19 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 16:00:22 - mmengine - INFO - Epoch(train) [96][ 500/1879] lr: 2.0000e-04 eta: 0:54:51 time: 0.4002 data_time: 0.0141 memory: 6717 grad_norm: 3.4077 loss: 0.9325 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9325 2023/04/14 16:00:28 - mmengine - INFO - Epoch(train) [96][ 520/1879] lr: 2.0000e-04 eta: 0:54:44 time: 0.3114 data_time: 0.0153 memory: 6717 grad_norm: 3.4399 loss: 1.0028 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0028 2023/04/14 16:00:36 - mmengine - INFO - Epoch(train) [96][ 540/1879] lr: 2.0000e-04 eta: 0:54:36 time: 0.4018 data_time: 0.0126 memory: 6717 grad_norm: 3.5096 loss: 1.0927 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0927 2023/04/14 16:00:43 - mmengine - INFO - Epoch(train) [96][ 560/1879] lr: 2.0000e-04 eta: 0:54:29 time: 0.3283 data_time: 0.0168 memory: 6717 grad_norm: 3.5195 loss: 1.2983 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2983 2023/04/14 16:00:51 - mmengine - INFO - Epoch(train) [96][ 580/1879] lr: 2.0000e-04 eta: 0:54:22 time: 0.4211 data_time: 0.0123 memory: 6717 grad_norm: 3.4913 loss: 0.9993 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9993 2023/04/14 16:00:57 - mmengine - INFO - Epoch(train) [96][ 600/1879] lr: 2.0000e-04 eta: 0:54:14 time: 0.3068 data_time: 0.0152 memory: 6717 grad_norm: 3.4678 loss: 1.3192 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3192 2023/04/14 16:01:05 - mmengine - INFO - Epoch(train) [96][ 620/1879] lr: 2.0000e-04 eta: 0:54:07 time: 0.3810 data_time: 0.0288 memory: 6717 grad_norm: 3.5435 loss: 1.1862 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1862 2023/04/14 16:01:12 - mmengine - INFO - Epoch(train) [96][ 640/1879] lr: 2.0000e-04 eta: 0:53:59 time: 0.3406 data_time: 0.0320 memory: 6717 grad_norm: 3.4347 loss: 1.0153 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0153 2023/04/14 16:01:19 - mmengine - INFO - Epoch(train) [96][ 660/1879] lr: 2.0000e-04 eta: 0:53:52 time: 0.3575 data_time: 0.0373 memory: 6717 grad_norm: 3.4575 loss: 1.0107 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0107 2023/04/14 16:01:27 - mmengine - INFO - Epoch(train) [96][ 680/1879] lr: 2.0000e-04 eta: 0:53:45 time: 0.3984 data_time: 0.0331 memory: 6717 grad_norm: 3.3548 loss: 1.1921 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1921 2023/04/14 16:01:34 - mmengine - INFO - Epoch(train) [96][ 700/1879] lr: 2.0000e-04 eta: 0:53:37 time: 0.3631 data_time: 0.0129 memory: 6717 grad_norm: 3.4314 loss: 1.1581 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.1581 2023/04/14 16:01:42 - mmengine - INFO - Epoch(train) [96][ 720/1879] lr: 2.0000e-04 eta: 0:53:30 time: 0.4044 data_time: 0.0155 memory: 6717 grad_norm: 3.4239 loss: 1.0081 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0081 2023/04/14 16:01:50 - mmengine - INFO - Epoch(train) [96][ 740/1879] lr: 2.0000e-04 eta: 0:53:22 time: 0.3829 data_time: 0.0127 memory: 6717 grad_norm: 3.4657 loss: 1.1438 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1438 2023/04/14 16:01:56 - mmengine - INFO - Epoch(train) [96][ 760/1879] lr: 2.0000e-04 eta: 0:53:15 time: 0.3244 data_time: 0.0155 memory: 6717 grad_norm: 3.4075 loss: 1.0835 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0835 2023/04/14 16:02:04 - mmengine - INFO - Epoch(train) [96][ 780/1879] lr: 2.0000e-04 eta: 0:53:08 time: 0.3778 data_time: 0.0124 memory: 6717 grad_norm: 3.4793 loss: 1.1233 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1233 2023/04/14 16:02:10 - mmengine - INFO - Epoch(train) [96][ 800/1879] lr: 2.0000e-04 eta: 0:53:00 time: 0.3323 data_time: 0.0160 memory: 6717 grad_norm: 3.3583 loss: 0.9475 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9475 2023/04/14 16:02:19 - mmengine - INFO - Epoch(train) [96][ 820/1879] lr: 2.0000e-04 eta: 0:52:53 time: 0.4093 data_time: 0.0131 memory: 6717 grad_norm: 3.4050 loss: 0.9220 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9220 2023/04/14 16:02:26 - mmengine - INFO - Epoch(train) [96][ 840/1879] lr: 2.0000e-04 eta: 0:52:45 time: 0.3475 data_time: 0.0163 memory: 6717 grad_norm: 3.3664 loss: 0.9385 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9385 2023/04/14 16:02:32 - mmengine - INFO - Epoch(train) [96][ 860/1879] lr: 2.0000e-04 eta: 0:52:38 time: 0.3425 data_time: 0.0124 memory: 6717 grad_norm: 3.5141 loss: 1.1631 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1631 2023/04/14 16:02:41 - mmengine - INFO - Epoch(train) [96][ 880/1879] lr: 2.0000e-04 eta: 0:52:31 time: 0.4062 data_time: 0.0163 memory: 6717 grad_norm: 3.4470 loss: 1.1261 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1261 2023/04/14 16:02:47 - mmengine - INFO - Epoch(train) [96][ 900/1879] lr: 2.0000e-04 eta: 0:52:23 time: 0.3225 data_time: 0.0130 memory: 6717 grad_norm: 3.4795 loss: 1.0435 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0435 2023/04/14 16:02:54 - mmengine - INFO - Epoch(train) [96][ 920/1879] lr: 2.0000e-04 eta: 0:52:16 time: 0.3733 data_time: 0.0142 memory: 6717 grad_norm: 3.4855 loss: 1.2189 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2189 2023/04/14 16:03:02 - mmengine - INFO - Epoch(train) [96][ 940/1879] lr: 2.0000e-04 eta: 0:52:08 time: 0.3567 data_time: 0.0153 memory: 6717 grad_norm: 3.5775 loss: 1.1283 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1283 2023/04/14 16:03:09 - mmengine - INFO - Epoch(train) [96][ 960/1879] lr: 2.0000e-04 eta: 0:52:01 time: 0.3912 data_time: 0.0657 memory: 6717 grad_norm: 3.3861 loss: 1.1022 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1022 2023/04/14 16:03:16 - mmengine - INFO - Epoch(train) [96][ 980/1879] lr: 2.0000e-04 eta: 0:51:54 time: 0.3470 data_time: 0.0502 memory: 6717 grad_norm: 3.5092 loss: 1.0778 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0778 2023/04/14 16:03:24 - mmengine - INFO - Epoch(train) [96][1000/1879] lr: 2.0000e-04 eta: 0:51:46 time: 0.3904 data_time: 0.0247 memory: 6717 grad_norm: 3.4793 loss: 1.0793 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0793 2023/04/14 16:03:31 - mmengine - INFO - Epoch(train) [96][1020/1879] lr: 2.0000e-04 eta: 0:51:39 time: 0.3207 data_time: 0.0631 memory: 6717 grad_norm: 3.5308 loss: 1.0131 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0131 2023/04/14 16:03:39 - mmengine - INFO - Epoch(train) [96][1040/1879] lr: 2.0000e-04 eta: 0:51:31 time: 0.4166 data_time: 0.0214 memory: 6717 grad_norm: 3.4016 loss: 1.1026 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1026 2023/04/14 16:03:46 - mmengine - INFO - Epoch(train) [96][1060/1879] lr: 2.0000e-04 eta: 0:51:24 time: 0.3452 data_time: 0.0129 memory: 6717 grad_norm: 3.4849 loss: 1.1307 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1307 2023/04/14 16:03:54 - mmengine - INFO - Epoch(train) [96][1080/1879] lr: 2.0000e-04 eta: 0:51:17 time: 0.4172 data_time: 0.0151 memory: 6717 grad_norm: 3.3962 loss: 1.0708 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0708 2023/04/14 16:04:01 - mmengine - INFO - Epoch(train) [96][1100/1879] lr: 2.0000e-04 eta: 0:51:09 time: 0.3243 data_time: 0.0146 memory: 6717 grad_norm: 3.4469 loss: 0.9267 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9267 2023/04/14 16:04:09 - mmengine - INFO - Epoch(train) [96][1120/1879] lr: 2.0000e-04 eta: 0:51:02 time: 0.4021 data_time: 0.0158 memory: 6717 grad_norm: 3.5003 loss: 1.0977 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0977 2023/04/14 16:04:16 - mmengine - INFO - Epoch(train) [96][1140/1879] lr: 2.0000e-04 eta: 0:50:54 time: 0.3523 data_time: 0.0120 memory: 6717 grad_norm: 3.5569 loss: 1.1092 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1092 2023/04/14 16:04:24 - mmengine - INFO - Epoch(train) [96][1160/1879] lr: 2.0000e-04 eta: 0:50:47 time: 0.4057 data_time: 0.0185 memory: 6717 grad_norm: 3.3785 loss: 0.9543 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9543 2023/04/14 16:04:30 - mmengine - INFO - Epoch(train) [96][1180/1879] lr: 2.0000e-04 eta: 0:50:39 time: 0.3177 data_time: 0.0136 memory: 6717 grad_norm: 3.4934 loss: 1.1248 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1248 2023/04/14 16:04:38 - mmengine - INFO - Epoch(train) [96][1200/1879] lr: 2.0000e-04 eta: 0:50:32 time: 0.3998 data_time: 0.0149 memory: 6717 grad_norm: 3.3773 loss: 1.0180 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0180 2023/04/14 16:04:45 - mmengine - INFO - Epoch(train) [96][1220/1879] lr: 2.0000e-04 eta: 0:50:25 time: 0.3561 data_time: 0.0134 memory: 6717 grad_norm: 3.3986 loss: 1.0661 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0661 2023/04/14 16:04:53 - mmengine - INFO - Epoch(train) [96][1240/1879] lr: 2.0000e-04 eta: 0:50:17 time: 0.3759 data_time: 0.0165 memory: 6717 grad_norm: 3.4768 loss: 1.0025 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0025 2023/04/14 16:05:00 - mmengine - INFO - Epoch(train) [96][1260/1879] lr: 2.0000e-04 eta: 0:50:10 time: 0.3753 data_time: 0.0142 memory: 6717 grad_norm: 3.4801 loss: 1.1710 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1710 2023/04/14 16:05:07 - mmengine - INFO - Epoch(train) [96][1280/1879] lr: 2.0000e-04 eta: 0:50:02 time: 0.3471 data_time: 0.0143 memory: 6717 grad_norm: 3.4382 loss: 1.0122 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0122 2023/04/14 16:05:15 - mmengine - INFO - Epoch(train) [96][1300/1879] lr: 2.0000e-04 eta: 0:49:55 time: 0.3784 data_time: 0.0148 memory: 6717 grad_norm: 3.4314 loss: 1.1985 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1985 2023/04/14 16:05:23 - mmengine - INFO - Epoch(train) [96][1320/1879] lr: 2.0000e-04 eta: 0:49:48 time: 0.3809 data_time: 0.0145 memory: 6717 grad_norm: 3.5181 loss: 1.1271 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1271 2023/04/14 16:05:29 - mmengine - INFO - Epoch(train) [96][1340/1879] lr: 2.0000e-04 eta: 0:49:40 time: 0.3361 data_time: 0.0134 memory: 6717 grad_norm: 3.5195 loss: 1.2443 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2443 2023/04/14 16:05:37 - mmengine - INFO - Epoch(train) [96][1360/1879] lr: 2.0000e-04 eta: 0:49:33 time: 0.3757 data_time: 0.0144 memory: 6717 grad_norm: 3.4493 loss: 1.0381 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0381 2023/04/14 16:05:45 - mmengine - INFO - Epoch(train) [96][1380/1879] lr: 2.0000e-04 eta: 0:49:26 time: 0.4204 data_time: 0.0134 memory: 6717 grad_norm: 3.5061 loss: 1.0540 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0540 2023/04/14 16:05:52 - mmengine - INFO - Epoch(train) [96][1400/1879] lr: 2.0000e-04 eta: 0:49:18 time: 0.3424 data_time: 0.0136 memory: 6717 grad_norm: 3.4552 loss: 1.2115 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2115 2023/04/14 16:06:01 - mmengine - INFO - Epoch(train) [96][1420/1879] lr: 2.0000e-04 eta: 0:49:11 time: 0.4227 data_time: 0.0137 memory: 6717 grad_norm: 3.4085 loss: 1.1998 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1998 2023/04/14 16:06:07 - mmengine - INFO - Epoch(train) [96][1440/1879] lr: 2.0000e-04 eta: 0:49:03 time: 0.3221 data_time: 0.0151 memory: 6717 grad_norm: 3.4267 loss: 1.0792 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0792 2023/04/14 16:06:15 - mmengine - INFO - Epoch(train) [96][1460/1879] lr: 2.0000e-04 eta: 0:48:56 time: 0.3969 data_time: 0.0146 memory: 6717 grad_norm: 3.3886 loss: 1.0668 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0668 2023/04/14 16:06:22 - mmengine - INFO - Epoch(train) [96][1480/1879] lr: 2.0000e-04 eta: 0:48:48 time: 0.3302 data_time: 0.0141 memory: 6717 grad_norm: 3.4798 loss: 0.9954 top1_acc: 0.5625 top5_acc: 1.0000 loss_cls: 0.9954 2023/04/14 16:06:27 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 16:06:30 - mmengine - INFO - Epoch(train) [96][1500/1879] lr: 2.0000e-04 eta: 0:48:41 time: 0.3951 data_time: 0.0146 memory: 6717 grad_norm: 3.4785 loss: 1.2348 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2348 2023/04/14 16:06:36 - mmengine - INFO - Epoch(train) [96][1520/1879] lr: 2.0000e-04 eta: 0:48:34 time: 0.3340 data_time: 0.0155 memory: 6717 grad_norm: 3.5006 loss: 1.2173 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2173 2023/04/14 16:06:44 - mmengine - INFO - Epoch(train) [96][1540/1879] lr: 2.0000e-04 eta: 0:48:26 time: 0.3967 data_time: 0.0130 memory: 6717 grad_norm: 3.4918 loss: 1.1337 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1337 2023/04/14 16:06:51 - mmengine - INFO - Epoch(train) [96][1560/1879] lr: 2.0000e-04 eta: 0:48:19 time: 0.3501 data_time: 0.0149 memory: 6717 grad_norm: 3.5218 loss: 1.0539 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0539 2023/04/14 16:06:58 - mmengine - INFO - Epoch(train) [96][1580/1879] lr: 2.0000e-04 eta: 0:48:11 time: 0.3343 data_time: 0.0145 memory: 6717 grad_norm: 3.4829 loss: 1.1057 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1057 2023/04/14 16:07:06 - mmengine - INFO - Epoch(train) [96][1600/1879] lr: 2.0000e-04 eta: 0:48:04 time: 0.3961 data_time: 0.0168 memory: 6717 grad_norm: 3.4487 loss: 1.3010 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3010 2023/04/14 16:07:12 - mmengine - INFO - Epoch(train) [96][1620/1879] lr: 2.0000e-04 eta: 0:47:57 time: 0.3176 data_time: 0.0186 memory: 6717 grad_norm: 3.3759 loss: 0.9956 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9956 2023/04/14 16:07:21 - mmengine - INFO - Epoch(train) [96][1640/1879] lr: 2.0000e-04 eta: 0:47:49 time: 0.4214 data_time: 0.0192 memory: 6717 grad_norm: 3.4978 loss: 1.1525 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1525 2023/04/14 16:07:27 - mmengine - INFO - Epoch(train) [96][1660/1879] lr: 2.0000e-04 eta: 0:47:42 time: 0.3409 data_time: 0.0126 memory: 6717 grad_norm: 3.3814 loss: 0.9900 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9900 2023/04/14 16:07:35 - mmengine - INFO - Epoch(train) [96][1680/1879] lr: 2.0000e-04 eta: 0:47:34 time: 0.4005 data_time: 0.0146 memory: 6717 grad_norm: 3.5284 loss: 1.0032 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0032 2023/04/14 16:07:42 - mmengine - INFO - Epoch(train) [96][1700/1879] lr: 2.0000e-04 eta: 0:47:27 time: 0.3153 data_time: 0.0125 memory: 6717 grad_norm: 3.4815 loss: 1.0286 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0286 2023/04/14 16:07:50 - mmengine - INFO - Epoch(train) [96][1720/1879] lr: 2.0000e-04 eta: 0:47:20 time: 0.4032 data_time: 0.0143 memory: 6717 grad_norm: 3.4822 loss: 1.0277 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0277 2023/04/14 16:07:58 - mmengine - INFO - Epoch(train) [96][1740/1879] lr: 2.0000e-04 eta: 0:47:12 time: 0.3935 data_time: 0.0133 memory: 6717 grad_norm: 3.5160 loss: 1.1930 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1930 2023/04/14 16:08:04 - mmengine - INFO - Epoch(train) [96][1760/1879] lr: 2.0000e-04 eta: 0:47:05 time: 0.3198 data_time: 0.0140 memory: 6717 grad_norm: 3.3936 loss: 0.9903 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9903 2023/04/14 16:08:12 - mmengine - INFO - Epoch(train) [96][1780/1879] lr: 2.0000e-04 eta: 0:46:57 time: 0.3893 data_time: 0.0145 memory: 6717 grad_norm: 3.4453 loss: 1.2715 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2715 2023/04/14 16:08:19 - mmengine - INFO - Epoch(train) [96][1800/1879] lr: 2.0000e-04 eta: 0:46:50 time: 0.3599 data_time: 0.0152 memory: 6717 grad_norm: 3.4624 loss: 1.0143 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0143 2023/04/14 16:08:26 - mmengine - INFO - Epoch(train) [96][1820/1879] lr: 2.0000e-04 eta: 0:46:43 time: 0.3607 data_time: 0.0132 memory: 6717 grad_norm: 3.5516 loss: 1.1290 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1290 2023/04/14 16:08:33 - mmengine - INFO - Epoch(train) [96][1840/1879] lr: 2.0000e-04 eta: 0:46:35 time: 0.3499 data_time: 0.0146 memory: 6717 grad_norm: 3.5104 loss: 1.1195 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1195 2023/04/14 16:08:41 - mmengine - INFO - Epoch(train) [96][1860/1879] lr: 2.0000e-04 eta: 0:46:28 time: 0.3865 data_time: 0.0155 memory: 6717 grad_norm: 3.3614 loss: 1.2226 top1_acc: 0.4375 top5_acc: 0.8125 loss_cls: 1.2226 2023/04/14 16:08:47 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 16:08:47 - mmengine - INFO - Epoch(train) [96][1879/1879] lr: 2.0000e-04 eta: 0:46:21 time: 0.2984 data_time: 0.0372 memory: 6717 grad_norm: 3.5891 loss: 1.2385 top1_acc: 0.2857 top5_acc: 0.7143 loss_cls: 1.2385 2023/04/14 16:08:47 - mmengine - INFO - Saving checkpoint at 96 epochs 2023/04/14 16:08:57 - mmengine - INFO - Epoch(val) [96][ 20/155] eta: 0:01:01 time: 0.4552 data_time: 0.4214 memory: 1391 2023/04/14 16:09:03 - mmengine - INFO - Epoch(val) [96][ 40/155] eta: 0:00:44 time: 0.3237 data_time: 0.2900 memory: 1391 2023/04/14 16:09:12 - mmengine - INFO - Epoch(val) [96][ 60/155] eta: 0:00:38 time: 0.4347 data_time: 0.4009 memory: 1391 2023/04/14 16:09:18 - mmengine - INFO - Epoch(val) [96][ 80/155] eta: 0:00:28 time: 0.3141 data_time: 0.2804 memory: 1391 2023/04/14 16:09:27 - mmengine - INFO - Epoch(val) [96][100/155] eta: 0:00:21 time: 0.4542 data_time: 0.4200 memory: 1391 2023/04/14 16:09:33 - mmengine - INFO - Epoch(val) [96][120/155] eta: 0:00:13 time: 0.3039 data_time: 0.2706 memory: 1391 2023/04/14 16:09:43 - mmengine - INFO - Epoch(val) [96][140/155] eta: 0:00:05 time: 0.4834 data_time: 0.4495 memory: 1391 2023/04/14 16:09:50 - mmengine - INFO - Epoch(val) [96][155/155] acc/top1: 0.6681 acc/top5: 0.8747 acc/mean1: 0.6680 data_time: 0.4143 time: 0.4464 2023/04/14 16:10:01 - mmengine - INFO - Epoch(train) [97][ 20/1879] lr: 2.0000e-04 eta: 0:46:13 time: 0.5326 data_time: 0.3129 memory: 6717 grad_norm: 3.4479 loss: 1.3174 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3174 2023/04/14 16:10:07 - mmengine - INFO - Epoch(train) [97][ 40/1879] lr: 2.0000e-04 eta: 0:46:06 time: 0.3305 data_time: 0.0963 memory: 6717 grad_norm: 3.5028 loss: 1.1334 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1334 2023/04/14 16:10:16 - mmengine - INFO - Epoch(train) [97][ 60/1879] lr: 2.0000e-04 eta: 0:45:59 time: 0.4202 data_time: 0.1102 memory: 6717 grad_norm: 3.4282 loss: 1.0940 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0940 2023/04/14 16:10:22 - mmengine - INFO - Epoch(train) [97][ 80/1879] lr: 2.0000e-04 eta: 0:45:51 time: 0.3289 data_time: 0.0241 memory: 6717 grad_norm: 3.4923 loss: 1.0452 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0452 2023/04/14 16:10:31 - mmengine - INFO - Epoch(train) [97][ 100/1879] lr: 2.0000e-04 eta: 0:45:44 time: 0.4444 data_time: 0.0145 memory: 6717 grad_norm: 3.5169 loss: 1.3779 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.3779 2023/04/14 16:10:39 - mmengine - INFO - Epoch(train) [97][ 120/1879] lr: 2.0000e-04 eta: 0:45:36 time: 0.3657 data_time: 0.0129 memory: 6717 grad_norm: 3.3456 loss: 0.9681 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9681 2023/04/14 16:10:47 - mmengine - INFO - Epoch(train) [97][ 140/1879] lr: 2.0000e-04 eta: 0:45:29 time: 0.4344 data_time: 0.0153 memory: 6717 grad_norm: 3.4064 loss: 1.1109 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1109 2023/04/14 16:10:53 - mmengine - INFO - Epoch(train) [97][ 160/1879] lr: 2.0000e-04 eta: 0:45:22 time: 0.2899 data_time: 0.0126 memory: 6717 grad_norm: 3.4699 loss: 1.0844 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0844 2023/04/14 16:11:01 - mmengine - INFO - Epoch(train) [97][ 180/1879] lr: 2.0000e-04 eta: 0:45:14 time: 0.4180 data_time: 0.0159 memory: 6717 grad_norm: 3.4900 loss: 1.0046 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0046 2023/04/14 16:11:08 - mmengine - INFO - Epoch(train) [97][ 200/1879] lr: 2.0000e-04 eta: 0:45:07 time: 0.3059 data_time: 0.0131 memory: 6717 grad_norm: 3.4854 loss: 1.1137 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1137 2023/04/14 16:11:16 - mmengine - INFO - Epoch(train) [97][ 220/1879] lr: 2.0000e-04 eta: 0:45:00 time: 0.4401 data_time: 0.0144 memory: 6717 grad_norm: 3.4247 loss: 0.9194 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 0.9194 2023/04/14 16:11:22 - mmengine - INFO - Epoch(train) [97][ 240/1879] lr: 2.0000e-04 eta: 0:44:52 time: 0.3039 data_time: 0.0136 memory: 6717 grad_norm: 3.4994 loss: 1.1181 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1181 2023/04/14 16:11:30 - mmengine - INFO - Epoch(train) [97][ 260/1879] lr: 2.0000e-04 eta: 0:44:45 time: 0.3921 data_time: 0.0154 memory: 6717 grad_norm: 3.4720 loss: 1.1655 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1655 2023/04/14 16:11:37 - mmengine - INFO - Epoch(train) [97][ 280/1879] lr: 2.0000e-04 eta: 0:44:37 time: 0.3326 data_time: 0.0126 memory: 6717 grad_norm: 3.5117 loss: 1.1041 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1041 2023/04/14 16:11:45 - mmengine - INFO - Epoch(train) [97][ 300/1879] lr: 2.0000e-04 eta: 0:44:30 time: 0.3949 data_time: 0.0152 memory: 6717 grad_norm: 3.5093 loss: 1.1943 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1943 2023/04/14 16:11:52 - mmengine - INFO - Epoch(train) [97][ 320/1879] lr: 2.0000e-04 eta: 0:44:22 time: 0.3627 data_time: 0.0123 memory: 6717 grad_norm: 3.4707 loss: 1.1915 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1915 2023/04/14 16:12:01 - mmengine - INFO - Epoch(train) [97][ 340/1879] lr: 2.0000e-04 eta: 0:44:15 time: 0.4243 data_time: 0.0146 memory: 6717 grad_norm: 3.4157 loss: 1.0254 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0254 2023/04/14 16:12:07 - mmengine - INFO - Epoch(train) [97][ 360/1879] lr: 2.0000e-04 eta: 0:44:08 time: 0.3239 data_time: 0.0126 memory: 6717 grad_norm: 3.4820 loss: 0.9828 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 0.9828 2023/04/14 16:12:15 - mmengine - INFO - Epoch(train) [97][ 380/1879] lr: 2.0000e-04 eta: 0:44:00 time: 0.3940 data_time: 0.0146 memory: 6717 grad_norm: 3.3359 loss: 0.9798 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9798 2023/04/14 16:12:22 - mmengine - INFO - Epoch(train) [97][ 400/1879] lr: 2.0000e-04 eta: 0:43:53 time: 0.3578 data_time: 0.0131 memory: 6717 grad_norm: 3.3698 loss: 1.1112 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1112 2023/04/14 16:12:30 - mmengine - INFO - Epoch(train) [97][ 420/1879] lr: 2.0000e-04 eta: 0:43:45 time: 0.3828 data_time: 0.0155 memory: 6717 grad_norm: 3.4433 loss: 1.1092 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1092 2023/04/14 16:12:37 - mmengine - INFO - Epoch(train) [97][ 440/1879] lr: 2.0000e-04 eta: 0:43:38 time: 0.3546 data_time: 0.0127 memory: 6717 grad_norm: 3.5349 loss: 1.1372 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1372 2023/04/14 16:12:45 - mmengine - INFO - Epoch(train) [97][ 460/1879] lr: 2.0000e-04 eta: 0:43:31 time: 0.4120 data_time: 0.0150 memory: 6717 grad_norm: 3.4666 loss: 1.1265 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1265 2023/04/14 16:12:51 - mmengine - INFO - Epoch(train) [97][ 480/1879] lr: 2.0000e-04 eta: 0:43:23 time: 0.3133 data_time: 0.0127 memory: 6717 grad_norm: 3.3957 loss: 0.9247 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 0.9247 2023/04/14 16:13:00 - mmengine - INFO - Epoch(train) [97][ 500/1879] lr: 2.0000e-04 eta: 0:43:16 time: 0.4214 data_time: 0.0153 memory: 6717 grad_norm: 3.4478 loss: 1.1627 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1627 2023/04/14 16:13:06 - mmengine - INFO - Epoch(train) [97][ 520/1879] lr: 2.0000e-04 eta: 0:43:08 time: 0.2848 data_time: 0.0132 memory: 6717 grad_norm: 3.4650 loss: 1.1870 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1870 2023/04/14 16:13:14 - mmengine - INFO - Epoch(train) [97][ 540/1879] lr: 2.0000e-04 eta: 0:43:01 time: 0.4115 data_time: 0.0165 memory: 6717 grad_norm: 3.4719 loss: 1.0728 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0728 2023/04/14 16:13:20 - mmengine - INFO - Epoch(train) [97][ 560/1879] lr: 2.0000e-04 eta: 0:42:54 time: 0.3089 data_time: 0.0128 memory: 6717 grad_norm: 3.4710 loss: 1.0689 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0689 2023/04/14 16:13:29 - mmengine - INFO - Epoch(train) [97][ 580/1879] lr: 2.0000e-04 eta: 0:42:46 time: 0.4325 data_time: 0.0583 memory: 6717 grad_norm: 3.4462 loss: 1.0458 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0458 2023/04/14 16:13:35 - mmengine - INFO - Epoch(train) [97][ 600/1879] lr: 2.0000e-04 eta: 0:42:39 time: 0.3036 data_time: 0.0415 memory: 6717 grad_norm: 3.4330 loss: 1.2206 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2206 2023/04/14 16:13:41 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 16:13:43 - mmengine - INFO - Epoch(train) [97][ 620/1879] lr: 2.0000e-04 eta: 0:42:31 time: 0.4138 data_time: 0.0904 memory: 6717 grad_norm: 3.5409 loss: 1.1368 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1368 2023/04/14 16:13:49 - mmengine - INFO - Epoch(train) [97][ 640/1879] lr: 2.0000e-04 eta: 0:42:24 time: 0.3059 data_time: 0.1107 memory: 6717 grad_norm: 3.4363 loss: 1.1620 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1620 2023/04/14 16:13:57 - mmengine - INFO - Epoch(train) [97][ 660/1879] lr: 2.0000e-04 eta: 0:42:17 time: 0.3950 data_time: 0.1947 memory: 6717 grad_norm: 3.4070 loss: 1.1667 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1667 2023/04/14 16:14:05 - mmengine - INFO - Epoch(train) [97][ 680/1879] lr: 2.0000e-04 eta: 0:42:09 time: 0.4185 data_time: 0.0137 memory: 6717 grad_norm: 3.5635 loss: 1.2681 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2681 2023/04/14 16:14:12 - mmengine - INFO - Epoch(train) [97][ 700/1879] lr: 2.0000e-04 eta: 0:42:02 time: 0.3390 data_time: 0.0160 memory: 6717 grad_norm: 3.5052 loss: 1.0220 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0220 2023/04/14 16:14:20 - mmengine - INFO - Epoch(train) [97][ 720/1879] lr: 2.0000e-04 eta: 0:41:54 time: 0.3887 data_time: 0.0128 memory: 6717 grad_norm: 3.3940 loss: 1.0903 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0903 2023/04/14 16:14:27 - mmengine - INFO - Epoch(train) [97][ 740/1879] lr: 2.0000e-04 eta: 0:41:47 time: 0.3744 data_time: 0.0290 memory: 6717 grad_norm: 3.5194 loss: 1.0577 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0577 2023/04/14 16:14:35 - mmengine - INFO - Epoch(train) [97][ 760/1879] lr: 2.0000e-04 eta: 0:41:40 time: 0.3765 data_time: 0.0128 memory: 6717 grad_norm: 3.5573 loss: 1.2531 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2531 2023/04/14 16:14:43 - mmengine - INFO - Epoch(train) [97][ 780/1879] lr: 2.0000e-04 eta: 0:41:32 time: 0.3898 data_time: 0.0164 memory: 6717 grad_norm: 3.4350 loss: 1.1569 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1569 2023/04/14 16:14:51 - mmengine - INFO - Epoch(train) [97][ 800/1879] lr: 2.0000e-04 eta: 0:41:25 time: 0.3869 data_time: 0.0132 memory: 6717 grad_norm: 3.4136 loss: 1.1320 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1320 2023/04/14 16:14:57 - mmengine - INFO - Epoch(train) [97][ 820/1879] lr: 2.0000e-04 eta: 0:41:17 time: 0.3430 data_time: 0.0159 memory: 6717 grad_norm: 3.4777 loss: 1.1915 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1915 2023/04/14 16:15:06 - mmengine - INFO - Epoch(train) [97][ 840/1879] lr: 2.0000e-04 eta: 0:41:10 time: 0.4084 data_time: 0.0127 memory: 6717 grad_norm: 3.5467 loss: 1.1505 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1505 2023/04/14 16:15:12 - mmengine - INFO - Epoch(train) [97][ 860/1879] lr: 2.0000e-04 eta: 0:41:03 time: 0.3303 data_time: 0.0170 memory: 6717 grad_norm: 3.4789 loss: 1.0831 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0831 2023/04/14 16:15:20 - mmengine - INFO - Epoch(train) [97][ 880/1879] lr: 2.0000e-04 eta: 0:40:55 time: 0.3842 data_time: 0.0131 memory: 6717 grad_norm: 3.4074 loss: 1.0055 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0055 2023/04/14 16:15:27 - mmengine - INFO - Epoch(train) [97][ 900/1879] lr: 2.0000e-04 eta: 0:40:48 time: 0.3660 data_time: 0.0158 memory: 6717 grad_norm: 3.4413 loss: 1.0187 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0187 2023/04/14 16:15:34 - mmengine - INFO - Epoch(train) [97][ 920/1879] lr: 2.0000e-04 eta: 0:40:40 time: 0.3541 data_time: 0.0125 memory: 6717 grad_norm: 3.4412 loss: 1.2085 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2085 2023/04/14 16:15:42 - mmengine - INFO - Epoch(train) [97][ 940/1879] lr: 2.0000e-04 eta: 0:40:33 time: 0.3844 data_time: 0.0156 memory: 6717 grad_norm: 3.4878 loss: 1.1469 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.1469 2023/04/14 16:15:49 - mmengine - INFO - Epoch(train) [97][ 960/1879] lr: 2.0000e-04 eta: 0:40:26 time: 0.3301 data_time: 0.0141 memory: 6717 grad_norm: 3.5795 loss: 1.2187 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2187 2023/04/14 16:15:56 - mmengine - INFO - Epoch(train) [97][ 980/1879] lr: 2.0000e-04 eta: 0:40:18 time: 0.3850 data_time: 0.0149 memory: 6717 grad_norm: 3.4528 loss: 1.1324 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1324 2023/04/14 16:16:03 - mmengine - INFO - Epoch(train) [97][1000/1879] lr: 2.0000e-04 eta: 0:40:11 time: 0.3360 data_time: 0.0144 memory: 6717 grad_norm: 3.5084 loss: 1.0303 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0303 2023/04/14 16:16:10 - mmengine - INFO - Epoch(train) [97][1020/1879] lr: 2.0000e-04 eta: 0:40:03 time: 0.3638 data_time: 0.0226 memory: 6717 grad_norm: 3.4673 loss: 1.2673 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2673 2023/04/14 16:16:18 - mmengine - INFO - Epoch(train) [97][1040/1879] lr: 2.0000e-04 eta: 0:39:56 time: 0.3870 data_time: 0.0314 memory: 6717 grad_norm: 3.4472 loss: 1.1139 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1139 2023/04/14 16:16:25 - mmengine - INFO - Epoch(train) [97][1060/1879] lr: 2.0000e-04 eta: 0:39:49 time: 0.3460 data_time: 0.0136 memory: 6717 grad_norm: 3.4656 loss: 1.2098 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2098 2023/04/14 16:16:32 - mmengine - INFO - Epoch(train) [97][1080/1879] lr: 2.0000e-04 eta: 0:39:41 time: 0.3705 data_time: 0.0172 memory: 6717 grad_norm: 3.5568 loss: 1.1150 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1150 2023/04/14 16:16:40 - mmengine - INFO - Epoch(train) [97][1100/1879] lr: 2.0000e-04 eta: 0:39:34 time: 0.3687 data_time: 0.0375 memory: 6717 grad_norm: 3.4858 loss: 1.1748 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.1748 2023/04/14 16:16:48 - mmengine - INFO - Epoch(train) [97][1120/1879] lr: 2.0000e-04 eta: 0:39:26 time: 0.4044 data_time: 0.0413 memory: 6717 grad_norm: 3.4688 loss: 0.8614 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.8614 2023/04/14 16:16:54 - mmengine - INFO - Epoch(train) [97][1140/1879] lr: 2.0000e-04 eta: 0:39:19 time: 0.3117 data_time: 0.0142 memory: 6717 grad_norm: 3.4815 loss: 1.2124 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.2124 2023/04/14 16:17:03 - mmengine - INFO - Epoch(train) [97][1160/1879] lr: 2.0000e-04 eta: 0:39:12 time: 0.4422 data_time: 0.0149 memory: 6717 grad_norm: 3.4382 loss: 1.1925 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1925 2023/04/14 16:17:10 - mmengine - INFO - Epoch(train) [97][1180/1879] lr: 2.0000e-04 eta: 0:39:04 time: 0.3368 data_time: 0.0161 memory: 6717 grad_norm: 3.4439 loss: 1.0409 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0409 2023/04/14 16:17:17 - mmengine - INFO - Epoch(train) [97][1200/1879] lr: 2.0000e-04 eta: 0:38:57 time: 0.3728 data_time: 0.0453 memory: 6717 grad_norm: 3.4571 loss: 1.1487 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1487 2023/04/14 16:17:25 - mmengine - INFO - Epoch(train) [97][1220/1879] lr: 2.0000e-04 eta: 0:38:49 time: 0.3809 data_time: 0.1527 memory: 6717 grad_norm: 3.3554 loss: 1.0784 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0784 2023/04/14 16:17:31 - mmengine - INFO - Epoch(train) [97][1240/1879] lr: 2.0000e-04 eta: 0:38:42 time: 0.3271 data_time: 0.1852 memory: 6717 grad_norm: 3.5348 loss: 1.1101 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1101 2023/04/14 16:17:40 - mmengine - INFO - Epoch(train) [97][1260/1879] lr: 2.0000e-04 eta: 0:38:35 time: 0.4360 data_time: 0.1935 memory: 6717 grad_norm: 3.5424 loss: 1.1902 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.1902 2023/04/14 16:17:47 - mmengine - INFO - Epoch(train) [97][1280/1879] lr: 2.0000e-04 eta: 0:38:27 time: 0.3442 data_time: 0.1085 memory: 6717 grad_norm: 3.4132 loss: 1.1752 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1752 2023/04/14 16:17:55 - mmengine - INFO - Epoch(train) [97][1300/1879] lr: 2.0000e-04 eta: 0:38:20 time: 0.4145 data_time: 0.1208 memory: 6717 grad_norm: 3.4714 loss: 1.0363 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0363 2023/04/14 16:18:01 - mmengine - INFO - Epoch(train) [97][1320/1879] lr: 2.0000e-04 eta: 0:38:12 time: 0.3007 data_time: 0.1146 memory: 6717 grad_norm: 3.4443 loss: 1.1222 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1222 2023/04/14 16:18:10 - mmengine - INFO - Epoch(train) [97][1340/1879] lr: 2.0000e-04 eta: 0:38:05 time: 0.4451 data_time: 0.1445 memory: 6717 grad_norm: 3.3827 loss: 0.9925 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9925 2023/04/14 16:18:17 - mmengine - INFO - Epoch(train) [97][1360/1879] lr: 2.0000e-04 eta: 0:37:58 time: 0.3291 data_time: 0.0662 memory: 6717 grad_norm: 3.5093 loss: 1.1198 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1198 2023/04/14 16:18:25 - mmengine - INFO - Epoch(train) [97][1380/1879] lr: 2.0000e-04 eta: 0:37:50 time: 0.4077 data_time: 0.0219 memory: 6717 grad_norm: 3.5055 loss: 1.1077 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1077 2023/04/14 16:18:31 - mmengine - INFO - Epoch(train) [97][1400/1879] lr: 2.0000e-04 eta: 0:37:43 time: 0.3228 data_time: 0.0634 memory: 6717 grad_norm: 3.4660 loss: 1.0971 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0971 2023/04/14 16:18:40 - mmengine - INFO - Epoch(train) [97][1420/1879] lr: 2.0000e-04 eta: 0:37:35 time: 0.4383 data_time: 0.1184 memory: 6717 grad_norm: 3.4729 loss: 1.2508 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2508 2023/04/14 16:18:46 - mmengine - INFO - Epoch(train) [97][1440/1879] lr: 2.0000e-04 eta: 0:37:28 time: 0.3014 data_time: 0.0751 memory: 6717 grad_norm: 3.4455 loss: 1.1446 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1446 2023/04/14 16:18:54 - mmengine - INFO - Epoch(train) [97][1460/1879] lr: 2.0000e-04 eta: 0:37:21 time: 0.3927 data_time: 0.0812 memory: 6717 grad_norm: 3.4726 loss: 0.9808 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9808 2023/04/14 16:19:01 - mmengine - INFO - Epoch(train) [97][1480/1879] lr: 2.0000e-04 eta: 0:37:13 time: 0.3243 data_time: 0.1215 memory: 6717 grad_norm: 3.3490 loss: 0.9623 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9623 2023/04/14 16:19:09 - mmengine - INFO - Epoch(train) [97][1500/1879] lr: 2.0000e-04 eta: 0:37:06 time: 0.4346 data_time: 0.1805 memory: 6717 grad_norm: 3.3619 loss: 1.0017 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0017 2023/04/14 16:19:16 - mmengine - INFO - Epoch(train) [97][1520/1879] lr: 2.0000e-04 eta: 0:36:58 time: 0.3276 data_time: 0.0560 memory: 6717 grad_norm: 3.4510 loss: 1.0336 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0336 2023/04/14 16:19:24 - mmengine - INFO - Epoch(train) [97][1540/1879] lr: 2.0000e-04 eta: 0:36:51 time: 0.4103 data_time: 0.0791 memory: 6717 grad_norm: 3.4848 loss: 1.2796 top1_acc: 0.5625 top5_acc: 0.6875 loss_cls: 1.2796 2023/04/14 16:19:31 - mmengine - INFO - Epoch(train) [97][1560/1879] lr: 2.0000e-04 eta: 0:36:44 time: 0.3477 data_time: 0.0129 memory: 6717 grad_norm: 3.4140 loss: 1.0369 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0369 2023/04/14 16:19:39 - mmengine - INFO - Epoch(train) [97][1580/1879] lr: 2.0000e-04 eta: 0:36:36 time: 0.4229 data_time: 0.0140 memory: 6717 grad_norm: 3.4763 loss: 1.1807 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1807 2023/04/14 16:19:47 - mmengine - INFO - Epoch(train) [97][1600/1879] lr: 2.0000e-04 eta: 0:36:29 time: 0.3512 data_time: 0.0134 memory: 6717 grad_norm: 3.5232 loss: 1.2072 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2072 2023/04/14 16:19:52 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 16:19:57 - mmengine - INFO - Epoch(train) [97][1620/1879] lr: 2.0000e-04 eta: 0:36:21 time: 0.5291 data_time: 0.0137 memory: 6717 grad_norm: 3.4793 loss: 1.0621 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0621 2023/04/14 16:20:04 - mmengine - INFO - Epoch(train) [97][1640/1879] lr: 2.0000e-04 eta: 0:36:14 time: 0.3235 data_time: 0.0143 memory: 6717 grad_norm: 3.5531 loss: 1.2480 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2480 2023/04/14 16:20:11 - mmengine - INFO - Epoch(train) [97][1660/1879] lr: 2.0000e-04 eta: 0:36:07 time: 0.3867 data_time: 0.0159 memory: 6717 grad_norm: 3.5327 loss: 1.2250 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2250 2023/04/14 16:20:18 - mmengine - INFO - Epoch(train) [97][1680/1879] lr: 2.0000e-04 eta: 0:35:59 time: 0.3521 data_time: 0.0156 memory: 6717 grad_norm: 3.4217 loss: 1.0241 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.0241 2023/04/14 16:20:26 - mmengine - INFO - Epoch(train) [97][1700/1879] lr: 2.0000e-04 eta: 0:35:52 time: 0.3708 data_time: 0.0145 memory: 6717 grad_norm: 3.5626 loss: 1.0734 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0734 2023/04/14 16:20:34 - mmengine - INFO - Epoch(train) [97][1720/1879] lr: 2.0000e-04 eta: 0:35:44 time: 0.3880 data_time: 0.0134 memory: 6717 grad_norm: 3.4587 loss: 1.1869 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1869 2023/04/14 16:20:41 - mmengine - INFO - Epoch(train) [97][1740/1879] lr: 2.0000e-04 eta: 0:35:37 time: 0.3803 data_time: 0.0168 memory: 6717 grad_norm: 3.4751 loss: 1.1307 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1307 2023/04/14 16:20:48 - mmengine - INFO - Epoch(train) [97][1760/1879] lr: 2.0000e-04 eta: 0:35:30 time: 0.3661 data_time: 0.0143 memory: 6717 grad_norm: 3.5699 loss: 1.3647 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.3647 2023/04/14 16:20:55 - mmengine - INFO - Epoch(train) [97][1780/1879] lr: 2.0000e-04 eta: 0:35:22 time: 0.3463 data_time: 0.0148 memory: 6717 grad_norm: 3.4792 loss: 1.1700 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1700 2023/04/14 16:21:03 - mmengine - INFO - Epoch(train) [97][1800/1879] lr: 2.0000e-04 eta: 0:35:15 time: 0.3742 data_time: 0.0151 memory: 6717 grad_norm: 3.5125 loss: 1.2137 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2137 2023/04/14 16:21:10 - mmengine - INFO - Epoch(train) [97][1820/1879] lr: 2.0000e-04 eta: 0:35:07 time: 0.3417 data_time: 0.0125 memory: 6717 grad_norm: 3.4540 loss: 1.1371 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1371 2023/04/14 16:21:17 - mmengine - INFO - Epoch(train) [97][1840/1879] lr: 2.0000e-04 eta: 0:35:00 time: 0.3635 data_time: 0.0153 memory: 6717 grad_norm: 3.5132 loss: 1.0698 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.0698 2023/04/14 16:21:25 - mmengine - INFO - Epoch(train) [97][1860/1879] lr: 2.0000e-04 eta: 0:34:53 time: 0.3972 data_time: 0.0129 memory: 6717 grad_norm: 3.5141 loss: 1.1385 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1385 2023/04/14 16:21:31 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 16:21:31 - mmengine - INFO - Epoch(train) [97][1879/1879] lr: 2.0000e-04 eta: 0:34:46 time: 0.3258 data_time: 0.0128 memory: 6717 grad_norm: 3.4710 loss: 1.1763 top1_acc: 0.4286 top5_acc: 0.8571 loss_cls: 1.1763 2023/04/14 16:21:40 - mmengine - INFO - Epoch(val) [97][ 20/155] eta: 0:01:02 time: 0.4640 data_time: 0.4316 memory: 1391 2023/04/14 16:21:46 - mmengine - INFO - Epoch(val) [97][ 40/155] eta: 0:00:43 time: 0.2957 data_time: 0.2625 memory: 1391 2023/04/14 16:21:55 - mmengine - INFO - Epoch(val) [97][ 60/155] eta: 0:00:38 time: 0.4478 data_time: 0.4146 memory: 1391 2023/04/14 16:22:01 - mmengine - INFO - Epoch(val) [97][ 80/155] eta: 0:00:28 time: 0.3167 data_time: 0.2831 memory: 1391 2023/04/14 16:22:10 - mmengine - INFO - Epoch(val) [97][100/155] eta: 0:00:21 time: 0.4547 data_time: 0.4216 memory: 1391 2023/04/14 16:22:16 - mmengine - INFO - Epoch(val) [97][120/155] eta: 0:00:13 time: 0.3051 data_time: 0.2713 memory: 1391 2023/04/14 16:22:26 - mmengine - INFO - Epoch(val) [97][140/155] eta: 0:00:05 time: 0.4828 data_time: 0.4501 memory: 1391 2023/04/14 16:22:33 - mmengine - INFO - Epoch(val) [97][155/155] acc/top1: 0.6679 acc/top5: 0.8737 acc/mean1: 0.6678 data_time: 0.4200 time: 0.4526 2023/04/14 16:22:43 - mmengine - INFO - Epoch(train) [98][ 20/1879] lr: 2.0000e-04 eta: 0:34:38 time: 0.4670 data_time: 0.3292 memory: 6717 grad_norm: 3.4750 loss: 1.2732 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.2732 2023/04/14 16:22:50 - mmengine - INFO - Epoch(train) [98][ 40/1879] lr: 2.0000e-04 eta: 0:34:31 time: 0.3502 data_time: 0.2183 memory: 6717 grad_norm: 3.3741 loss: 1.0055 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0055 2023/04/14 16:22:58 - mmengine - INFO - Epoch(train) [98][ 60/1879] lr: 2.0000e-04 eta: 0:34:23 time: 0.4155 data_time: 0.2824 memory: 6717 grad_norm: 3.3379 loss: 1.0700 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0700 2023/04/14 16:23:05 - mmengine - INFO - Epoch(train) [98][ 80/1879] lr: 2.0000e-04 eta: 0:34:16 time: 0.3318 data_time: 0.2022 memory: 6717 grad_norm: 3.3713 loss: 1.1736 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1736 2023/04/14 16:23:12 - mmengine - INFO - Epoch(train) [98][ 100/1879] lr: 2.0000e-04 eta: 0:34:09 time: 0.3861 data_time: 0.2511 memory: 6717 grad_norm: 3.4647 loss: 1.1528 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1528 2023/04/14 16:23:19 - mmengine - INFO - Epoch(train) [98][ 120/1879] lr: 2.0000e-04 eta: 0:34:01 time: 0.3278 data_time: 0.1866 memory: 6717 grad_norm: 3.4912 loss: 1.1779 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1779 2023/04/14 16:23:27 - mmengine - INFO - Epoch(train) [98][ 140/1879] lr: 2.0000e-04 eta: 0:33:54 time: 0.3989 data_time: 0.2527 memory: 6717 grad_norm: 3.5017 loss: 1.3321 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3321 2023/04/14 16:23:34 - mmengine - INFO - Epoch(train) [98][ 160/1879] lr: 2.0000e-04 eta: 0:33:46 time: 0.3343 data_time: 0.1909 memory: 6717 grad_norm: 3.4527 loss: 0.9483 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 0.9483 2023/04/14 16:23:42 - mmengine - INFO - Epoch(train) [98][ 180/1879] lr: 2.0000e-04 eta: 0:33:39 time: 0.4246 data_time: 0.2437 memory: 6717 grad_norm: 3.4253 loss: 1.0859 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0859 2023/04/14 16:23:48 - mmengine - INFO - Epoch(train) [98][ 200/1879] lr: 2.0000e-04 eta: 0:33:32 time: 0.3012 data_time: 0.1535 memory: 6717 grad_norm: 3.4265 loss: 1.2428 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2428 2023/04/14 16:23:56 - mmengine - INFO - Epoch(train) [98][ 220/1879] lr: 2.0000e-04 eta: 0:33:24 time: 0.4019 data_time: 0.2645 memory: 6717 grad_norm: 3.4872 loss: 1.0896 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0896 2023/04/14 16:24:03 - mmengine - INFO - Epoch(train) [98][ 240/1879] lr: 2.0000e-04 eta: 0:33:17 time: 0.3248 data_time: 0.1954 memory: 6717 grad_norm: 3.4341 loss: 1.0411 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0411 2023/04/14 16:24:11 - mmengine - INFO - Epoch(train) [98][ 260/1879] lr: 2.0000e-04 eta: 0:33:09 time: 0.4234 data_time: 0.2484 memory: 6717 grad_norm: 3.5109 loss: 1.1659 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1659 2023/04/14 16:24:18 - mmengine - INFO - Epoch(train) [98][ 280/1879] lr: 2.0000e-04 eta: 0:33:02 time: 0.3199 data_time: 0.1240 memory: 6717 grad_norm: 3.4510 loss: 1.1019 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1019 2023/04/14 16:24:26 - mmengine - INFO - Epoch(train) [98][ 300/1879] lr: 2.0000e-04 eta: 0:32:55 time: 0.4271 data_time: 0.2758 memory: 6717 grad_norm: 3.5380 loss: 1.2583 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2583 2023/04/14 16:24:33 - mmengine - INFO - Epoch(train) [98][ 320/1879] lr: 2.0000e-04 eta: 0:32:47 time: 0.3310 data_time: 0.1768 memory: 6717 grad_norm: 3.3531 loss: 1.0300 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0300 2023/04/14 16:24:41 - mmengine - INFO - Epoch(train) [98][ 340/1879] lr: 2.0000e-04 eta: 0:32:40 time: 0.4125 data_time: 0.2747 memory: 6717 grad_norm: 3.5389 loss: 0.9880 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9880 2023/04/14 16:24:48 - mmengine - INFO - Epoch(train) [98][ 360/1879] lr: 2.0000e-04 eta: 0:32:32 time: 0.3475 data_time: 0.2087 memory: 6717 grad_norm: 3.4949 loss: 0.9970 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9970 2023/04/14 16:24:57 - mmengine - INFO - Epoch(train) [98][ 380/1879] lr: 2.0000e-04 eta: 0:32:25 time: 0.4356 data_time: 0.2993 memory: 6717 grad_norm: 3.4525 loss: 1.2084 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2084 2023/04/14 16:25:03 - mmengine - INFO - Epoch(train) [98][ 400/1879] lr: 2.0000e-04 eta: 0:32:18 time: 0.2954 data_time: 0.1575 memory: 6717 grad_norm: 3.5451 loss: 1.2617 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2617 2023/04/14 16:25:10 - mmengine - INFO - Epoch(train) [98][ 420/1879] lr: 2.0000e-04 eta: 0:32:10 time: 0.3788 data_time: 0.2398 memory: 6717 grad_norm: 3.4748 loss: 1.0368 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0368 2023/04/14 16:25:17 - mmengine - INFO - Epoch(train) [98][ 440/1879] lr: 2.0000e-04 eta: 0:32:03 time: 0.3463 data_time: 0.2089 memory: 6717 grad_norm: 3.3769 loss: 1.0923 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0923 2023/04/14 16:25:26 - mmengine - INFO - Epoch(train) [98][ 460/1879] lr: 2.0000e-04 eta: 0:31:55 time: 0.4587 data_time: 0.3209 memory: 6717 grad_norm: 3.3756 loss: 1.0123 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0123 2023/04/14 16:25:33 - mmengine - INFO - Epoch(train) [98][ 480/1879] lr: 2.0000e-04 eta: 0:31:48 time: 0.3256 data_time: 0.1887 memory: 6717 grad_norm: 3.4398 loss: 1.0666 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0666 2023/04/14 16:25:41 - mmengine - INFO - Epoch(train) [98][ 500/1879] lr: 2.0000e-04 eta: 0:31:41 time: 0.4107 data_time: 0.2672 memory: 6717 grad_norm: 3.4484 loss: 0.9889 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9889 2023/04/14 16:25:48 - mmengine - INFO - Epoch(train) [98][ 520/1879] lr: 2.0000e-04 eta: 0:31:33 time: 0.3328 data_time: 0.1863 memory: 6717 grad_norm: 3.4727 loss: 1.1904 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1904 2023/04/14 16:25:55 - mmengine - INFO - Epoch(train) [98][ 540/1879] lr: 2.0000e-04 eta: 0:31:26 time: 0.3857 data_time: 0.2417 memory: 6717 grad_norm: 3.4335 loss: 1.1294 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1294 2023/04/14 16:26:02 - mmengine - INFO - Epoch(train) [98][ 560/1879] lr: 2.0000e-04 eta: 0:31:18 time: 0.3232 data_time: 0.1803 memory: 6717 grad_norm: 3.4941 loss: 1.1685 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1685 2023/04/14 16:26:10 - mmengine - INFO - Epoch(train) [98][ 580/1879] lr: 2.0000e-04 eta: 0:31:11 time: 0.3956 data_time: 0.2565 memory: 6717 grad_norm: 3.4794 loss: 1.0904 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0904 2023/04/14 16:26:17 - mmengine - INFO - Epoch(train) [98][ 600/1879] lr: 2.0000e-04 eta: 0:31:04 time: 0.3366 data_time: 0.1959 memory: 6717 grad_norm: 3.4536 loss: 1.0710 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0710 2023/04/14 16:26:25 - mmengine - INFO - Epoch(train) [98][ 620/1879] lr: 2.0000e-04 eta: 0:30:56 time: 0.4141 data_time: 0.2710 memory: 6717 grad_norm: 3.3740 loss: 1.0264 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0264 2023/04/14 16:26:31 - mmengine - INFO - Epoch(train) [98][ 640/1879] lr: 2.0000e-04 eta: 0:30:49 time: 0.3210 data_time: 0.1620 memory: 6717 grad_norm: 3.4264 loss: 1.1067 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1067 2023/04/14 16:26:40 - mmengine - INFO - Epoch(train) [98][ 660/1879] lr: 2.0000e-04 eta: 0:30:41 time: 0.4214 data_time: 0.2663 memory: 6717 grad_norm: 3.4207 loss: 1.0988 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0988 2023/04/14 16:26:46 - mmengine - INFO - Epoch(train) [98][ 680/1879] lr: 2.0000e-04 eta: 0:30:34 time: 0.3085 data_time: 0.1484 memory: 6717 grad_norm: 3.5594 loss: 1.1452 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1452 2023/04/14 16:26:54 - mmengine - INFO - Epoch(train) [98][ 700/1879] lr: 2.0000e-04 eta: 0:30:27 time: 0.3987 data_time: 0.2334 memory: 6717 grad_norm: 3.4597 loss: 1.0436 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0436 2023/04/14 16:27:01 - mmengine - INFO - Epoch(train) [98][ 720/1879] lr: 2.0000e-04 eta: 0:30:19 time: 0.3467 data_time: 0.1636 memory: 6717 grad_norm: 3.4808 loss: 1.1016 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.1016 2023/04/14 16:27:07 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 16:27:09 - mmengine - INFO - Epoch(train) [98][ 740/1879] lr: 2.0000e-04 eta: 0:30:12 time: 0.4164 data_time: 0.2712 memory: 6717 grad_norm: 3.5026 loss: 1.1225 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1225 2023/04/14 16:27:16 - mmengine - INFO - Epoch(train) [98][ 760/1879] lr: 2.0000e-04 eta: 0:30:04 time: 0.3242 data_time: 0.1816 memory: 6717 grad_norm: 3.4391 loss: 1.0122 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0122 2023/04/14 16:27:24 - mmengine - INFO - Epoch(train) [98][ 780/1879] lr: 2.0000e-04 eta: 0:29:57 time: 0.4291 data_time: 0.2307 memory: 6717 grad_norm: 3.4450 loss: 1.1490 top1_acc: 0.9375 top5_acc: 0.9375 loss_cls: 1.1490 2023/04/14 16:27:30 - mmengine - INFO - Epoch(train) [98][ 800/1879] lr: 2.0000e-04 eta: 0:29:50 time: 0.2995 data_time: 0.1193 memory: 6717 grad_norm: 3.4748 loss: 1.1102 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.1102 2023/04/14 16:27:39 - mmengine - INFO - Epoch(train) [98][ 820/1879] lr: 2.0000e-04 eta: 0:29:42 time: 0.4429 data_time: 0.0841 memory: 6717 grad_norm: 3.4813 loss: 1.0137 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0137 2023/04/14 16:27:46 - mmengine - INFO - Epoch(train) [98][ 840/1879] lr: 2.0000e-04 eta: 0:29:35 time: 0.3357 data_time: 0.0661 memory: 6717 grad_norm: 3.5954 loss: 1.1549 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1549 2023/04/14 16:27:54 - mmengine - INFO - Epoch(train) [98][ 860/1879] lr: 2.0000e-04 eta: 0:29:27 time: 0.4090 data_time: 0.0144 memory: 6717 grad_norm: 3.4953 loss: 1.2520 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.2520 2023/04/14 16:28:01 - mmengine - INFO - Epoch(train) [98][ 880/1879] lr: 2.0000e-04 eta: 0:29:20 time: 0.3281 data_time: 0.0141 memory: 6717 grad_norm: 3.4479 loss: 1.2670 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2670 2023/04/14 16:28:09 - mmengine - INFO - Epoch(train) [98][ 900/1879] lr: 2.0000e-04 eta: 0:29:13 time: 0.4073 data_time: 0.0269 memory: 6717 grad_norm: 3.5498 loss: 1.1369 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1369 2023/04/14 16:28:15 - mmengine - INFO - Epoch(train) [98][ 920/1879] lr: 2.0000e-04 eta: 0:29:05 time: 0.3073 data_time: 0.0208 memory: 6717 grad_norm: 3.4296 loss: 1.1002 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.1002 2023/04/14 16:28:23 - mmengine - INFO - Epoch(train) [98][ 940/1879] lr: 2.0000e-04 eta: 0:28:58 time: 0.4108 data_time: 0.1058 memory: 6717 grad_norm: 3.4732 loss: 1.0130 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0130 2023/04/14 16:28:30 - mmengine - INFO - Epoch(train) [98][ 960/1879] lr: 2.0000e-04 eta: 0:28:50 time: 0.3230 data_time: 0.0831 memory: 6717 grad_norm: 3.4127 loss: 1.0566 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0566 2023/04/14 16:28:37 - mmengine - INFO - Epoch(train) [98][ 980/1879] lr: 2.0000e-04 eta: 0:28:43 time: 0.3841 data_time: 0.2047 memory: 6717 grad_norm: 3.5180 loss: 1.1301 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1301 2023/04/14 16:28:44 - mmengine - INFO - Epoch(train) [98][1000/1879] lr: 2.0000e-04 eta: 0:28:36 time: 0.3578 data_time: 0.0890 memory: 6717 grad_norm: 3.5376 loss: 1.2304 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2304 2023/04/14 16:28:53 - mmengine - INFO - Epoch(train) [98][1020/1879] lr: 2.0000e-04 eta: 0:28:28 time: 0.4069 data_time: 0.0693 memory: 6717 grad_norm: 3.4686 loss: 1.1401 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1401 2023/04/14 16:28:59 - mmengine - INFO - Epoch(train) [98][1040/1879] lr: 2.0000e-04 eta: 0:28:21 time: 0.3421 data_time: 0.0522 memory: 6717 grad_norm: 3.4515 loss: 1.2276 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2276 2023/04/14 16:29:08 - mmengine - INFO - Epoch(train) [98][1060/1879] lr: 2.0000e-04 eta: 0:28:13 time: 0.4536 data_time: 0.0142 memory: 6717 grad_norm: 3.5151 loss: 1.1665 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1665 2023/04/14 16:29:15 - mmengine - INFO - Epoch(train) [98][1080/1879] lr: 2.0000e-04 eta: 0:28:06 time: 0.3175 data_time: 0.0130 memory: 6717 grad_norm: 3.5341 loss: 1.1049 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1049 2023/04/14 16:29:23 - mmengine - INFO - Epoch(train) [98][1100/1879] lr: 2.0000e-04 eta: 0:27:59 time: 0.4231 data_time: 0.0127 memory: 6717 grad_norm: 3.4845 loss: 1.0038 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0038 2023/04/14 16:29:30 - mmengine - INFO - Epoch(train) [98][1120/1879] lr: 2.0000e-04 eta: 0:27:51 time: 0.3237 data_time: 0.0143 memory: 6717 grad_norm: 3.4981 loss: 1.2258 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2258 2023/04/14 16:29:38 - mmengine - INFO - Epoch(train) [98][1140/1879] lr: 2.0000e-04 eta: 0:27:44 time: 0.4028 data_time: 0.0154 memory: 6717 grad_norm: 3.4972 loss: 1.1675 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1675 2023/04/14 16:29:45 - mmengine - INFO - Epoch(train) [98][1160/1879] lr: 2.0000e-04 eta: 0:27:36 time: 0.3430 data_time: 0.0137 memory: 6717 grad_norm: 3.5176 loss: 0.9929 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9929 2023/04/14 16:29:53 - mmengine - INFO - Epoch(train) [98][1180/1879] lr: 2.0000e-04 eta: 0:27:29 time: 0.4384 data_time: 0.0133 memory: 6717 grad_norm: 3.4203 loss: 1.1890 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1890 2023/04/14 16:30:00 - mmengine - INFO - Epoch(train) [98][1200/1879] lr: 2.0000e-04 eta: 0:27:22 time: 0.3354 data_time: 0.0134 memory: 6717 grad_norm: 3.5617 loss: 1.1649 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1649 2023/04/14 16:30:08 - mmengine - INFO - Epoch(train) [98][1220/1879] lr: 2.0000e-04 eta: 0:27:14 time: 0.3924 data_time: 0.0148 memory: 6717 grad_norm: 3.4882 loss: 1.2459 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.2459 2023/04/14 16:30:14 - mmengine - INFO - Epoch(train) [98][1240/1879] lr: 2.0000e-04 eta: 0:27:07 time: 0.3196 data_time: 0.0149 memory: 6717 grad_norm: 3.4984 loss: 1.1860 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1860 2023/04/14 16:30:23 - mmengine - INFO - Epoch(train) [98][1260/1879] lr: 2.0000e-04 eta: 0:26:59 time: 0.4051 data_time: 0.0146 memory: 6717 grad_norm: 3.5110 loss: 1.1082 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1082 2023/04/14 16:30:29 - mmengine - INFO - Epoch(train) [98][1280/1879] lr: 2.0000e-04 eta: 0:26:52 time: 0.3187 data_time: 0.0143 memory: 6717 grad_norm: 3.4499 loss: 1.0733 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0733 2023/04/14 16:30:37 - mmengine - INFO - Epoch(train) [98][1300/1879] lr: 2.0000e-04 eta: 0:26:45 time: 0.3856 data_time: 0.0131 memory: 6717 grad_norm: 3.4572 loss: 0.9717 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 0.9717 2023/04/14 16:30:43 - mmengine - INFO - Epoch(train) [98][1320/1879] lr: 2.0000e-04 eta: 0:26:37 time: 0.3389 data_time: 0.0149 memory: 6717 grad_norm: 3.4446 loss: 0.9352 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9352 2023/04/14 16:30:52 - mmengine - INFO - Epoch(train) [98][1340/1879] lr: 2.0000e-04 eta: 0:26:30 time: 0.4449 data_time: 0.0138 memory: 6717 grad_norm: 3.4853 loss: 1.1097 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1097 2023/04/14 16:30:58 - mmengine - INFO - Epoch(train) [98][1360/1879] lr: 2.0000e-04 eta: 0:26:22 time: 0.2826 data_time: 0.0143 memory: 6717 grad_norm: 3.4322 loss: 1.1130 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1130 2023/04/14 16:31:06 - mmengine - INFO - Epoch(train) [98][1380/1879] lr: 2.0000e-04 eta: 0:26:15 time: 0.3958 data_time: 0.0169 memory: 6717 grad_norm: 3.3986 loss: 1.1083 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1083 2023/04/14 16:31:12 - mmengine - INFO - Epoch(train) [98][1400/1879] lr: 2.0000e-04 eta: 0:26:07 time: 0.3250 data_time: 0.0136 memory: 6717 grad_norm: 3.5046 loss: 1.1733 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1733 2023/04/14 16:31:21 - mmengine - INFO - Epoch(train) [98][1420/1879] lr: 2.0000e-04 eta: 0:26:00 time: 0.4119 data_time: 0.0142 memory: 6717 grad_norm: 3.5076 loss: 1.2836 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.2836 2023/04/14 16:31:27 - mmengine - INFO - Epoch(train) [98][1440/1879] lr: 2.0000e-04 eta: 0:25:53 time: 0.3122 data_time: 0.0180 memory: 6717 grad_norm: 3.4269 loss: 1.0797 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0797 2023/04/14 16:31:36 - mmengine - INFO - Epoch(train) [98][1460/1879] lr: 2.0000e-04 eta: 0:25:45 time: 0.4450 data_time: 0.0152 memory: 6717 grad_norm: 3.4448 loss: 1.0508 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0508 2023/04/14 16:31:42 - mmengine - INFO - Epoch(train) [98][1480/1879] lr: 2.0000e-04 eta: 0:25:38 time: 0.3043 data_time: 0.0135 memory: 6717 grad_norm: 3.4815 loss: 1.0693 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0693 2023/04/14 16:31:49 - mmengine - INFO - Epoch(train) [98][1500/1879] lr: 2.0000e-04 eta: 0:25:30 time: 0.3704 data_time: 0.0152 memory: 6717 grad_norm: 3.4808 loss: 1.1610 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1610 2023/04/14 16:31:57 - mmengine - INFO - Epoch(train) [98][1520/1879] lr: 2.0000e-04 eta: 0:25:23 time: 0.4050 data_time: 0.0126 memory: 6717 grad_norm: 3.5316 loss: 1.1777 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1777 2023/04/14 16:32:05 - mmengine - INFO - Epoch(train) [98][1540/1879] lr: 2.0000e-04 eta: 0:25:16 time: 0.3600 data_time: 0.0150 memory: 6717 grad_norm: 3.4867 loss: 1.2219 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2219 2023/04/14 16:32:13 - mmengine - INFO - Epoch(train) [98][1560/1879] lr: 2.0000e-04 eta: 0:25:08 time: 0.4241 data_time: 0.0141 memory: 6717 grad_norm: 3.4642 loss: 1.1560 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.1560 2023/04/14 16:32:19 - mmengine - INFO - Epoch(train) [98][1580/1879] lr: 2.0000e-04 eta: 0:25:01 time: 0.3122 data_time: 0.0128 memory: 6717 grad_norm: 3.4722 loss: 1.2671 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2671 2023/04/14 16:32:27 - mmengine - INFO - Epoch(train) [98][1600/1879] lr: 2.0000e-04 eta: 0:24:53 time: 0.4022 data_time: 0.0142 memory: 6717 grad_norm: 3.4522 loss: 1.2915 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.2915 2023/04/14 16:32:34 - mmengine - INFO - Epoch(train) [98][1620/1879] lr: 2.0000e-04 eta: 0:24:46 time: 0.3509 data_time: 0.0145 memory: 6717 grad_norm: 3.5075 loss: 1.2302 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2302 2023/04/14 16:32:43 - mmengine - INFO - Epoch(train) [98][1640/1879] lr: 2.0000e-04 eta: 0:24:39 time: 0.4313 data_time: 0.0139 memory: 6717 grad_norm: 3.5185 loss: 1.1682 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1682 2023/04/14 16:32:49 - mmengine - INFO - Epoch(train) [98][1660/1879] lr: 2.0000e-04 eta: 0:24:31 time: 0.3189 data_time: 0.0140 memory: 6717 grad_norm: 3.5196 loss: 1.2075 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.2075 2023/04/14 16:32:58 - mmengine - INFO - Epoch(train) [98][1680/1879] lr: 2.0000e-04 eta: 0:24:24 time: 0.4186 data_time: 0.0145 memory: 6717 grad_norm: 3.4981 loss: 1.2477 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2477 2023/04/14 16:33:04 - mmengine - INFO - Epoch(train) [98][1700/1879] lr: 2.0000e-04 eta: 0:24:16 time: 0.3170 data_time: 0.0141 memory: 6717 grad_norm: 3.4635 loss: 1.2403 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2403 2023/04/14 16:33:12 - mmengine - INFO - Epoch(train) [98][1720/1879] lr: 2.0000e-04 eta: 0:24:09 time: 0.3900 data_time: 0.0134 memory: 6717 grad_norm: 3.5069 loss: 1.1998 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1998 2023/04/14 16:33:17 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 16:33:18 - mmengine - INFO - Epoch(train) [98][1740/1879] lr: 2.0000e-04 eta: 0:24:02 time: 0.3125 data_time: 0.0144 memory: 6717 grad_norm: 3.5311 loss: 1.1761 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1761 2023/04/14 16:33:26 - mmengine - INFO - Epoch(train) [98][1760/1879] lr: 2.0000e-04 eta: 0:23:54 time: 0.3802 data_time: 0.0137 memory: 6717 grad_norm: 3.4234 loss: 1.0335 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0335 2023/04/14 16:33:34 - mmengine - INFO - Epoch(train) [98][1780/1879] lr: 2.0000e-04 eta: 0:23:47 time: 0.3938 data_time: 0.0128 memory: 6717 grad_norm: 3.4708 loss: 1.1754 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1754 2023/04/14 16:33:41 - mmengine - INFO - Epoch(train) [98][1800/1879] lr: 2.0000e-04 eta: 0:23:39 time: 0.3504 data_time: 0.0132 memory: 6717 grad_norm: 3.4603 loss: 0.9837 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9837 2023/04/14 16:33:49 - mmengine - INFO - Epoch(train) [98][1820/1879] lr: 2.0000e-04 eta: 0:23:32 time: 0.3989 data_time: 0.0148 memory: 6717 grad_norm: 3.4564 loss: 1.0920 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0920 2023/04/14 16:33:55 - mmengine - INFO - Epoch(train) [98][1840/1879] lr: 2.0000e-04 eta: 0:23:25 time: 0.3329 data_time: 0.0139 memory: 6717 grad_norm: 3.4772 loss: 1.1006 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.1006 2023/04/14 16:34:03 - mmengine - INFO - Epoch(train) [98][1860/1879] lr: 2.0000e-04 eta: 0:23:17 time: 0.3846 data_time: 0.0129 memory: 6717 grad_norm: 3.4595 loss: 1.0285 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0285 2023/04/14 16:34:09 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 16:34:09 - mmengine - INFO - Epoch(train) [98][1879/1879] lr: 2.0000e-04 eta: 0:23:10 time: 0.3700 data_time: 0.0128 memory: 6717 grad_norm: 3.8887 loss: 1.2864 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.2864 2023/04/14 16:34:18 - mmengine - INFO - Epoch(val) [98][ 20/155] eta: 0:01:02 time: 0.4666 data_time: 0.4333 memory: 1391 2023/04/14 16:34:25 - mmengine - INFO - Epoch(val) [98][ 40/155] eta: 0:00:44 time: 0.3094 data_time: 0.2769 memory: 1391 2023/04/14 16:34:33 - mmengine - INFO - Epoch(val) [98][ 60/155] eta: 0:00:38 time: 0.4299 data_time: 0.3961 memory: 1391 2023/04/14 16:34:40 - mmengine - INFO - Epoch(val) [98][ 80/155] eta: 0:00:28 time: 0.3176 data_time: 0.2845 memory: 1391 2023/04/14 16:34:49 - mmengine - INFO - Epoch(val) [98][100/155] eta: 0:00:21 time: 0.4565 data_time: 0.4237 memory: 1391 2023/04/14 16:34:55 - mmengine - INFO - Epoch(val) [98][120/155] eta: 0:00:13 time: 0.2957 data_time: 0.2624 memory: 1391 2023/04/14 16:35:04 - mmengine - INFO - Epoch(val) [98][140/155] eta: 0:00:05 time: 0.4454 data_time: 0.4122 memory: 1391 2023/04/14 16:35:11 - mmengine - INFO - Epoch(val) [98][155/155] acc/top1: 0.6685 acc/top5: 0.8744 acc/mean1: 0.6685 data_time: 0.3672 time: 0.4000 2023/04/14 16:35:21 - mmengine - INFO - Epoch(train) [99][ 20/1879] lr: 2.0000e-04 eta: 0:23:03 time: 0.5256 data_time: 0.2594 memory: 6717 grad_norm: 3.5127 loss: 1.0819 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.0819 2023/04/14 16:35:28 - mmengine - INFO - Epoch(train) [99][ 40/1879] lr: 2.0000e-04 eta: 0:22:55 time: 0.3254 data_time: 0.0419 memory: 6717 grad_norm: 3.3662 loss: 0.9823 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9823 2023/04/14 16:35:36 - mmengine - INFO - Epoch(train) [99][ 60/1879] lr: 2.0000e-04 eta: 0:22:48 time: 0.4250 data_time: 0.0221 memory: 6717 grad_norm: 3.5446 loss: 1.1246 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1246 2023/04/14 16:35:42 - mmengine - INFO - Epoch(train) [99][ 80/1879] lr: 2.0000e-04 eta: 0:22:41 time: 0.3111 data_time: 0.0140 memory: 6717 grad_norm: 3.4577 loss: 1.1927 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1927 2023/04/14 16:35:51 - mmengine - INFO - Epoch(train) [99][ 100/1879] lr: 2.0000e-04 eta: 0:22:33 time: 0.4132 data_time: 0.0521 memory: 6717 grad_norm: 3.4460 loss: 1.1504 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1504 2023/04/14 16:35:58 - mmengine - INFO - Epoch(train) [99][ 120/1879] lr: 2.0000e-04 eta: 0:22:26 time: 0.3438 data_time: 0.0219 memory: 6717 grad_norm: 3.5844 loss: 1.0539 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0539 2023/04/14 16:36:06 - mmengine - INFO - Epoch(train) [99][ 140/1879] lr: 2.0000e-04 eta: 0:22:18 time: 0.4161 data_time: 0.0141 memory: 6717 grad_norm: 3.4766 loss: 1.1912 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1912 2023/04/14 16:36:12 - mmengine - INFO - Epoch(train) [99][ 160/1879] lr: 2.0000e-04 eta: 0:22:11 time: 0.2924 data_time: 0.0155 memory: 6717 grad_norm: 3.4844 loss: 1.1349 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1349 2023/04/14 16:36:21 - mmengine - INFO - Epoch(train) [99][ 180/1879] lr: 2.0000e-04 eta: 0:22:04 time: 0.4387 data_time: 0.0328 memory: 6717 grad_norm: 3.4229 loss: 0.9371 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9371 2023/04/14 16:36:27 - mmengine - INFO - Epoch(train) [99][ 200/1879] lr: 2.0000e-04 eta: 0:21:56 time: 0.3183 data_time: 0.0361 memory: 6717 grad_norm: 3.4064 loss: 1.1668 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1668 2023/04/14 16:36:35 - mmengine - INFO - Epoch(train) [99][ 220/1879] lr: 2.0000e-04 eta: 0:21:49 time: 0.3973 data_time: 0.0805 memory: 6717 grad_norm: 3.5442 loss: 1.1599 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.1599 2023/04/14 16:36:42 - mmengine - INFO - Epoch(train) [99][ 240/1879] lr: 2.0000e-04 eta: 0:21:41 time: 0.3526 data_time: 0.0553 memory: 6717 grad_norm: 3.5164 loss: 1.0055 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0055 2023/04/14 16:36:51 - mmengine - INFO - Epoch(train) [99][ 260/1879] lr: 2.0000e-04 eta: 0:21:34 time: 0.4273 data_time: 0.0259 memory: 6717 grad_norm: 3.4508 loss: 1.1700 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1700 2023/04/14 16:36:57 - mmengine - INFO - Epoch(train) [99][ 280/1879] lr: 2.0000e-04 eta: 0:21:27 time: 0.3072 data_time: 0.0129 memory: 6717 grad_norm: 3.4423 loss: 1.1502 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1502 2023/04/14 16:37:05 - mmengine - INFO - Epoch(train) [99][ 300/1879] lr: 2.0000e-04 eta: 0:21:19 time: 0.4322 data_time: 0.0234 memory: 6717 grad_norm: 3.4817 loss: 1.0518 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0518 2023/04/14 16:37:12 - mmengine - INFO - Epoch(train) [99][ 320/1879] lr: 2.0000e-04 eta: 0:21:12 time: 0.3426 data_time: 0.0127 memory: 6717 grad_norm: 3.4546 loss: 1.0351 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0351 2023/04/14 16:37:20 - mmengine - INFO - Epoch(train) [99][ 340/1879] lr: 2.0000e-04 eta: 0:21:04 time: 0.4016 data_time: 0.0166 memory: 6717 grad_norm: 3.4948 loss: 1.0463 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.0463 2023/04/14 16:37:27 - mmengine - INFO - Epoch(train) [99][ 360/1879] lr: 2.0000e-04 eta: 0:20:57 time: 0.3185 data_time: 0.0123 memory: 6717 grad_norm: 3.3627 loss: 0.9233 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9233 2023/04/14 16:37:35 - mmengine - INFO - Epoch(train) [99][ 380/1879] lr: 2.0000e-04 eta: 0:20:50 time: 0.4440 data_time: 0.0159 memory: 6717 grad_norm: 3.4630 loss: 1.0058 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0058 2023/04/14 16:37:42 - mmengine - INFO - Epoch(train) [99][ 400/1879] lr: 2.0000e-04 eta: 0:20:42 time: 0.3345 data_time: 0.0128 memory: 6717 grad_norm: 3.4519 loss: 1.2821 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.2821 2023/04/14 16:37:51 - mmengine - INFO - Epoch(train) [99][ 420/1879] lr: 2.0000e-04 eta: 0:20:35 time: 0.4198 data_time: 0.0150 memory: 6717 grad_norm: 3.4493 loss: 1.1161 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1161 2023/04/14 16:37:56 - mmengine - INFO - Epoch(train) [99][ 440/1879] lr: 2.0000e-04 eta: 0:20:27 time: 0.2828 data_time: 0.0124 memory: 6717 grad_norm: 3.5348 loss: 1.1564 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.1564 2023/04/14 16:38:04 - mmengine - INFO - Epoch(train) [99][ 460/1879] lr: 2.0000e-04 eta: 0:20:20 time: 0.4041 data_time: 0.0162 memory: 6717 grad_norm: 3.4292 loss: 0.9559 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9559 2023/04/14 16:38:11 - mmengine - INFO - Epoch(train) [99][ 480/1879] lr: 2.0000e-04 eta: 0:20:13 time: 0.3315 data_time: 0.0130 memory: 6717 grad_norm: 3.4157 loss: 1.3427 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3427 2023/04/14 16:38:20 - mmengine - INFO - Epoch(train) [99][ 500/1879] lr: 2.0000e-04 eta: 0:20:05 time: 0.4539 data_time: 0.0150 memory: 6717 grad_norm: 3.4130 loss: 1.0809 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 1.0809 2023/04/14 16:38:26 - mmengine - INFO - Epoch(train) [99][ 520/1879] lr: 2.0000e-04 eta: 0:19:58 time: 0.2912 data_time: 0.0123 memory: 6717 grad_norm: 3.4358 loss: 0.9832 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9832 2023/04/14 16:38:34 - mmengine - INFO - Epoch(train) [99][ 540/1879] lr: 2.0000e-04 eta: 0:19:50 time: 0.4063 data_time: 0.0181 memory: 6717 grad_norm: 3.4698 loss: 1.0660 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0660 2023/04/14 16:38:41 - mmengine - INFO - Epoch(train) [99][ 560/1879] lr: 2.0000e-04 eta: 0:19:43 time: 0.3322 data_time: 0.0142 memory: 6717 grad_norm: 3.5327 loss: 1.3020 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3020 2023/04/14 16:38:49 - mmengine - INFO - Epoch(train) [99][ 580/1879] lr: 2.0000e-04 eta: 0:19:36 time: 0.4218 data_time: 0.0157 memory: 6717 grad_norm: 3.4474 loss: 1.0566 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0566 2023/04/14 16:38:56 - mmengine - INFO - Epoch(train) [99][ 600/1879] lr: 2.0000e-04 eta: 0:19:28 time: 0.3385 data_time: 0.0130 memory: 6717 grad_norm: 3.4749 loss: 0.9506 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9506 2023/04/14 16:39:05 - mmengine - INFO - Epoch(train) [99][ 620/1879] lr: 2.0000e-04 eta: 0:19:21 time: 0.4536 data_time: 0.0474 memory: 6717 grad_norm: 3.4419 loss: 1.2136 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2136 2023/04/14 16:39:11 - mmengine - INFO - Epoch(train) [99][ 640/1879] lr: 2.0000e-04 eta: 0:19:13 time: 0.3128 data_time: 0.0750 memory: 6717 grad_norm: 3.4811 loss: 1.1054 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1054 2023/04/14 16:39:19 - mmengine - INFO - Epoch(train) [99][ 660/1879] lr: 2.0000e-04 eta: 0:19:06 time: 0.4101 data_time: 0.0453 memory: 6717 grad_norm: 3.4617 loss: 1.1837 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1837 2023/04/14 16:39:26 - mmengine - INFO - Epoch(train) [99][ 680/1879] lr: 2.0000e-04 eta: 0:18:59 time: 0.3098 data_time: 0.0129 memory: 6717 grad_norm: 3.4676 loss: 1.1158 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1158 2023/04/14 16:39:34 - mmengine - INFO - Epoch(train) [99][ 700/1879] lr: 2.0000e-04 eta: 0:18:51 time: 0.4070 data_time: 0.0158 memory: 6717 grad_norm: 3.4947 loss: 1.2433 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2433 2023/04/14 16:39:41 - mmengine - INFO - Epoch(train) [99][ 720/1879] lr: 2.0000e-04 eta: 0:18:44 time: 0.3361 data_time: 0.0135 memory: 6717 grad_norm: 3.4314 loss: 1.0878 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0878 2023/04/14 16:39:49 - mmengine - INFO - Epoch(train) [99][ 740/1879] lr: 2.0000e-04 eta: 0:18:36 time: 0.4125 data_time: 0.0156 memory: 6717 grad_norm: 3.4114 loss: 1.0176 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0176 2023/04/14 16:39:56 - mmengine - INFO - Epoch(train) [99][ 760/1879] lr: 2.0000e-04 eta: 0:18:29 time: 0.3436 data_time: 0.0122 memory: 6717 grad_norm: 3.4610 loss: 1.1637 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1637 2023/04/14 16:40:03 - mmengine - INFO - Epoch(train) [99][ 780/1879] lr: 2.0000e-04 eta: 0:18:22 time: 0.3843 data_time: 0.0148 memory: 6717 grad_norm: 3.4272 loss: 1.0582 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0582 2023/04/14 16:40:09 - mmengine - INFO - Epoch(train) [99][ 800/1879] lr: 2.0000e-04 eta: 0:18:14 time: 0.3004 data_time: 0.0137 memory: 6717 grad_norm: 3.5349 loss: 1.2366 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2366 2023/04/14 16:40:18 - mmengine - INFO - Epoch(train) [99][ 820/1879] lr: 2.0000e-04 eta: 0:18:07 time: 0.4541 data_time: 0.0194 memory: 6717 grad_norm: 3.4597 loss: 1.1329 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1329 2023/04/14 16:40:25 - mmengine - INFO - Epoch(train) [99][ 840/1879] lr: 2.0000e-04 eta: 0:17:59 time: 0.3409 data_time: 0.0139 memory: 6717 grad_norm: 3.4797 loss: 1.0933 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0933 2023/04/14 16:40:32 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 16:40:33 - mmengine - INFO - Epoch(train) [99][ 860/1879] lr: 2.0000e-04 eta: 0:17:52 time: 0.4074 data_time: 0.0145 memory: 6717 grad_norm: 3.4800 loss: 1.1149 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1149 2023/04/14 16:40:40 - mmengine - INFO - Epoch(train) [99][ 880/1879] lr: 2.0000e-04 eta: 0:17:45 time: 0.3403 data_time: 0.0131 memory: 6717 grad_norm: 3.4950 loss: 1.0662 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0662 2023/04/14 16:40:48 - mmengine - INFO - Epoch(train) [99][ 900/1879] lr: 2.0000e-04 eta: 0:17:37 time: 0.3677 data_time: 0.0140 memory: 6717 grad_norm: 3.5142 loss: 1.2099 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2099 2023/04/14 16:40:54 - mmengine - INFO - Epoch(train) [99][ 920/1879] lr: 2.0000e-04 eta: 0:17:30 time: 0.3153 data_time: 0.0144 memory: 6717 grad_norm: 3.4566 loss: 1.0380 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0380 2023/04/14 16:41:02 - mmengine - INFO - Epoch(train) [99][ 940/1879] lr: 2.0000e-04 eta: 0:17:22 time: 0.4001 data_time: 0.0153 memory: 6717 grad_norm: 3.4755 loss: 1.0207 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.0207 2023/04/14 16:41:08 - mmengine - INFO - Epoch(train) [99][ 960/1879] lr: 2.0000e-04 eta: 0:17:15 time: 0.3156 data_time: 0.0131 memory: 6717 grad_norm: 3.6107 loss: 1.1700 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1700 2023/04/14 16:41:16 - mmengine - INFO - Epoch(train) [99][ 980/1879] lr: 2.0000e-04 eta: 0:17:08 time: 0.3832 data_time: 0.0161 memory: 6717 grad_norm: 3.5697 loss: 1.1133 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1133 2023/04/14 16:41:23 - mmengine - INFO - Epoch(train) [99][1000/1879] lr: 2.0000e-04 eta: 0:17:00 time: 0.3661 data_time: 0.0140 memory: 6717 grad_norm: 3.4217 loss: 1.0149 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0149 2023/04/14 16:41:31 - mmengine - INFO - Epoch(train) [99][1020/1879] lr: 2.0000e-04 eta: 0:16:53 time: 0.3660 data_time: 0.0157 memory: 6717 grad_norm: 3.3752 loss: 1.0449 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0449 2023/04/14 16:41:38 - mmengine - INFO - Epoch(train) [99][1040/1879] lr: 2.0000e-04 eta: 0:16:45 time: 0.3796 data_time: 0.0140 memory: 6717 grad_norm: 3.5835 loss: 1.2128 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.2128 2023/04/14 16:41:46 - mmengine - INFO - Epoch(train) [99][1060/1879] lr: 2.0000e-04 eta: 0:16:38 time: 0.3738 data_time: 0.0161 memory: 6717 grad_norm: 3.4294 loss: 1.1672 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1672 2023/04/14 16:41:53 - mmengine - INFO - Epoch(train) [99][1080/1879] lr: 2.0000e-04 eta: 0:16:31 time: 0.3918 data_time: 0.0126 memory: 6717 grad_norm: 3.5622 loss: 1.2127 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2127 2023/04/14 16:42:00 - mmengine - INFO - Epoch(train) [99][1100/1879] lr: 2.0000e-04 eta: 0:16:23 time: 0.3288 data_time: 0.0154 memory: 6717 grad_norm: 3.4322 loss: 1.2175 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2175 2023/04/14 16:42:07 - mmengine - INFO - Epoch(train) [99][1120/1879] lr: 2.0000e-04 eta: 0:16:16 time: 0.3608 data_time: 0.0140 memory: 6717 grad_norm: 3.5766 loss: 1.1084 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 1.1084 2023/04/14 16:42:15 - mmengine - INFO - Epoch(train) [99][1140/1879] lr: 2.0000e-04 eta: 0:16:08 time: 0.3823 data_time: 0.0177 memory: 6717 grad_norm: 3.4004 loss: 0.9660 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 0.9660 2023/04/14 16:42:23 - mmengine - INFO - Epoch(train) [99][1160/1879] lr: 2.0000e-04 eta: 0:16:01 time: 0.4045 data_time: 0.0134 memory: 6717 grad_norm: 3.5280 loss: 1.1849 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1849 2023/04/14 16:42:30 - mmengine - INFO - Epoch(train) [99][1180/1879] lr: 2.0000e-04 eta: 0:15:54 time: 0.3257 data_time: 0.0155 memory: 6717 grad_norm: 3.5409 loss: 1.3101 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.3101 2023/04/14 16:42:38 - mmengine - INFO - Epoch(train) [99][1200/1879] lr: 2.0000e-04 eta: 0:15:46 time: 0.4227 data_time: 0.0125 memory: 6717 grad_norm: 3.5205 loss: 0.9879 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9879 2023/04/14 16:42:45 - mmengine - INFO - Epoch(train) [99][1220/1879] lr: 2.0000e-04 eta: 0:15:39 time: 0.3271 data_time: 0.0163 memory: 6717 grad_norm: 3.4046 loss: 0.9661 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 0.9661 2023/04/14 16:42:53 - mmengine - INFO - Epoch(train) [99][1240/1879] lr: 2.0000e-04 eta: 0:15:31 time: 0.4201 data_time: 0.0133 memory: 6717 grad_norm: 3.4691 loss: 1.1205 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1205 2023/04/14 16:43:00 - mmengine - INFO - Epoch(train) [99][1260/1879] lr: 2.0000e-04 eta: 0:15:24 time: 0.3336 data_time: 0.0168 memory: 6717 grad_norm: 3.3586 loss: 1.0058 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0058 2023/04/14 16:43:08 - mmengine - INFO - Epoch(train) [99][1280/1879] lr: 2.0000e-04 eta: 0:15:17 time: 0.4025 data_time: 0.0126 memory: 6717 grad_norm: 3.4180 loss: 1.0579 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0579 2023/04/14 16:43:14 - mmengine - INFO - Epoch(train) [99][1300/1879] lr: 2.0000e-04 eta: 0:15:09 time: 0.3230 data_time: 0.0155 memory: 6717 grad_norm: 3.4444 loss: 1.1996 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.1996 2023/04/14 16:43:22 - mmengine - INFO - Epoch(train) [99][1320/1879] lr: 2.0000e-04 eta: 0:15:02 time: 0.4118 data_time: 0.0125 memory: 6717 grad_norm: 3.4459 loss: 1.1057 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.1057 2023/04/14 16:43:29 - mmengine - INFO - Epoch(train) [99][1340/1879] lr: 2.0000e-04 eta: 0:14:54 time: 0.3472 data_time: 0.0162 memory: 6717 grad_norm: 3.4785 loss: 1.0447 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0447 2023/04/14 16:43:36 - mmengine - INFO - Epoch(train) [99][1360/1879] lr: 2.0000e-04 eta: 0:14:47 time: 0.3529 data_time: 0.0131 memory: 6717 grad_norm: 3.5673 loss: 1.3941 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3941 2023/04/14 16:43:43 - mmengine - INFO - Epoch(train) [99][1380/1879] lr: 2.0000e-04 eta: 0:14:40 time: 0.3222 data_time: 0.0165 memory: 6717 grad_norm: 3.4612 loss: 1.1131 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1131 2023/04/14 16:43:51 - mmengine - INFO - Epoch(train) [99][1400/1879] lr: 2.0000e-04 eta: 0:14:32 time: 0.3942 data_time: 0.0125 memory: 6717 grad_norm: 3.4522 loss: 0.9893 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 0.9893 2023/04/14 16:43:58 - mmengine - INFO - Epoch(train) [99][1420/1879] lr: 2.0000e-04 eta: 0:14:25 time: 0.3378 data_time: 0.0145 memory: 6717 grad_norm: 3.4352 loss: 1.1806 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1806 2023/04/14 16:44:06 - mmengine - INFO - Epoch(train) [99][1440/1879] lr: 2.0000e-04 eta: 0:14:17 time: 0.4097 data_time: 0.0136 memory: 6717 grad_norm: 3.4332 loss: 1.1747 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1747 2023/04/14 16:44:12 - mmengine - INFO - Epoch(train) [99][1460/1879] lr: 2.0000e-04 eta: 0:14:10 time: 0.3183 data_time: 0.0154 memory: 6717 grad_norm: 3.5442 loss: 1.2536 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2536 2023/04/14 16:44:20 - mmengine - INFO - Epoch(train) [99][1480/1879] lr: 2.0000e-04 eta: 0:14:03 time: 0.3895 data_time: 0.0140 memory: 6717 grad_norm: 3.4441 loss: 1.1326 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1326 2023/04/14 16:44:27 - mmengine - INFO - Epoch(train) [99][1500/1879] lr: 2.0000e-04 eta: 0:13:55 time: 0.3609 data_time: 0.0153 memory: 6717 grad_norm: 3.4890 loss: 1.0554 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0554 2023/04/14 16:44:35 - mmengine - INFO - Epoch(train) [99][1520/1879] lr: 2.0000e-04 eta: 0:13:48 time: 0.3841 data_time: 0.0145 memory: 6717 grad_norm: 3.4337 loss: 1.3162 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.3162 2023/04/14 16:44:42 - mmengine - INFO - Epoch(train) [99][1540/1879] lr: 2.0000e-04 eta: 0:13:40 time: 0.3824 data_time: 0.0150 memory: 6717 grad_norm: 3.5865 loss: 1.1327 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1327 2023/04/14 16:44:50 - mmengine - INFO - Epoch(train) [99][1560/1879] lr: 2.0000e-04 eta: 0:13:33 time: 0.3686 data_time: 0.0130 memory: 6717 grad_norm: 3.5423 loss: 1.0387 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0387 2023/04/14 16:44:58 - mmengine - INFO - Epoch(train) [99][1580/1879] lr: 2.0000e-04 eta: 0:13:26 time: 0.3854 data_time: 0.0144 memory: 6717 grad_norm: 3.5742 loss: 1.1123 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1123 2023/04/14 16:45:05 - mmengine - INFO - Epoch(train) [99][1600/1879] lr: 2.0000e-04 eta: 0:13:18 time: 0.3599 data_time: 0.0133 memory: 6717 grad_norm: 3.5597 loss: 1.2452 top1_acc: 0.5625 top5_acc: 0.8125 loss_cls: 1.2452 2023/04/14 16:45:12 - mmengine - INFO - Epoch(train) [99][1620/1879] lr: 2.0000e-04 eta: 0:13:11 time: 0.3740 data_time: 0.0142 memory: 6717 grad_norm: 3.4093 loss: 1.0896 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0896 2023/04/14 16:45:20 - mmengine - INFO - Epoch(train) [99][1640/1879] lr: 2.0000e-04 eta: 0:13:03 time: 0.3664 data_time: 0.0154 memory: 6717 grad_norm: 3.5465 loss: 1.2170 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2170 2023/04/14 16:45:27 - mmengine - INFO - Epoch(train) [99][1660/1879] lr: 2.0000e-04 eta: 0:12:56 time: 0.3741 data_time: 0.0146 memory: 6717 grad_norm: 3.4652 loss: 1.1661 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1661 2023/04/14 16:45:35 - mmengine - INFO - Epoch(train) [99][1680/1879] lr: 2.0000e-04 eta: 0:12:49 time: 0.4046 data_time: 0.0133 memory: 6717 grad_norm: 3.4956 loss: 1.1344 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1344 2023/04/14 16:45:42 - mmengine - INFO - Epoch(train) [99][1700/1879] lr: 2.0000e-04 eta: 0:12:41 time: 0.3385 data_time: 0.0149 memory: 6717 grad_norm: 3.4295 loss: 1.0277 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0277 2023/04/14 16:45:50 - mmengine - INFO - Epoch(train) [99][1720/1879] lr: 2.0000e-04 eta: 0:12:34 time: 0.4021 data_time: 0.0129 memory: 6717 grad_norm: 3.4777 loss: 1.1024 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1024 2023/04/14 16:45:58 - mmengine - INFO - Epoch(train) [99][1740/1879] lr: 2.0000e-04 eta: 0:12:26 time: 0.3890 data_time: 0.0137 memory: 6717 grad_norm: 3.4602 loss: 1.1388 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1388 2023/04/14 16:46:04 - mmengine - INFO - Epoch(train) [99][1760/1879] lr: 2.0000e-04 eta: 0:12:19 time: 0.3330 data_time: 0.0133 memory: 6717 grad_norm: 3.5900 loss: 1.2759 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2759 2023/04/14 16:46:13 - mmengine - INFO - Epoch(train) [99][1780/1879] lr: 2.0000e-04 eta: 0:12:12 time: 0.4068 data_time: 0.0150 memory: 6717 grad_norm: 3.3584 loss: 0.8710 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.8710 2023/04/14 16:46:19 - mmengine - INFO - Epoch(train) [99][1800/1879] lr: 2.0000e-04 eta: 0:12:04 time: 0.3132 data_time: 0.0125 memory: 6717 grad_norm: 3.4331 loss: 1.2064 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2064 2023/04/14 16:46:27 - mmengine - INFO - Epoch(train) [99][1820/1879] lr: 2.0000e-04 eta: 0:11:57 time: 0.4176 data_time: 0.0155 memory: 6717 grad_norm: 3.5336 loss: 1.0916 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0916 2023/04/14 16:46:33 - mmengine - INFO - Epoch(train) [99][1840/1879] lr: 2.0000e-04 eta: 0:11:49 time: 0.3129 data_time: 0.0126 memory: 6717 grad_norm: 3.4508 loss: 1.1966 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1966 2023/04/14 16:46:42 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 16:46:42 - mmengine - INFO - Epoch(train) [99][1860/1879] lr: 2.0000e-04 eta: 0:11:42 time: 0.4358 data_time: 0.0151 memory: 6717 grad_norm: 3.4380 loss: 1.1838 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1838 2023/04/14 16:46:48 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 16:46:48 - mmengine - INFO - Epoch(train) [99][1879/1879] lr: 2.0000e-04 eta: 0:11:35 time: 0.2800 data_time: 0.0109 memory: 6717 grad_norm: 3.5210 loss: 1.2705 top1_acc: 0.4286 top5_acc: 0.4286 loss_cls: 1.2705 2023/04/14 16:46:48 - mmengine - INFO - Saving checkpoint at 99 epochs 2023/04/14 16:46:57 - mmengine - INFO - Epoch(val) [99][ 20/155] eta: 0:01:02 time: 0.4611 data_time: 0.4284 memory: 1391 2023/04/14 16:47:04 - mmengine - INFO - Epoch(val) [99][ 40/155] eta: 0:00:44 time: 0.3167 data_time: 0.2838 memory: 1391 2023/04/14 16:47:12 - mmengine - INFO - Epoch(val) [99][ 60/155] eta: 0:00:38 time: 0.4311 data_time: 0.3980 memory: 1391 2023/04/14 16:47:19 - mmengine - INFO - Epoch(val) [99][ 80/155] eta: 0:00:28 time: 0.3139 data_time: 0.2805 memory: 1391 2023/04/14 16:47:28 - mmengine - INFO - Epoch(val) [99][100/155] eta: 0:00:21 time: 0.4531 data_time: 0.4200 memory: 1391 2023/04/14 16:47:34 - mmengine - INFO - Epoch(val) [99][120/155] eta: 0:00:13 time: 0.2992 data_time: 0.2655 memory: 1391 2023/04/14 16:47:43 - mmengine - INFO - Epoch(val) [99][140/155] eta: 0:00:05 time: 0.4424 data_time: 0.4088 memory: 1391 2023/04/14 16:47:49 - mmengine - INFO - Epoch(val) [99][155/155] acc/top1: 0.6694 acc/top5: 0.8734 acc/mean1: 0.6694 data_time: 0.3796 time: 0.4125 2023/04/14 16:47:59 - mmengine - INFO - Epoch(train) [100][ 20/1879] lr: 2.0000e-04 eta: 0:11:27 time: 0.4802 data_time: 0.2496 memory: 6717 grad_norm: 3.4844 loss: 0.9409 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 0.9409 2023/04/14 16:48:06 - mmengine - INFO - Epoch(train) [100][ 40/1879] lr: 2.0000e-04 eta: 0:11:20 time: 0.3309 data_time: 0.0385 memory: 6717 grad_norm: 3.4528 loss: 1.0409 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0409 2023/04/14 16:48:14 - mmengine - INFO - Epoch(train) [100][ 60/1879] lr: 2.0000e-04 eta: 0:11:13 time: 0.4327 data_time: 0.0152 memory: 6717 grad_norm: 3.5413 loss: 1.2195 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2195 2023/04/14 16:48:21 - mmengine - INFO - Epoch(train) [100][ 80/1879] lr: 2.0000e-04 eta: 0:11:05 time: 0.3443 data_time: 0.0133 memory: 6717 grad_norm: 3.4129 loss: 1.1727 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1727 2023/04/14 16:48:29 - mmengine - INFO - Epoch(train) [100][ 100/1879] lr: 2.0000e-04 eta: 0:10:58 time: 0.3892 data_time: 0.0145 memory: 6717 grad_norm: 3.4814 loss: 1.1560 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.1560 2023/04/14 16:48:35 - mmengine - INFO - Epoch(train) [100][ 120/1879] lr: 2.0000e-04 eta: 0:10:50 time: 0.3053 data_time: 0.0200 memory: 6717 grad_norm: 3.5065 loss: 1.2533 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2533 2023/04/14 16:48:43 - mmengine - INFO - Epoch(train) [100][ 140/1879] lr: 2.0000e-04 eta: 0:10:43 time: 0.4020 data_time: 0.0155 memory: 6717 grad_norm: 3.5576 loss: 1.1750 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.1750 2023/04/14 16:48:50 - mmengine - INFO - Epoch(train) [100][ 160/1879] lr: 2.0000e-04 eta: 0:10:36 time: 0.3167 data_time: 0.0131 memory: 6717 grad_norm: 3.4771 loss: 1.0785 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0785 2023/04/14 16:48:57 - mmengine - INFO - Epoch(train) [100][ 180/1879] lr: 2.0000e-04 eta: 0:10:28 time: 0.3836 data_time: 0.0154 memory: 6717 grad_norm: 3.4748 loss: 1.0712 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.0712 2023/04/14 16:49:04 - mmengine - INFO - Epoch(train) [100][ 200/1879] lr: 2.0000e-04 eta: 0:10:21 time: 0.3327 data_time: 0.0138 memory: 6717 grad_norm: 3.4531 loss: 1.2731 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2731 2023/04/14 16:49:13 - mmengine - INFO - Epoch(train) [100][ 220/1879] lr: 2.0000e-04 eta: 0:10:13 time: 0.4511 data_time: 0.0165 memory: 6717 grad_norm: 3.3284 loss: 1.0934 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.0934 2023/04/14 16:49:19 - mmengine - INFO - Epoch(train) [100][ 240/1879] lr: 2.0000e-04 eta: 0:10:06 time: 0.3260 data_time: 0.0124 memory: 6717 grad_norm: 3.5640 loss: 1.2086 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.2086 2023/04/14 16:49:28 - mmengine - INFO - Epoch(train) [100][ 260/1879] lr: 2.0000e-04 eta: 0:09:59 time: 0.4113 data_time: 0.0143 memory: 6717 grad_norm: 3.5009 loss: 1.1345 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1345 2023/04/14 16:49:34 - mmengine - INFO - Epoch(train) [100][ 280/1879] lr: 2.0000e-04 eta: 0:09:51 time: 0.3107 data_time: 0.0157 memory: 6717 grad_norm: 3.5208 loss: 1.2448 top1_acc: 0.5000 top5_acc: 0.6875 loss_cls: 1.2448 2023/04/14 16:49:42 - mmengine - INFO - Epoch(train) [100][ 300/1879] lr: 2.0000e-04 eta: 0:09:44 time: 0.4098 data_time: 0.0142 memory: 6717 grad_norm: 3.4712 loss: 1.0653 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.0653 2023/04/14 16:49:49 - mmengine - INFO - Epoch(train) [100][ 320/1879] lr: 2.0000e-04 eta: 0:09:36 time: 0.3383 data_time: 0.0277 memory: 6717 grad_norm: 3.4324 loss: 1.0426 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0426 2023/04/14 16:49:57 - mmengine - INFO - Epoch(train) [100][ 340/1879] lr: 2.0000e-04 eta: 0:09:29 time: 0.3830 data_time: 0.0436 memory: 6717 grad_norm: 3.5023 loss: 1.2092 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2092 2023/04/14 16:50:04 - mmengine - INFO - Epoch(train) [100][ 360/1879] lr: 2.0000e-04 eta: 0:09:22 time: 0.3655 data_time: 0.0419 memory: 6717 grad_norm: 3.4514 loss: 1.0732 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0732 2023/04/14 16:50:12 - mmengine - INFO - Epoch(train) [100][ 380/1879] lr: 2.0000e-04 eta: 0:09:14 time: 0.3844 data_time: 0.0111 memory: 6717 grad_norm: 3.4127 loss: 0.9632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9632 2023/04/14 16:50:18 - mmengine - INFO - Epoch(train) [100][ 400/1879] lr: 2.0000e-04 eta: 0:09:07 time: 0.3414 data_time: 0.0144 memory: 6717 grad_norm: 3.4298 loss: 1.2059 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.2059 2023/04/14 16:50:27 - mmengine - INFO - Epoch(train) [100][ 420/1879] lr: 2.0000e-04 eta: 0:08:59 time: 0.4216 data_time: 0.0155 memory: 6717 grad_norm: 3.4260 loss: 1.1307 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1307 2023/04/14 16:50:33 - mmengine - INFO - Epoch(train) [100][ 440/1879] lr: 2.0000e-04 eta: 0:08:52 time: 0.3217 data_time: 0.0143 memory: 6717 grad_norm: 3.3934 loss: 1.0619 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0619 2023/04/14 16:50:41 - mmengine - INFO - Epoch(train) [100][ 460/1879] lr: 2.0000e-04 eta: 0:08:45 time: 0.3873 data_time: 0.0133 memory: 6717 grad_norm: 3.4432 loss: 1.0334 top1_acc: 0.8750 top5_acc: 0.9375 loss_cls: 1.0334 2023/04/14 16:50:47 - mmengine - INFO - Epoch(train) [100][ 480/1879] lr: 2.0000e-04 eta: 0:08:37 time: 0.3080 data_time: 0.0155 memory: 6717 grad_norm: 3.3822 loss: 1.0399 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0399 2023/04/14 16:50:56 - mmengine - INFO - Epoch(train) [100][ 500/1879] lr: 2.0000e-04 eta: 0:08:30 time: 0.4588 data_time: 0.0142 memory: 6717 grad_norm: 3.5657 loss: 1.1221 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1221 2023/04/14 16:51:02 - mmengine - INFO - Epoch(train) [100][ 520/1879] lr: 2.0000e-04 eta: 0:08:22 time: 0.3034 data_time: 0.0154 memory: 6717 grad_norm: 3.4834 loss: 1.0806 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0806 2023/04/14 16:51:11 - mmengine - INFO - Epoch(train) [100][ 540/1879] lr: 2.0000e-04 eta: 0:08:15 time: 0.4080 data_time: 0.0143 memory: 6717 grad_norm: 3.3935 loss: 1.2111 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2111 2023/04/14 16:51:17 - mmengine - INFO - Epoch(train) [100][ 560/1879] lr: 2.0000e-04 eta: 0:08:08 time: 0.3221 data_time: 0.0144 memory: 6717 grad_norm: 3.4762 loss: 1.1496 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.1496 2023/04/14 16:51:25 - mmengine - INFO - Epoch(train) [100][ 580/1879] lr: 2.0000e-04 eta: 0:08:00 time: 0.4096 data_time: 0.0143 memory: 6717 grad_norm: 3.3707 loss: 0.9900 top1_acc: 0.5625 top5_acc: 0.9375 loss_cls: 0.9900 2023/04/14 16:51:32 - mmengine - INFO - Epoch(train) [100][ 600/1879] lr: 2.0000e-04 eta: 0:07:53 time: 0.3171 data_time: 0.0149 memory: 6717 grad_norm: 3.4939 loss: 1.2158 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.2158 2023/04/14 16:51:40 - mmengine - INFO - Epoch(train) [100][ 620/1879] lr: 2.0000e-04 eta: 0:07:45 time: 0.4200 data_time: 0.0160 memory: 6717 grad_norm: 3.3903 loss: 1.0168 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0168 2023/04/14 16:51:47 - mmengine - INFO - Epoch(train) [100][ 640/1879] lr: 2.0000e-04 eta: 0:07:38 time: 0.3452 data_time: 0.0140 memory: 6717 grad_norm: 3.5055 loss: 1.2390 top1_acc: 0.6250 top5_acc: 0.8125 loss_cls: 1.2390 2023/04/14 16:51:56 - mmengine - INFO - Epoch(train) [100][ 660/1879] lr: 2.0000e-04 eta: 0:07:31 time: 0.4402 data_time: 0.0172 memory: 6717 grad_norm: 3.5051 loss: 1.1403 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1403 2023/04/14 16:52:02 - mmengine - INFO - Epoch(train) [100][ 680/1879] lr: 2.0000e-04 eta: 0:07:23 time: 0.3338 data_time: 0.0140 memory: 6717 grad_norm: 3.3877 loss: 1.0331 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0331 2023/04/14 16:52:10 - mmengine - INFO - Epoch(train) [100][ 700/1879] lr: 2.0000e-04 eta: 0:07:16 time: 0.3878 data_time: 0.0137 memory: 6717 grad_norm: 3.5099 loss: 1.1363 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1363 2023/04/14 16:52:17 - mmengine - INFO - Epoch(train) [100][ 720/1879] lr: 2.0000e-04 eta: 0:07:08 time: 0.3392 data_time: 0.0145 memory: 6717 grad_norm: 3.4341 loss: 1.2378 top1_acc: 0.8125 top5_acc: 0.9375 loss_cls: 1.2378 2023/04/14 16:52:25 - mmengine - INFO - Epoch(train) [100][ 740/1879] lr: 2.0000e-04 eta: 0:07:01 time: 0.3948 data_time: 0.0139 memory: 6717 grad_norm: 3.5037 loss: 1.0345 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0345 2023/04/14 16:52:32 - mmengine - INFO - Epoch(train) [100][ 760/1879] lr: 2.0000e-04 eta: 0:06:54 time: 0.3631 data_time: 0.0143 memory: 6717 grad_norm: 3.4916 loss: 0.9769 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9769 2023/04/14 16:52:40 - mmengine - INFO - Epoch(train) [100][ 780/1879] lr: 2.0000e-04 eta: 0:06:46 time: 0.3744 data_time: 0.0136 memory: 6717 grad_norm: 3.4087 loss: 0.9865 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9865 2023/04/14 16:52:47 - mmengine - INFO - Epoch(train) [100][ 800/1879] lr: 2.0000e-04 eta: 0:06:39 time: 0.3551 data_time: 0.0147 memory: 6717 grad_norm: 3.6312 loss: 1.2794 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.2794 2023/04/14 16:52:55 - mmengine - INFO - Epoch(train) [100][ 820/1879] lr: 2.0000e-04 eta: 0:06:31 time: 0.4086 data_time: 0.0131 memory: 6717 grad_norm: 3.4196 loss: 1.0192 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0192 2023/04/14 16:53:01 - mmengine - INFO - Epoch(train) [100][ 840/1879] lr: 2.0000e-04 eta: 0:06:24 time: 0.3225 data_time: 0.0150 memory: 6717 grad_norm: 3.5087 loss: 1.3004 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.3004 2023/04/14 16:53:10 - mmengine - INFO - Epoch(train) [100][ 860/1879] lr: 2.0000e-04 eta: 0:06:17 time: 0.4115 data_time: 0.0146 memory: 6717 grad_norm: 3.4669 loss: 1.2403 top1_acc: 0.8125 top5_acc: 0.8125 loss_cls: 1.2403 2023/04/14 16:53:16 - mmengine - INFO - Epoch(train) [100][ 880/1879] lr: 2.0000e-04 eta: 0:06:09 time: 0.3187 data_time: 0.0140 memory: 6717 grad_norm: 3.4772 loss: 1.0766 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0766 2023/04/14 16:53:23 - mmengine - INFO - Epoch(train) [100][ 900/1879] lr: 2.0000e-04 eta: 0:06:02 time: 0.3511 data_time: 0.0147 memory: 6717 grad_norm: 3.5715 loss: 1.0037 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0037 2023/04/14 16:53:31 - mmengine - INFO - Epoch(train) [100][ 920/1879] lr: 2.0000e-04 eta: 0:05:54 time: 0.3901 data_time: 0.0146 memory: 6717 grad_norm: 3.3792 loss: 1.0665 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0665 2023/04/14 16:53:38 - mmengine - INFO - Epoch(train) [100][ 940/1879] lr: 2.0000e-04 eta: 0:05:47 time: 0.3797 data_time: 0.0128 memory: 6717 grad_norm: 3.4244 loss: 1.0065 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.0065 2023/04/14 16:53:45 - mmengine - INFO - Epoch(train) [100][ 960/1879] lr: 2.0000e-04 eta: 0:05:40 time: 0.3474 data_time: 0.0149 memory: 6717 grad_norm: 3.5078 loss: 1.1207 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1207 2023/04/14 16:53:53 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 16:53:53 - mmengine - INFO - Epoch(train) [100][ 980/1879] lr: 2.0000e-04 eta: 0:05:32 time: 0.3997 data_time: 0.0137 memory: 6717 grad_norm: 3.5609 loss: 1.1912 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1912 2023/04/14 16:54:00 - mmengine - INFO - Epoch(train) [100][1000/1879] lr: 2.0000e-04 eta: 0:05:25 time: 0.3108 data_time: 0.0167 memory: 6717 grad_norm: 3.4654 loss: 1.1276 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1276 2023/04/14 16:54:07 - mmengine - INFO - Epoch(train) [100][1020/1879] lr: 2.0000e-04 eta: 0:05:17 time: 0.3882 data_time: 0.0141 memory: 6717 grad_norm: 3.4049 loss: 1.1208 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.1208 2023/04/14 16:54:14 - mmengine - INFO - Epoch(train) [100][1040/1879] lr: 2.0000e-04 eta: 0:05:10 time: 0.3459 data_time: 0.0654 memory: 6717 grad_norm: 3.5137 loss: 1.0326 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 1.0326 2023/04/14 16:54:22 - mmengine - INFO - Epoch(train) [100][1060/1879] lr: 2.0000e-04 eta: 0:05:03 time: 0.3924 data_time: 0.0407 memory: 6717 grad_norm: 3.5220 loss: 1.0476 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0476 2023/04/14 16:54:29 - mmengine - INFO - Epoch(train) [100][1080/1879] lr: 2.0000e-04 eta: 0:04:55 time: 0.3502 data_time: 0.0241 memory: 6717 grad_norm: 3.4915 loss: 1.1937 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.1937 2023/04/14 16:54:37 - mmengine - INFO - Epoch(train) [100][1100/1879] lr: 2.0000e-04 eta: 0:04:48 time: 0.3830 data_time: 0.0133 memory: 6717 grad_norm: 3.4180 loss: 0.9369 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 0.9369 2023/04/14 16:54:45 - mmengine - INFO - Epoch(train) [100][1120/1879] lr: 2.0000e-04 eta: 0:04:40 time: 0.3870 data_time: 0.0146 memory: 6717 grad_norm: 3.4664 loss: 1.0890 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0890 2023/04/14 16:54:51 - mmengine - INFO - Epoch(train) [100][1140/1879] lr: 2.0000e-04 eta: 0:04:33 time: 0.3373 data_time: 0.0218 memory: 6717 grad_norm: 3.4281 loss: 1.1395 top1_acc: 0.6250 top5_acc: 0.9375 loss_cls: 1.1395 2023/04/14 16:55:00 - mmengine - INFO - Epoch(train) [100][1160/1879] lr: 2.0000e-04 eta: 0:04:26 time: 0.4084 data_time: 0.0138 memory: 6717 grad_norm: 3.4806 loss: 1.2084 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2084 2023/04/14 16:55:06 - mmengine - INFO - Epoch(train) [100][1180/1879] lr: 2.0000e-04 eta: 0:04:18 time: 0.3378 data_time: 0.0136 memory: 6717 grad_norm: 3.5616 loss: 0.9865 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9865 2023/04/14 16:55:15 - mmengine - INFO - Epoch(train) [100][1200/1879] lr: 2.0000e-04 eta: 0:04:11 time: 0.4080 data_time: 0.0144 memory: 6717 grad_norm: 3.4216 loss: 1.1021 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.1021 2023/04/14 16:55:21 - mmengine - INFO - Epoch(train) [100][1220/1879] lr: 2.0000e-04 eta: 0:04:03 time: 0.3255 data_time: 0.0127 memory: 6717 grad_norm: 3.4688 loss: 1.0810 top1_acc: 0.5625 top5_acc: 0.8750 loss_cls: 1.0810 2023/04/14 16:55:29 - mmengine - INFO - Epoch(train) [100][1240/1879] lr: 2.0000e-04 eta: 0:03:56 time: 0.3834 data_time: 0.0159 memory: 6717 grad_norm: 3.5641 loss: 1.1936 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.1936 2023/04/14 16:55:35 - mmengine - INFO - Epoch(train) [100][1260/1879] lr: 2.0000e-04 eta: 0:03:49 time: 0.3122 data_time: 0.0134 memory: 6717 grad_norm: 3.4194 loss: 0.9655 top1_acc: 0.6875 top5_acc: 1.0000 loss_cls: 0.9655 2023/04/14 16:55:43 - mmengine - INFO - Epoch(train) [100][1280/1879] lr: 2.0000e-04 eta: 0:03:41 time: 0.3963 data_time: 0.0145 memory: 6717 grad_norm: 3.4763 loss: 1.0307 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0307 2023/04/14 16:55:50 - mmengine - INFO - Epoch(train) [100][1300/1879] lr: 2.0000e-04 eta: 0:03:34 time: 0.3335 data_time: 0.0207 memory: 6717 grad_norm: 3.5145 loss: 1.1350 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1350 2023/04/14 16:55:57 - mmengine - INFO - Epoch(train) [100][1320/1879] lr: 2.0000e-04 eta: 0:03:26 time: 0.3813 data_time: 0.0159 memory: 6717 grad_norm: 3.4718 loss: 1.2718 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.2718 2023/04/14 16:56:04 - mmengine - INFO - Epoch(train) [100][1340/1879] lr: 2.0000e-04 eta: 0:03:19 time: 0.3604 data_time: 0.0146 memory: 6717 grad_norm: 3.5058 loss: 1.0966 top1_acc: 0.5000 top5_acc: 0.9375 loss_cls: 1.0966 2023/04/14 16:56:13 - mmengine - INFO - Epoch(train) [100][1360/1879] lr: 2.0000e-04 eta: 0:03:12 time: 0.4476 data_time: 0.0132 memory: 6717 grad_norm: 3.4675 loss: 1.2117 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.2117 2023/04/14 16:56:20 - mmengine - INFO - Epoch(train) [100][1380/1879] lr: 2.0000e-04 eta: 0:03:04 time: 0.3365 data_time: 0.0152 memory: 6717 grad_norm: 3.4704 loss: 1.3257 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3257 2023/04/14 16:56:28 - mmengine - INFO - Epoch(train) [100][1400/1879] lr: 2.0000e-04 eta: 0:02:57 time: 0.3695 data_time: 0.0139 memory: 6717 grad_norm: 3.5033 loss: 1.2778 top1_acc: 0.7500 top5_acc: 0.8125 loss_cls: 1.2778 2023/04/14 16:56:35 - mmengine - INFO - Epoch(train) [100][1420/1879] lr: 2.0000e-04 eta: 0:02:49 time: 0.3836 data_time: 0.0147 memory: 6717 grad_norm: 3.4925 loss: 1.1281 top1_acc: 0.4375 top5_acc: 0.9375 loss_cls: 1.1281 2023/04/14 16:56:42 - mmengine - INFO - Epoch(train) [100][1440/1879] lr: 2.0000e-04 eta: 0:02:42 time: 0.3263 data_time: 0.0147 memory: 6717 grad_norm: 3.5278 loss: 1.2882 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2882 2023/04/14 16:56:50 - mmengine - INFO - Epoch(train) [100][1460/1879] lr: 2.0000e-04 eta: 0:02:35 time: 0.3975 data_time: 0.0130 memory: 6717 grad_norm: 3.5737 loss: 1.1107 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1107 2023/04/14 16:56:57 - mmengine - INFO - Epoch(train) [100][1480/1879] lr: 2.0000e-04 eta: 0:02:27 time: 0.3585 data_time: 0.0139 memory: 6717 grad_norm: 3.5151 loss: 1.0553 top1_acc: 0.5000 top5_acc: 0.8125 loss_cls: 1.0553 2023/04/14 16:57:05 - mmengine - INFO - Epoch(train) [100][1500/1879] lr: 2.0000e-04 eta: 0:02:20 time: 0.3937 data_time: 0.0130 memory: 6717 grad_norm: 3.4095 loss: 1.0067 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0067 2023/04/14 16:57:12 - mmengine - INFO - Epoch(train) [100][1520/1879] lr: 2.0000e-04 eta: 0:02:12 time: 0.3566 data_time: 0.0145 memory: 6717 grad_norm: 3.4986 loss: 1.2434 top1_acc: 0.6875 top5_acc: 0.7500 loss_cls: 1.2434 2023/04/14 16:57:19 - mmengine - INFO - Epoch(train) [100][1540/1879] lr: 2.0000e-04 eta: 0:02:05 time: 0.3633 data_time: 0.0139 memory: 6717 grad_norm: 3.4556 loss: 1.0592 top1_acc: 0.8125 top5_acc: 1.0000 loss_cls: 1.0592 2023/04/14 16:57:26 - mmengine - INFO - Epoch(train) [100][1560/1879] lr: 2.0000e-04 eta: 0:01:58 time: 0.3338 data_time: 0.0148 memory: 6717 grad_norm: 3.5492 loss: 1.0405 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.0405 2023/04/14 16:57:34 - mmengine - INFO - Epoch(train) [100][1580/1879] lr: 2.0000e-04 eta: 0:01:50 time: 0.3965 data_time: 0.0136 memory: 6717 grad_norm: 3.5054 loss: 1.1520 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1520 2023/04/14 16:57:41 - mmengine - INFO - Epoch(train) [100][1600/1879] lr: 2.0000e-04 eta: 0:01:43 time: 0.3551 data_time: 0.0143 memory: 6717 grad_norm: 3.4454 loss: 1.2755 top1_acc: 0.6250 top5_acc: 0.6875 loss_cls: 1.2755 2023/04/14 16:57:48 - mmengine - INFO - Epoch(train) [100][1620/1879] lr: 2.0000e-04 eta: 0:01:35 time: 0.3649 data_time: 0.0134 memory: 6717 grad_norm: 3.4501 loss: 1.1782 top1_acc: 0.5625 top5_acc: 0.7500 loss_cls: 1.1782 2023/04/14 16:57:56 - mmengine - INFO - Epoch(train) [100][1640/1879] lr: 2.0000e-04 eta: 0:01:28 time: 0.3778 data_time: 0.0152 memory: 6717 grad_norm: 3.4545 loss: 1.0331 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 1.0331 2023/04/14 16:58:03 - mmengine - INFO - Epoch(train) [100][1660/1879] lr: 2.0000e-04 eta: 0:01:21 time: 0.3396 data_time: 0.0144 memory: 6717 grad_norm: 3.4895 loss: 1.0411 top1_acc: 0.4375 top5_acc: 0.7500 loss_cls: 1.0411 2023/04/14 16:58:10 - mmengine - INFO - Epoch(train) [100][1680/1879] lr: 2.0000e-04 eta: 0:01:13 time: 0.3676 data_time: 0.0135 memory: 6717 grad_norm: 3.4184 loss: 1.0423 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0423 2023/04/14 16:58:17 - mmengine - INFO - Epoch(train) [100][1700/1879] lr: 2.0000e-04 eta: 0:01:06 time: 0.3530 data_time: 0.0160 memory: 6717 grad_norm: 3.4452 loss: 1.0967 top1_acc: 0.7500 top5_acc: 0.9375 loss_cls: 1.0967 2023/04/14 16:58:25 - mmengine - INFO - Epoch(train) [100][1720/1879] lr: 2.0000e-04 eta: 0:00:58 time: 0.4006 data_time: 0.0127 memory: 6717 grad_norm: 3.4220 loss: 0.9375 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9375 2023/04/14 16:58:32 - mmengine - INFO - Epoch(train) [100][1740/1879] lr: 2.0000e-04 eta: 0:00:51 time: 0.3413 data_time: 0.0157 memory: 6717 grad_norm: 3.3841 loss: 0.9668 top1_acc: 0.9375 top5_acc: 1.0000 loss_cls: 0.9668 2023/04/14 16:58:39 - mmengine - INFO - Epoch(train) [100][1760/1879] lr: 2.0000e-04 eta: 0:00:44 time: 0.3713 data_time: 0.0140 memory: 6717 grad_norm: 3.3528 loss: 1.0761 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0761 2023/04/14 16:58:47 - mmengine - INFO - Epoch(train) [100][1780/1879] lr: 2.0000e-04 eta: 0:00:36 time: 0.3834 data_time: 0.0143 memory: 6717 grad_norm: 3.5084 loss: 1.1483 top1_acc: 0.6875 top5_acc: 0.9375 loss_cls: 1.1483 2023/04/14 16:58:54 - mmengine - INFO - Epoch(train) [100][1800/1879] lr: 2.0000e-04 eta: 0:00:29 time: 0.3517 data_time: 0.0139 memory: 6717 grad_norm: 3.6390 loss: 1.1970 top1_acc: 0.6875 top5_acc: 0.8750 loss_cls: 1.1970 2023/04/14 16:59:01 - mmengine - INFO - Epoch(train) [100][1820/1879] lr: 2.0000e-04 eta: 0:00:21 time: 0.3693 data_time: 0.0152 memory: 6717 grad_norm: 3.4837 loss: 1.1243 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1243 2023/04/14 16:59:09 - mmengine - INFO - Epoch(train) [100][1840/1879] lr: 2.0000e-04 eta: 0:00:14 time: 0.3588 data_time: 0.0151 memory: 6717 grad_norm: 3.4989 loss: 1.0054 top1_acc: 0.8125 top5_acc: 0.8750 loss_cls: 1.0054 2023/04/14 16:59:16 - mmengine - INFO - Epoch(train) [100][1860/1879] lr: 2.0000e-04 eta: 0:00:07 time: 0.3834 data_time: 0.0144 memory: 6717 grad_norm: 3.4954 loss: 0.9813 top1_acc: 0.6875 top5_acc: 0.8125 loss_cls: 0.9813 2023/04/14 16:59:23 - mmengine - INFO - Exp name: tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb_20230413_195605 2023/04/14 16:59:23 - mmengine - INFO - Epoch(train) [100][1879/1879] lr: 2.0000e-04 eta: 0:00:00 time: 0.3748 data_time: 0.0124 memory: 6717 grad_norm: 3.4902 loss: 1.1308 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.1308 2023/04/14 16:59:23 - mmengine - INFO - Saving checkpoint at 100 epochs 2023/04/14 16:59:32 - mmengine - INFO - Epoch(val) [100][ 20/155] eta: 0:01:02 time: 0.4598 data_time: 0.4263 memory: 1391 2023/04/14 16:59:39 - mmengine - INFO - Epoch(val) [100][ 40/155] eta: 0:00:44 time: 0.3192 data_time: 0.2858 memory: 1391 2023/04/14 16:59:47 - mmengine - INFO - Epoch(val) [100][ 60/155] eta: 0:00:38 time: 0.4240 data_time: 0.3907 memory: 1391 2023/04/14 16:59:54 - mmengine - INFO - Epoch(val) [100][ 80/155] eta: 0:00:28 time: 0.3147 data_time: 0.2812 memory: 1391 2023/04/14 17:00:02 - mmengine - INFO - Epoch(val) [100][100/155] eta: 0:00:21 time: 0.4273 data_time: 0.3942 memory: 1391 2023/04/14 17:00:09 - mmengine - INFO - Epoch(val) [100][120/155] eta: 0:00:13 time: 0.3380 data_time: 0.3040 memory: 1391 2023/04/14 17:00:19 - mmengine - INFO - Epoch(val) [100][140/155] eta: 0:00:05 time: 0.4827 data_time: 0.4494 memory: 1391 2023/04/14 17:00:26 - mmengine - INFO - Epoch(val) [100][155/155] acc/top1: 0.6689 acc/top5: 0.8755 acc/mean1: 0.6689 data_time: 0.4162 time: 0.4485